A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications
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paper_title: A Survey of Indoor Inertial Positioning Systems for Pedestrians

paper_content:

With the continual miniaturisation of sensors and processing nodes, Pedestrian Dead Reckoning (PDR) systems are becoming feasible options for indoor tracking. These use inertial and other sensors, often combined with domain-specific knowledge about walking, to track user movements. There is currently a wealth of relevant literature spread across different research communities. In this survey, a taxonomy of modern PDRs is developed and used to contextualise the contributions from different areas. Techniques for step detection, characterisation, inertial navigation and step-and-heading-based dead-reckoning are reviewed and compared. Techniques that incorporate building maps through particle filters are analysed, along with hybrid systems that use absolute position fixes to correct dead-reckoning output. In addition, consideration is given to the possibility of using smartphones as PDR sensing devices. The survey concludes that PDR techniques alone can offer good short- to medium- term tracking under certain circumstances, but that regular absolute position fixes from partner systems will be needed to ensure long-term operation and to cope with unexpected behaviours. It concludes by identifying a detailed list of challenges for PDR researchers.
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paper_title: Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

paper_content:

The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of global positioning system (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.
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paper_title: Indoor geolocation science and technology

paper_content:

This article presents an overview of the technical aspects of the existing technologies for wireless indoor location systems. The two major challenges for accurate location finding in indoor areas are the complexity of radio propagation and the ad hoc nature of the deployed infrastructure in these areas. Because of these difficulties a variety of signaling techniques, overall system architectures, and location finding algorithms are emerging for this application. This article provides a fundamental understanding of the issues related to indoor geolocation science that are needed for design and performance evaluation of emerging indoor geolocation systems.
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paper_title: Indoor Tracking: Theory, Methods, and Technologies

paper_content:

In the last decade, the research on and the technology for outdoor tracking have seen an explosion of advances. It is expected that in the near future, we will witness similar trends for indoor scenarios where people spend more than 70% of their lives. The rationale for this is that there is a need for reliable and high-definition real-time tracking systems that have the ability to operate in indoor environments, thus complementing those based on satellite technologies, such as the Global Positioning System (GPS). The indoor environments are very challenging, and as a result, a large variety of technologies have been proposed for coping with them, but no legacy solution has emerged. This paper presents a survey on indoor wireless tracking of mobile nodes from a signal processing perspective. It can be argued that the indoor tracking problem is more challenging than the problem on indoor localization. The reason is simple: From a set of measurements, one has to estimate not one location but a series of correlated locations of a mobile node. The paper illustrates the theory, the main tools, and the most promising technologies for indoor tracking. New directions of research are also discussed.
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paper_title: Location Systems for Ubiquitous Computing

paper_content:

This survey and taxonomy of location systems for mobile-computing applications describes a spectrum of current products and explores the latest in the field. To make sense of this domain, we have developed a taxonomy to help developers of location-aware applications better evaluate their options when choosing a location-sensing system. The taxonomy may also aid researchers in identifying opportunities for new location-sensing techniques.
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paper_title: Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances †

paper_content:

In recent years, indoor positioning has emerged as a critical function in many end-user applications; including military, civilian, disaster relief and peacekeeping missions. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task in part because various objects reflect and disperse signals. Ultra WideBand (UWB) is an emerging technology in the field of indoor positioning that has shown better performance compared to others. In order to set the stage for this work, we provide a survey of the state-of-the-art technologies in indoor positioning, followed by a detailed comparative analysis of UWB positioning technologies. We also provide an analysis of strengths, weaknesses, opportunities, and threats (SWOT) to analyze the present state of UWB positioning technologies. While SWOT is not a quantitative approach, it helps in assessing the real status and in revealing the potential of UWB positioning to effectively address the indoor positioning problem. Unlike previous studies, this paper presents new taxonomies, reviews some major recent advances, and argues for further exploration by the research community of this challenging problem space.
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paper_title: Improvement Schemes for Indoor Mobile Location Estimation: A Survey

paper_content:

Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research.
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paper_title: A survey of calibration-free indoor positioning systems

paper_content:

Last decade observed a significant research effort directed towards indoor localization utilizing location fingerprinting techniques. Fingerprinting solutions generally require a pre-deployment site survey procedure during which a radio-map is constructed by laboriously collecting signal strength samples (e.g., Wi-Fi) over the whole localization area. However, such localization efforts have certain shortcomings. For example, it is time consuming, labor intensive, vulnerable to environmental changes, and the process requires certain pedigree on the surveyor that may deem the fingerprinting techniques impractical to be deployed over large areas (e.g., shopping malls, multi-storey offices/residences, etc.). Newer emerging techniques try to bypass this expensive pre-deployment effort of fingerprinting solutions altogether. They may build the radio-map through the implicit participation of the building occupants, office employee, shoppers, visitors, etc. Apart from the traditional performance comparison criteria like accuracy, precision, robustness, scalability, algorithmic complexity based on which the localization techniques were evaluated, these newer approaches warrant some additional ones. For example, whether they require an actual geographical map of the localization area, the percentage of occasional location fix to ensure reasonable accuracy, the usage of explicit/implicit user participation to construct the radio map, the usage of building landmarks (e.g., entrance, conference room, elevator, escalator, etc.) or additional sensors (e.g., accelerometer, gyroscope, compass, etc.), whether they address device heterogeneity, etc. In this article, we survey the newer emerging fingerprinting solutions that try to relieve the pre-deployment woes. We also identify some newer performance comparison criteria based on these solutions’ inherent characteristics, and apply them together with the traditional ones in order to evaluate a number of such proposed systems.
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paper_title: Survey of Wireless Indoor Positioning Techniques and Systems

paper_content:

Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.
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paper_title: Vehicle Positioning Using 5G Millimeter-Wave Systems

paper_content:

Recent growth in traffic and the resulting congestion and accidents has increased the demand for vehicle positioning systems. Existing global navigation satellite systems were designed for line of sight environments and thus accurately determining the location of a vehicle in urban areas with tall buildings or regions with dense foliage is difficult. Fifth generation (5G) cellular networks provide device-to-device communication capabilities which can be exploited to determine the real-time location of vehicles. Millimeter-wave (mmWave) transmission is regarded as a key technology for 5G networks. This paper examines vehicle positioning using 5G mmWave signals. Both a correlation receiver and an energy detector are considered for timing estimation. Furthermore, fixed and dynamic thresholds for energy detection are examined. It is shown that a correlation receiver can provide excellent ranging accuracy but has high computational complexity, whereas an energy detector has low computational complexity and provides good ranging accuracy. Furthermore, the Gaussian raised-cosine pulse (RCP), Gaussian pulse, and Sinc-RCP impulse radio waveforms provide the best performance.
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paper_title: A New Three Object Triangulation Algorithm for Mobile Robot Positioning

paper_content:

Positioning is a fundamental issue in mobile robot applications. It can be achieved in many ways. Among them, triangulation based on angles measured with the help of beacons is a proven technique. Most of the many triangulation algorithms proposed so far have major limitations. For example, some of them need a particular beacon ordering, have blind spots, or only work within the triangle defined by the three beacons. More reliable methods exist; however, they have an increasing complexity, or they require to handle certain spatial arrangements separately. In this paper, we present a simple and new three object triangulation algorithm, known as ToTal, that natively works in the whole plane and for any beacon ordering. We also provide a comprehensive comparison between many algorithms and show that our algorithm is faster and simpler than comparable algorithms. In addition to its inherent efficiency, our algorithm provides a very useful and unique reliability measure that is assessable anywhere in the plane, which can be used to identify pathological cases, or as a validation gate in Kalman filters.
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paper_title: Fundamental Limits of Wideband Localization - Part II: Cooperative Networks

paper_content:

The availability of position information is of great importance in many commercial, governmental, and military applications. Localization is commonly accomplished through the use of radio communication between mobile devices (agents) and fixed infrastructure (anchors). However, precise determination of agent positions is a challenging task, especially in harsh environments due to radio blockage or limited anchor deployment. In these situations, cooperation among agents can significantly improve localization accuracy and reduce localization outage probabilities. A general framework of analyzing the fundamental limits of wideband localization has been developed in Part I of the paper. Here, we build on this framework and establish the fundamental limits of wideband cooperative location-aware networks. Our analysis is based on the waveforms received at the nodes, in conjunction with Fisher information inequality. We provide a geometrical interpretation of equivalent Fisher information (EFI) for cooperative networks. This approach allows us to succinctly derive fundamental performance limits and their scaling behaviors, and to treat anchors and agents in a unified way from the perspective of localization accuracy. Our results yield important insights into how and when cooperation is beneficial.
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paper_title: Fundamental Limits of Wideband Localization— Part I: A General Framework

paper_content:

The availability of position information is of great importance in many commercial, public safety, and military applications. The coming years will see the emergence of location-aware networks with submeter accuracy, relying on accurate range measurements provided by wide bandwidth transmissions. In this two-part paper, we determine the fundamental limits of localization accuracy of wideband wireless networks in harsh multipath environments. We first develop a general framework to characterize the localization accuracy of a given node here and then extend our analysis to cooperative location-aware networks in Part II. In this paper, we characterize localization accuracy in terms of a performance measure called the squared position error bound (SPEB), and introduce the notion of equivalent Fisher information (EFI) to derive the SPEB in a succinct expression. This methodology provides insights into the essence of the localization problem by unifying localization information from individual anchors and that from a priori knowledge of the agent's position in a canonical form. Our analysis begins with the received waveforms themselves rather than utilizing only the signal metrics extracted from these waveforms, such as time-of-arrival and received signal strength. Hence, our framework exploits all the information inherent in the received waveforms, and the resulting SPEB serves as a fundamental limit of localization accuracy.
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paper_title: Wireless Sensor Network Localization Techniques

paper_content:

Wireless sensor network localization is an important area that attracted significant research interest. This interest is expected to grow further with the proliferation of wireless sensor network applications. This paper provides an overview of the measurement techniques in sensor network localization and the one-hop localization algorithms based on these measurements. A detailed investigation on multi-hop connectivity-based and distance-based localization algorithms are presented. A list of open research problems in the area of distance-based sensor network localization is provided with discussion on possible approaches to them.
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paper_title: Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks

paper_content:

UWB technology provides an excellent means for wireless positioning due to its high resolution capability in the time domain. Its ability to resolve multipath components makes it possible to obtain accurate location estimates without the need for complex estimation algorithms. In this article, theoretical limits for TOA estimation and TOA-based location estimation for UWB systems have been considered. Due to the complexity of the optimal schemes, suboptimal but practical alternatives have been emphasized. Performance limits for hybrid TOA/SS and TDOA/SS schemes have also been considered. Although the fundamental mechanisms for localization, including AOA-, TOA-, TDOA-, and SS-based methods, apply to all radio air interface, some positioning techniques are favored by UWB-based systems using ultrawide bandwidths.
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paper_title: The anatomy of a context-aware application

paper_content:

We describe a sensor-driven, or sentient, platform for context-aware computing that enables applications to follow mobile users as they move around a building. The platform is particularly suitable for richly equipped, networked environments. The only item a user is required to carry is a small sensor tag, which identifies them to the system and locates them accurately in three dimensions. The platform builds a dynamic model of the environment using these location sensors and resource information gathered by telemetry software, and presents it in a form suitable for application programmers. Use of the platform is illustrated through a practical example, which allows a user's current working desktop to follow them as they move around the environment.
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paper_title: Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances †

paper_content:

In recent years, indoor positioning has emerged as a critical function in many end-user applications; including military, civilian, disaster relief and peacekeeping missions. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task in part because various objects reflect and disperse signals. Ultra WideBand (UWB) is an emerging technology in the field of indoor positioning that has shown better performance compared to others. In order to set the stage for this work, we provide a survey of the state-of-the-art technologies in indoor positioning, followed by a detailed comparative analysis of UWB positioning technologies. We also provide an analysis of strengths, weaknesses, opportunities, and threats (SWOT) to analyze the present state of UWB positioning technologies. While SWOT is not a quantitative approach, it helps in assessing the real status and in revealing the potential of UWB positioning to effectively address the indoor positioning problem. Unlike previous studies, this paper presents new taxonomies, reviews some major recent advances, and argues for further exploration by the research community of this challenging problem space.
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paper_title: The Cricket location-support system

paper_content:

This paper presents the design, implementation, and evaluation of Cricket , a location-support system for in-building, mobile, location-dependent applications. It allows applications running on mobile and static nodes to learn their physical location by using listeners that hear and analyze information from beacons spread throughout the building. Cricket is the result of several design goals, including user privacy, decentralized administration, network heterogeneity, and low cost. Rather than explicitly tracking user location, Cricket helps devices learn where they are and lets them decide whom to advertise this information to; it does not rely on any centralized management or control and there is no explicit coordination between beacons; it provides information to devices regardless of their type of network connectivity; and each Cricket device is made from off-the-shelf components and costs less than U.S. $10. We describe the randomized algorithm used by beacons to transmit information, the use of concurrent radio and ultrasonic signals to infer distance, the listener inference algorithms to overcome multipath and interference, and practical beacon configuration and positioning techniques that improve accuracy. Our experience with Cricket shows that several location-dependent applications such as in-building active maps and device control can be developed with little effort or manual configuration.
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paper_title: Survey of Wireless Indoor Positioning Techniques and Systems

paper_content:

Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.
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paper_title: Ad hoc positioning system (APS) using AOA

paper_content:

Position information of individual nodes is useful in implementing functions such as routing and querying in ad-hoc networks. Deriving position information by using the capability of the nodes to measure time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA) and signal strength have been used to localize nodes relative to a frame of reference. The nodes in an ad-hoc network can have multiple capabilities and exploiting one or more of the capabilities can improve the quality of positioning. In this paper, we show how AOA capability of the nodes can be used to derive position information. We propose a method for all nodes to determine their orientation and position in an ad-hoc network where only a fraction of the nodes have positioning capabilities, under the assumption that each node has the AOA capability.
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paper_title: Collaborative Mapping for Pedestrian Navigation in Security Applications

paper_content:

In rescue missions or law enforcement applications, accurate determination of every team member’s position and providing this information on a map may significantly improve mutual situation awareness and potentially reduce the risk of accidentally harming a team member. Furthermore, it could help keep track of the areas that have been already visited, helping the coordination of the mission at hand.
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paper_title: FootSLAM: Pedestrian Simultaneous Localization and Mapping Without Exteroceptive Sensors—Hitchhiking on Human Perception and Cognition

paper_content:

In this paper, we describe FootSLAM, a Bayesian estimation approach that achieves simultaneous localization and mapping for pedestrians. FootSLAM uses odometry obtained with foot-mounted inertial sensors. Whereas existing approaches to infrastructure-less pedestrian position determination are either subject to unbounded growth of positioning error, or require either a priori map information, or exteroceptive sensors, such as cameras or light detection and ranging (LIDARs), FootSLAM achieves long-term error stability solely based on inertial sensor measurements. An analysis of the problem based on a dynamic Bayesian network (DBN) model reveals that this surprising result becomes possible by effectively hitchhiking on human perception and cognition. Two extensions to FootSLAM, namely, PlaceSLAM, for incorporating additional measurements or user provided hints, and FeetSLAM, for automated collaborative mapping, are discussed. Experimental data that validate FootSLAM and its extensions are presented. It is foreseeable that the sensors and processing power of future devices such as smartphones are likely to suffice to position the bearer with the same accuracy that FootSLAM achieves with foot-mounted sensors already today.
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paper_title: PocketSLAM based on the principle of the FootSLAM algorithm

paper_content:

Infrastructureless indoor navigation remains a challenging research area in spite of the fact that multiple low cost sensors suitable for positioning recently became available even in mobile phones. Since Global Navigation Satellite Signals (GNSS) are often unavailable indoors, the use of Inertial Measurement Units (IMUs) seems to be promising for indoor navigation. Auspicious results are available when the sensor is placed on the foot in indoor positioning applications, but still many problems remain when the drift error during stance phases cannot be reduced. An indoor navigation solution is the so called FootSLAM algorithm, where reliable positioning (positioning error below 1 meter in the case of loop closures) is possible assuming that the sensor is fixed on the foot. More recently, another navigation system was proposed where the sensor is located in the pocket of a pedestrian. Since no special fixation or no special shoe is needed, this location of the sensor is more convenient for mass market users. But also this system suffers from a remaining drift that needs to be compensated. In this paper, we propose a combination of the pocket navigation system with the FootSLAM approach and show that it is also possible to reduce the remaining drift with this combination.
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paper_title: Relative Location Estimation in Wireless Sensor Networks

paper_content:

Self-configuration in wireless sensor networks is a general class of estimation problems that we study via the Cramer-Rao bound (CRB). Specifically, we consider sensor location estimation when sensors measure received signal strength (RSS) or time-of-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the network have a known location, whereas the remaining locations must be estimated. We derive CRBs and maximum-likelihood estimators (MLEs) under Gaussian and log-normal models for the TOA and RSS measurements, respectively. An extensive TOA and RSS measurement campaign in an indoor office area illustrates MLE performance. Finally, relative location estimation algorithms are implemented in a wireless sensor network testbed and deployed in indoor and outdoor environments. The measurements and testbed experiments demonstrate 1-m RMS location errors using TOA, and 1- to 2-m RMS location errors using RSS.
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paper_title: Building a Practical Wi-Fi-Based Indoor Navigation System

paper_content:

This article presents the seven-step process involved in building a practical Wi-Fi-based indoor navigation system, which was implemented at the COEX complex in Seoul, Korea, in October 2010. The article describes the primary activities in each step using the COEX example. More than 200,000 users have downloaded the system since its first release. The successful launch of the COEX indoor navigation system suggests that indoor navigation is becoming a reality.
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paper_title: A mobile indoor positioning system based on iBeacon technology

paper_content:

To increase the efficiency in the emergency room, the goal of this research is to implement a mobile-based indoor positioning system using mobile applications (APP) with the iBeacon solution based on the Bluetooth Low Energy (BLE) technology. We use the Received Signal Strength (RSS) based localization method to estimate the patients' locations. Our positioning algorithm achieves 97.22% (95% Confidence Interval = 95.90% - 98.55%) accuracy of classification. As the result, our mechanism is reliable enough to satisfy the need for medical staff to track the locations of their patients.
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paper_title: Probability kernel regression for WiFi localisation

paper_content:

Various methods have been developed for indoor localisation using WLAN signals. Algorithms that fingerprint the received signal strength indicators RSSI of WiFi for different locations can achieve tracking accuracies of the order of a few metres. RSSI fingerprinting suffers from two main limitations: first, as the signal environment changes, so does the fingerprint database, which requires regular updates; second, it has been reported that, in practice, certain devices record more complex e.g bimodal distributions of WiFi signals, precluding algorithms based on the mean RSSI. Mirowski et al . [2011. KL-divergence kernel regression for non-Gaussian fingerprint based localization. In : International conference on indoor positioning and indoor navigation , Guimaraes, Portugal] have recently introduced a simple methodology that takes into account the full distribution for computing similarities among fingerprints using the Kullback–Leibler KL divergence, and then performs localisation through kernel regression. Their algorithm provides a natural way of smoothing over time and motion trajectories and can be applied directly to histograms of WiFi connections to access points, ignoring RSSI distributions, hence removing the need for fingerprint recalibration. It has been shown to outperform nearest neighbours or Kalman and particle filtres, achieving up to 1 m accuracy in office environments. In this article, we focus on the relevance of Gaussian or non-Gaussian distributions for modelling RSSI distributions by considering additional probabilistic kernels for comparing Gaussian distributions and by evaluating them on three contrasting datasets. We discuss their limitations and formulate how the KL-divergence kernel regression algorithm bridges the gap with other WiFi localisation algorithms, notably Bayesian networks, support vector machines and K nearest neighbours. Finally, we revisit the assumptions on the fingerprint maps and overview practical WiFi localisation software implementation.
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paper_title: Collaborative Mapping for Pedestrian Navigation in Security Applications

paper_content:

In rescue missions or law enforcement applications, accurate determination of every team member’s position and providing this information on a map may significantly improve mutual situation awareness and potentially reduce the risk of accidentally harming a team member. Furthermore, it could help keep track of the areas that have been already visited, helping the coordination of the mission at hand.
---
paper_title: FootSLAM: Pedestrian Simultaneous Localization and Mapping Without Exteroceptive Sensors—Hitchhiking on Human Perception and Cognition

paper_content:

In this paper, we describe FootSLAM, a Bayesian estimation approach that achieves simultaneous localization and mapping for pedestrians. FootSLAM uses odometry obtained with foot-mounted inertial sensors. Whereas existing approaches to infrastructure-less pedestrian position determination are either subject to unbounded growth of positioning error, or require either a priori map information, or exteroceptive sensors, such as cameras or light detection and ranging (LIDARs), FootSLAM achieves long-term error stability solely based on inertial sensor measurements. An analysis of the problem based on a dynamic Bayesian network (DBN) model reveals that this surprising result becomes possible by effectively hitchhiking on human perception and cognition. Two extensions to FootSLAM, namely, PlaceSLAM, for incorporating additional measurements or user provided hints, and FeetSLAM, for automated collaborative mapping, are discussed. Experimental data that validate FootSLAM and its extensions are presented. It is foreseeable that the sensors and processing power of future devices such as smartphones are likely to suffice to position the bearer with the same accuracy that FootSLAM achieves with foot-mounted sensors already today.
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paper_title: A comparison of Pedestrian Dead-Reckoning algorithms using a low-cost MEMS IMU

paper_content:

Human localization is a very valuable information for smart environments. State-of-the-art Local Positioning Systems (LPS) require a complex sensor-network infrastructure to locate with enough accuracy and coverage. Alternatively, Inertial Measuring Units (IMU) can be used to estimate the movement of a person, by detecting steps, estimating stride lengths and the directions of motion; a methodology that is called Pedestrian Dead-Reckoning (PDR). In this paper, we use low-performance Micro-Electro-Mechanical (MEMS) inertial sensors attached to the foot of a person. This sensor has triaxial orthogonal accelerometers, gyroscopes and magnetometers. We describe, implement and compare several of the most relevant algorithms for step detection, stride length, heading and position estimation. The challenge using MEMS is to provide location estimations with enough accuracy and a limited drift. Several tests were conducted outdoors and indoors, and we found that the stride length estimation errors were about 1%. The positioning errors were almost always below 5% of the total travelled distance. The main source of positioning errors are the absolute orientation estimation.
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paper_title: Unscented Kalman filter and Magnetic Angular Rate Update (MARU) for an improved Pedestrian Dead-Reckoning

paper_content:

The Extended Kalman Filter (EKF) has been the state of the art in Pedestrian Dead-Reckoning for foot-mounted Inertial Measurements Units. However due to the non-linearity in the propagation of the orientation the EKF is not the optimal Bayesian filter. We propose the usage of the Unscented Kalman Filter (UKF) as the integration algorithm for the inertial measurements. The UKF improves the mean and covariance propagation needed for the Kalman filter. Although the UKF provides a better estimate of the orientation, with Zero velocity UPdaTes (ZUPT) measurements, the yaw and the bias in the gyroscope associated with it becomes unobserved and might generate errors in the positioning. We studied the changes in the magnetic field during the stance phase and their relationship with the turn rates to propose three measurements using the magnetometer signal that will be called Magnetic Angular Rate Updates (MARUs). The first measurement uses the change in the angle of the magnetic field in the horizontal plane to measure the change in the yaw and provides a simple measurement for the UKF implementation. The second measurement relates the change in the magnetic field vector to the turn rate and provides information on the bias of the gyroscope for an UKF. The last measurement uses a first order approximation to generate a linear relationship with the gyroscope bias and therefore it can be used in an EKF. Finally we proposed a metric for the reliability of the stance as a way to use the pre and post stance information but adjusting the covariance of the measurements gradually from swing to stance. These methods were tested on real and simulated signals and they have shown improvements over the original PDR algorithms.
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paper_title: A New Three Object Triangulation Algorithm for Mobile Robot Positioning

paper_content:

Positioning is a fundamental issue in mobile robot applications. It can be achieved in many ways. Among them, triangulation based on angles measured with the help of beacons is a proven technique. Most of the many triangulation algorithms proposed so far have major limitations. For example, some of them need a particular beacon ordering, have blind spots, or only work within the triangle defined by the three beacons. More reliable methods exist; however, they have an increasing complexity, or they require to handle certain spatial arrangements separately. In this paper, we present a simple and new three object triangulation algorithm, known as ToTal, that natively works in the whole plane and for any beacon ordering. We also provide a comprehensive comparison between many algorithms and show that our algorithm is faster and simpler than comparable algorithms. In addition to its inherent efficiency, our algorithm provides a very useful and unique reliability measure that is assessable anywhere in the plane, which can be used to identify pathological cases, or as a validation gate in Kalman filters.
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paper_title: A stigmergic approach to indoor localization using Bluetooth Low Energy beacons

paper_content:

Localization of people and devices is one of the main building blocks of context aware systems since the user position represents the core information for detecting user's activities, devices activations, proximity to points of interest, etc. While for outdoor scenarios Global Positioning System (GPS) constitutes a reliable and easily available technology, for indoor scenarios GPS is largely unavailable. In this paper we present a range-based indoor localization system that exploits the Received Signal Strength (RSS) of Bluetooth Low Energy (BLE) beacon packets broadcast by anchor nodes and received by a BLE-enabled device. The method used to infer the user's position is based on stigmergy. We exploit the stigmergic marking process to create an on-line probability map identifying the user's position in the indoor environment.
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paper_title: Indoor Localization Using Improved RSS-Based Lateration Methods

paper_content:

Location estimation is a critical step for many location-aware applications. To obtain location information, localization methods employing Received Signal Strength (RSS) are attestative since it can reuse the existing wireless infrastructure for localization. Among the large class of localization schemes, RSS-based lateration methods have the advantage of providing closed-form solutions for mathematical analysis as compared to heuristic-based localization approaches. However, the localization accuracy of RSS-based lateration methods are significantly affected by the unpredictable setup in indoor environments. To improve the applicability of RSS-based lateration methods in indoors, we propose two approaches, regression-based and correlation-based. The regression-based approach uses linear regression to discover a better fit of signal propagation model between RSS and the distance, while the correlation-based approach utilizes the correlation among RSS in local area to obtain more accurate signal propagation. Our results using both simulation as well as real experiments demonstrate that our improved methods outperform the original RSS-based lateration methods significantly.
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paper_title: Collaborative Localization With Received-Signal Strength in Wireless Sensor Networks

paper_content:

In this paper, we present an in-depth study of two collaborative-localization methods, called the multidimensional scaling (MDS) and maximum-likelihood estimator (MLE), for wireless sensor networks. From theoretical analysis, it is shown that MLE is more appropriate than MDS, given the underlying assumption of statistical signal models of the received-signal-strength-based localization problem. We also show that MDS can approximately achieve asymptotic efficiency with appropriate weighting schemes in some scenarios. From extensive simulation results, it is noted that the nonlinear least square algorithms that are commonly used to determine MLE are not as efficient as the iterative MDS algorithms. Thus, we propose a new integrated method MDS-MLE to effectively benefit from the strength of both methods. In the new method, MDS is used as an initialization method for MLE. With the solution of MDS as an initial value, MLE converges much faster and achieves significantly better performance than with random initial values. Superior performance of the new method is clearly demonstrated through simulation results. The effects of the deployment density of sensor nodes and reference nodes (RNs), as well as the deployment structure of RNs, are also studied through various simulations.
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paper_title: Locating the nodes: cooperative localization in wireless sensor networks

paper_content:

Accurate and low-cost sensor localization is a critical requirement for the deployment of wireless sensor networks in a wide variety of applications. In cooperative localization, sensors work together in a peer-to-peer manner to make measurements and then forms a map of the network. Various application requirements influence the design of sensor localization systems. In this article, the authors describe the measurement-based statistical models useful to describe time-of-arrival (TOA), angle-of-arrival (AOA), and received-signal-strength (RSS) measurements in wireless sensor networks. Wideband and ultra-wideband (UWB) measurements, and RF and acoustic media are also discussed. Using the models, the authors have shown the calculation of a Cramer-Rao bound (CRB) on the location estimation precision possible for a given set of measurements. The article briefly surveys a large and growing body of sensor localization algorithms. This article is intended to emphasize the basic statistical signal processing background necessary to understand the state-of-the-art and to make progress in the new and largely open areas of sensor network localization research.
---
paper_title: Indoor localization through dynamic time warping

paper_content:

Identifying and locating oneself in different hallways of high rise buildings forms the classic indoor localization problem. GPS does not work indoors and WiFi may not be omnipresent. This paper presents a novel approach to ambient magnetic fields based indoor localization. We present a system that classifies magnetic signatures using dynamic time warping. Specifically, by aligning similar magnetic signatures that differ in magnitude or time, we classify the signatures and infer the location irrepective of the person and his/her mode of commuting. A Nexus One smartphone was employed, utilizing its builtin magnetic field sensor to create a user friendly localization application solely on the phone. By using a variety of subjects including sighted, blindfolded and people using wheelchairs to handle the human speed variation problem, we evaluated the system across 26 and 15 hallways of two different buildings and obtained accuracies of 92.6%, and 91.1% respectively. With these encouraging results, we believe our proposed solution is user independent and caters to a wide range of hallways.
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paper_title: SmartPDR: Smartphone-Based Pedestrian Dead Reckoning for Indoor Localization

paper_content:

Indoor pedestrian tracking extends location-based services to indoor environments where GPS signal is rarely detected. Typical indoor localization method is Wi-Fi-based positioning system, which is practical showing accuracy and extending coverage. However, it involves significant costs of installing and managing wireless access points. A practical indoor pedestrian-tracking approach should consider the absence of any infrastructure or pretrained database. In this paper, we present a smartphone-based pedestrian dead reckoning, SmartPDR, which tracks pedestrians through typical dead reckoning approach using data from inertial sensors embedded in smartphones. SmartPDR does not require any complex and expensive additional device or infrastructure that most existing pedestrian tracking systems rely on. The proposed system was implemented on off-the-shelf smartphones and the performance was evaluated in several buildings. Despite inherent localization errors from low-cost noisy sensors and complicated human movements, SmartPDR successfully tracks indoor user's location, which is confirmed from the experimental results with reasonable location accuracy. Indoor pedestrian tracking system using smartphone inertial sensors can be a promising methodology validating its practical usage through real deployment.
---
paper_title: Proposing an Extended iBeacon System for Indoor Route Guidance

paper_content:

This paper presents a concept of an extended iBeacon system for indoornavigation and guidance. iBeacon is a proximal wireless notification service proposed by Apple, Inc. This service uses beacon modules emitting radio waves based on Bluetooth Low Energy technology and provides automatic triggering of a single notification from a beacon module to multiple smartphones at proximity. In usual iBeacon systems, there is one-to-one correspondence between notification and beacon module. However, if we introduce a memory to remember the history of iBeacon detection, iBeacon systems can be extended to handle multiple notifications by multiple beacon modules with awareness of some contexts of pedestrians' moving directions, which enables a guide, for example, to the exit of buildings. We explain this idea for indoor route guidance using iBeacon and discuss its implementation with our simple experiment and performance evaluation.
---
paper_title: Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks

paper_content:

UWB technology provides an excellent means for wireless positioning due to its high resolution capability in the time domain. Its ability to resolve multipath components makes it possible to obtain accurate location estimates without the need for complex estimation algorithms. In this article, theoretical limits for TOA estimation and TOA-based location estimation for UWB systems have been considered. Due to the complexity of the optimal schemes, suboptimal but practical alternatives have been emphasized. Performance limits for hybrid TOA/SS and TDOA/SS schemes have also been considered. Although the fundamental mechanisms for localization, including AOA-, TOA-, TDOA-, and SS-based methods, apply to all radio air interface, some positioning techniques are favored by UWB-based systems using ultrawide bandwidths.
---
paper_title: Efficient Sequence Matching and Path Construction for Geomagnetic Indoor Localization

paper_content:

Geomagnetic field is highly applicable to indoor localization due to its pervasive spatial presence, high signal stability and infrastructure-less support. Previous work in the area often fuses it with pedometer (step counter) via particles. These approaches are computationally intensive and require strong assumptions on user behavior. To overcome the weaknesses, we propose Magil, a pedometer-free approach leveraging solely upon magnetic field for indoor localization. To the best of our knowledge, this is the first piece of work using only geomagnetism for smartphone localization. Magil is applicable to any open or complex indoor environment. In the offline phase, it continuously collects and stores geomagnetic fingerprints while a surveyor is walking in pre-defined paths covering the indoor area. In the online phase, it identifies the indoor segments whose fingerprint variations highly match with the target observations. With a modified shortest path formulation, Magil selects and connects these matched segments and obtains the target locations and paths. We have implemented Magil, and conducted extensive experiments in our university campus. Our results show that Magil outperforms many state-of-the-art schemes by a wide margin (cutting localization error by more than 30%).
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paper_title: PocketSLAM based on the principle of the FootSLAM algorithm

paper_content:

Infrastructureless indoor navigation remains a challenging research area in spite of the fact that multiple low cost sensors suitable for positioning recently became available even in mobile phones. Since Global Navigation Satellite Signals (GNSS) are often unavailable indoors, the use of Inertial Measurement Units (IMUs) seems to be promising for indoor navigation. Auspicious results are available when the sensor is placed on the foot in indoor positioning applications, but still many problems remain when the drift error during stance phases cannot be reduced. An indoor navigation solution is the so called FootSLAM algorithm, where reliable positioning (positioning error below 1 meter in the case of loop closures) is possible assuming that the sensor is fixed on the foot. More recently, another navigation system was proposed where the sensor is located in the pocket of a pedestrian. Since no special fixation or no special shoe is needed, this location of the sensor is more convenient for mass market users. But also this system suffers from a remaining drift that needs to be compensated. In this paper, we propose a combination of the pocket navigation system with the FootSLAM approach and show that it is also possible to reduce the remaining drift with this combination.
---
paper_title: Proximity Detection with RFID: A Step Toward the Internet of Things

paper_content:

In the Internet of Things (IoT), things will be able to sense, communicate, and interact. They will also exchange data and information and locate themselves and other things that surround them. To interact, the things must recognize that they're in proximity of other things. It's anticipated that a widespread component of the IoT will be passive RFID tags, because they're inexpensive and provide automatic identification. However, passive RFID tags can't perform complex operations, such as proximity detection and localization. In this article, the authors describe existing problems with current RFID systems and survey potential solutions for proximity detection. They then present a new RFID device, called "Sense-a-Tag" (ST), that can passively detect and decode backscatter signals from tags in its proximity. The authors show that when STs are added to a standard RFID system, the problems of proximity detection in the IoT with RFID tags can readily be resolved. They demonstrate the feasibility of an ST-based RFID system through proximity detection experiments.
---
paper_title: Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU

paper_content:

The estimation of the position of a person in a building is a must for creating Intelligent Spaces. State-of-the-art Local Positioning Systems (LPS) require a complex sensornetwork infrastructure to locate with enough accuracy and coverage. Alternatively, Inertial Measuring Units (IMU) can be used to estimate the movement of a person; a methodology that is called Pedestrian Dead-Reckoning (PDR). In this paper, we describe and implement a Kalman-based framework, called INS-EKF-ZUPT (IEZ), to estimate the position and attitude of a person while walking. IEZ makes use of an Extended Kalman filter (EKF), an INS mechanization algorithm, a Zero Velocity Update (ZUPT) methodology, as well as, a stance detection algorithm. As the IEZ methodology is not able to estimate the heading and its drift (non-observable variables), then several methods are used for heading drift reduction: ZARU, HDR and Compass. The main contribution of the paper is the integration of the heading drift reduction algorithms into a Kalman-based IEZ platform, which represents an extended PDR methodology (IEZ+) valid for operation in indoor spaces with local magnetic disturbances. The IEZ+ PDR methodology was tested in several simulated and real indoor scenarios with a low-performance IMU mounted on the foot. The positioning errors were about 1% of the total travelled distance, which are good figures if compared with other works using IMUs of higher performance.
---
paper_title: The anatomy of a context-aware application

paper_content:

We describe a sensor-driven, or sentient, platform for context-aware computing that enables applications to follow mobile users as they move around a building. The platform is particularly suitable for richly equipped, networked environments. The only item a user is required to carry is a small sensor tag, which identifies them to the system and locates them accurately in three dimensions. The platform builds a dynamic model of the environment using these location sensors and resource information gathered by telemetry software, and presents it in a form suitable for application programmers. Use of the platform is illustrated through a practical example, which allows a user's current working desktop to follow them as they move around the environment.
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paper_title: Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances †

paper_content:

In recent years, indoor positioning has emerged as a critical function in many end-user applications; including military, civilian, disaster relief and peacekeeping missions. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task in part because various objects reflect and disperse signals. Ultra WideBand (UWB) is an emerging technology in the field of indoor positioning that has shown better performance compared to others. In order to set the stage for this work, we provide a survey of the state-of-the-art technologies in indoor positioning, followed by a detailed comparative analysis of UWB positioning technologies. We also provide an analysis of strengths, weaknesses, opportunities, and threats (SWOT) to analyze the present state of UWB positioning technologies. While SWOT is not a quantitative approach, it helps in assessing the real status and in revealing the potential of UWB positioning to effectively address the indoor positioning problem. Unlike previous studies, this paper presents new taxonomies, reviews some major recent advances, and argues for further exploration by the research community of this challenging problem space.
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paper_title: The Cricket location-support system

paper_content:

This paper presents the design, implementation, and evaluation of Cricket , a location-support system for in-building, mobile, location-dependent applications. It allows applications running on mobile and static nodes to learn their physical location by using listeners that hear and analyze information from beacons spread throughout the building. Cricket is the result of several design goals, including user privacy, decentralized administration, network heterogeneity, and low cost. Rather than explicitly tracking user location, Cricket helps devices learn where they are and lets them decide whom to advertise this information to; it does not rely on any centralized management or control and there is no explicit coordination between beacons; it provides information to devices regardless of their type of network connectivity; and each Cricket device is made from off-the-shelf components and costs less than U.S. $10. We describe the randomized algorithm used by beacons to transmit information, the use of concurrent radio and ultrasonic signals to infer distance, the listener inference algorithms to overcome multipath and interference, and practical beacon configuration and positioning techniques that improve accuracy. Our experience with Cricket shows that several location-dependent applications such as in-building active maps and device control can be developed with little effort or manual configuration.
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paper_title: The Horus WLAN location determination system

paper_content:

We present the design and implementation of the Horus WLAN location determination system. The design of the Horus system aims at satisfying two goals: high accuracy and low computational requirements. The Horus system identifies different causes for the wireless channel variations and addresses them to achieve its high accuracy. It uses location-clustering techniques to reduce the computational requirements of the algorithm. The lightweight Horus algorithm helps in supporting a larger number of users by running the algorithm at the clients.We discuss the different components of the Horus system and its implementation under two different operating systems and evaluate the performance of the Horus system on two testbeds. Our results show that the Horus system achieves its goal. It has an error of less than 0.6 meter on the average and its computational requirements are more than an order of magnitude better than other WLAN location determination systems. Moreover, the techniques developed in the context of the Horus system are general and can be applied to other WLAN location determination systems to enhance their accuracy. We also report lessons learned from experimenting with the Horus system and provide directions for future work.
---
paper_title: Indoor Pedestrian Tracking by On-Body Multiple Receivers

paper_content:

During the past years, the development of indoor localization systems has been a hot topic in research, because the global navigation satellite systems suffer from a significant performance degradation due to the fact that the line of sight to the satellites is not available. The proposed system employs the received signal strength indicator from multiple anchor nodes from an operating wireless sensor network (WSN). In addition, we place multiple receivers around the user’s body and thanks to machine learning techniques; we are able to estimate the distance and angle between the user and any of the anchor nodes of the WSN. This allows us to estimate the heading of the user without the use of inertial sensors or magnetometers. Finally, the user’s position estimation is refined using an extended Kalman filter that considers the constant velocity kinematic model. The system has been validated in multiple real scenarios obtaining a root mean squared error around the meter for the different tests performed.
---
paper_title: Smart office energy management system using bluetooth low energy based beacons and a mobile app

paper_content:

This paper describes a smart office energy management system (SOEMS) that can reduce the energy consumption of PCs, monitors, and lights in an office environment through the use of a mobile app and Bluetooth Low Energy based Beacons (BLE Beacons). BLE Beacons placed at several places in an office and a mobile app are used to determine whether a user enters or exits the office to change the power saving mode of the user's PCs, monitors, and lights. The proposed system can reduce energy consumption in the office without causing user inconvenience.
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paper_title: Bayesian indoor positioning systems

paper_content:

In this paper, we introduce a new approach to location estimation where, instead of locating a single client, we simultaneously locate a set of wireless clients. We present a Bayesian hierarchical model for indoor location estimation in wireless networks. We demonstrate that our model achieves accuracy that is similar to other published models and algorithms. By harnessing prior knowledge, our model eliminates the requirement for training data as compared with existing approaches, thereby introducing the notion of a fully adaptive zero profiling approach to location estimation.
---
paper_title: A New Location Estimation System for Wireless Networks Based on Linear Discriminant Functions and Hidden Markov Models

paper_content:

Location estimation is a recent interesting research area that exploits the possibilities of modern communication technology. In this paper, we present a new location system for wireless networks that is especially suitable for indoor terminal-based architectures, as it improves both the speed and the memory requirements. The algorithm is based on the application of linear discriminant functions and Markovian models and its performance has been compared with other systems presented in the literature. Simulation results show a very good performance in reducing the computing time and memory space and displaying an adequate behavior under conditions of few a priori calibration points per position.
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paper_title: Ad hoc positioning system (APS) using AOA

paper_content:

Position information of individual nodes is useful in implementing functions such as routing and querying in ad-hoc networks. Deriving position information by using the capability of the nodes to measure time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA) and signal strength have been used to localize nodes relative to a frame of reference. The nodes in an ad-hoc network can have multiple capabilities and exploiting one or more of the capabilities can improve the quality of positioning. In this paper, we show how AOA capability of the nodes can be used to derive position information. We propose a method for all nodes to determine their orientation and position in an ad-hoc network where only a fraction of the nodes have positioning capabilities, under the assumption that each node has the AOA capability.
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paper_title: RADAR: an in-building RF-based user location and tracking system

paper_content:

The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy.
---
paper_title: GPS-less Low Cost Outdoor Localization For Very Small Devices

paper_content:

Instrumenting the physical world through large networks of wireless sensor nodes, particularly for applications like environmental monitoring of water and soil, requires that these nodes be very small, lightweight, untethered, and unobtrusive. The problem of localization, that is, determining where a given node is physically located in a network, is a challenging one, and yet extremely crucial for many of these applications. Practical considerations such as the small size, form factor, cost and power constraints of nodes preclude the reliance on GPS of all nodes in these networks. We review localization techniques and evaluate the effectiveness of a very simple connectivity metric method for localization in outdoor environments that makes use of the inherent RF communications capabilities of these devices. A fixed number of reference points in the network with overlapping regions of coverage transmit periodic beacon signals. Nodes use a simple connectivity metric, which is more robust to environmental vagaries, to infer proximity to a given subset of these reference points. Nodes localize themselves to the centroid of their proximate reference points. The accuracy of localization is then dependent on the separation distance between two-adjacent reference points and the transmission range of these reference points. Initial experimental results show that the accuracy for 90 percent of our data points is within one-third of the separation distance. However, future work is needed to extend the technique to more cluttered environments.
---
paper_title: Locating the nodes: cooperative localization in wireless sensor networks

paper_content:

Accurate and low-cost sensor localization is a critical requirement for the deployment of wireless sensor networks in a wide variety of applications. In cooperative localization, sensors work together in a peer-to-peer manner to make measurements and then forms a map of the network. Various application requirements influence the design of sensor localization systems. In this article, the authors describe the measurement-based statistical models useful to describe time-of-arrival (TOA), angle-of-arrival (AOA), and received-signal-strength (RSS) measurements in wireless sensor networks. Wideband and ultra-wideband (UWB) measurements, and RF and acoustic media are also discussed. Using the models, the authors have shown the calculation of a Cramer-Rao bound (CRB) on the location estimation precision possible for a given set of measurements. The article briefly surveys a large and growing body of sensor localization algorithms. This article is intended to emphasize the basic statistical signal processing background necessary to understand the state-of-the-art and to make progress in the new and largely open areas of sensor network localization research.
---
paper_title: Relative Location Estimation in Wireless Sensor Networks

paper_content:

Self-configuration in wireless sensor networks is a general class of estimation problems that we study via the Cramer-Rao bound (CRB). Specifically, we consider sensor location estimation when sensors measure received signal strength (RSS) or time-of-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the network have a known location, whereas the remaining locations must be estimated. We derive CRBs and maximum-likelihood estimators (MLEs) under Gaussian and log-normal models for the TOA and RSS measurements, respectively. An extensive TOA and RSS measurement campaign in an indoor office area illustrates MLE performance. Finally, relative location estimation algorithms are implemented in a wireless sensor network testbed and deployed in indoor and outdoor environments. The measurements and testbed experiments demonstrate 1-m RMS location errors using TOA, and 1- to 2-m RMS location errors using RSS.
---
paper_title: A stigmergic approach to indoor localization using Bluetooth Low Energy beacons

paper_content:

Localization of people and devices is one of the main building blocks of context aware systems since the user position represents the core information for detecting user's activities, devices activations, proximity to points of interest, etc. While for outdoor scenarios Global Positioning System (GPS) constitutes a reliable and easily available technology, for indoor scenarios GPS is largely unavailable. In this paper we present a range-based indoor localization system that exploits the Received Signal Strength (RSS) of Bluetooth Low Energy (BLE) beacon packets broadcast by anchor nodes and received by a BLE-enabled device. The method used to infer the user's position is based on stigmergy. We exploit the stigmergic marking process to create an on-line probability map identifying the user's position in the indoor environment.
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paper_title: Indoor Localization Using Improved RSS-Based Lateration Methods

paper_content:

Location estimation is a critical step for many location-aware applications. To obtain location information, localization methods employing Received Signal Strength (RSS) are attestative since it can reuse the existing wireless infrastructure for localization. Among the large class of localization schemes, RSS-based lateration methods have the advantage of providing closed-form solutions for mathematical analysis as compared to heuristic-based localization approaches. However, the localization accuracy of RSS-based lateration methods are significantly affected by the unpredictable setup in indoor environments. To improve the applicability of RSS-based lateration methods in indoors, we propose two approaches, regression-based and correlation-based. The regression-based approach uses linear regression to discover a better fit of signal propagation model between RSS and the distance, while the correlation-based approach utilizes the correlation among RSS in local area to obtain more accurate signal propagation. Our results using both simulation as well as real experiments demonstrate that our improved methods outperform the original RSS-based lateration methods significantly.
---
paper_title: Collaborative Localization With Received-Signal Strength in Wireless Sensor Networks

paper_content:

In this paper, we present an in-depth study of two collaborative-localization methods, called the multidimensional scaling (MDS) and maximum-likelihood estimator (MLE), for wireless sensor networks. From theoretical analysis, it is shown that MLE is more appropriate than MDS, given the underlying assumption of statistical signal models of the received-signal-strength-based localization problem. We also show that MDS can approximately achieve asymptotic efficiency with appropriate weighting schemes in some scenarios. From extensive simulation results, it is noted that the nonlinear least square algorithms that are commonly used to determine MLE are not as efficient as the iterative MDS algorithms. Thus, we propose a new integrated method MDS-MLE to effectively benefit from the strength of both methods. In the new method, MDS is used as an initialization method for MLE. With the solution of MDS as an initial value, MLE converges much faster and achieves significantly better performance than with random initial values. Superior performance of the new method is clearly demonstrated through simulation results. The effects of the deployment density of sensor nodes and reference nodes (RNs), as well as the deployment structure of RNs, are also studied through various simulations.
---
paper_title: Locating the nodes: cooperative localization in wireless sensor networks

paper_content:

Accurate and low-cost sensor localization is a critical requirement for the deployment of wireless sensor networks in a wide variety of applications. In cooperative localization, sensors work together in a peer-to-peer manner to make measurements and then forms a map of the network. Various application requirements influence the design of sensor localization systems. In this article, the authors describe the measurement-based statistical models useful to describe time-of-arrival (TOA), angle-of-arrival (AOA), and received-signal-strength (RSS) measurements in wireless sensor networks. Wideband and ultra-wideband (UWB) measurements, and RF and acoustic media are also discussed. Using the models, the authors have shown the calculation of a Cramer-Rao bound (CRB) on the location estimation precision possible for a given set of measurements. The article briefly surveys a large and growing body of sensor localization algorithms. This article is intended to emphasize the basic statistical signal processing background necessary to understand the state-of-the-art and to make progress in the new and largely open areas of sensor network localization research.
---
paper_title: Indoor Pedestrian Tracking by On-Body Multiple Receivers

paper_content:

During the past years, the development of indoor localization systems has been a hot topic in research, because the global navigation satellite systems suffer from a significant performance degradation due to the fact that the line of sight to the satellites is not available. The proposed system employs the received signal strength indicator from multiple anchor nodes from an operating wireless sensor network (WSN). In addition, we place multiple receivers around the user’s body and thanks to machine learning techniques; we are able to estimate the distance and angle between the user and any of the anchor nodes of the WSN. This allows us to estimate the heading of the user without the use of inertial sensors or magnetometers. Finally, the user’s position estimation is refined using an extended Kalman filter that considers the constant velocity kinematic model. The system has been validated in multiple real scenarios obtaining a root mean squared error around the meter for the different tests performed.
---
paper_title: A mobile indoor positioning system based on iBeacon technology

paper_content:

To increase the efficiency in the emergency room, the goal of this research is to implement a mobile-based indoor positioning system using mobile applications (APP) with the iBeacon solution based on the Bluetooth Low Energy (BLE) technology. We use the Received Signal Strength (RSS) based localization method to estimate the patients' locations. Our positioning algorithm achieves 97.22% (95% Confidence Interval = 95.90% - 98.55%) accuracy of classification. As the result, our mechanism is reliable enough to satisfy the need for medical staff to track the locations of their patients.
---
paper_title: Proposing an Extended iBeacon System for Indoor Route Guidance

paper_content:

This paper presents a concept of an extended iBeacon system for indoornavigation and guidance. iBeacon is a proximal wireless notification service proposed by Apple, Inc. This service uses beacon modules emitting radio waves based on Bluetooth Low Energy technology and provides automatic triggering of a single notification from a beacon module to multiple smartphones at proximity. In usual iBeacon systems, there is one-to-one correspondence between notification and beacon module. However, if we introduce a memory to remember the history of iBeacon detection, iBeacon systems can be extended to handle multiple notifications by multiple beacon modules with awareness of some contexts of pedestrians' moving directions, which enables a guide, for example, to the exit of buildings. We explain this idea for indoor route guidance using iBeacon and discuss its implementation with our simple experiment and performance evaluation.
---
paper_title: Proximity Detection with RFID: A Step Toward the Internet of Things

paper_content:

In the Internet of Things (IoT), things will be able to sense, communicate, and interact. They will also exchange data and information and locate themselves and other things that surround them. To interact, the things must recognize that they're in proximity of other things. It's anticipated that a widespread component of the IoT will be passive RFID tags, because they're inexpensive and provide automatic identification. However, passive RFID tags can't perform complex operations, such as proximity detection and localization. In this article, the authors describe existing problems with current RFID systems and survey potential solutions for proximity detection. They then present a new RFID device, called "Sense-a-Tag" (ST), that can passively detect and decode backscatter signals from tags in its proximity. The authors show that when STs are added to a standard RFID system, the problems of proximity detection in the IoT with RFID tags can readily be resolved. They demonstrate the feasibility of an ST-based RFID system through proximity detection experiments.
---
paper_title: Smart office energy management system using bluetooth low energy based beacons and a mobile app

paper_content:

This paper describes a smart office energy management system (SOEMS) that can reduce the energy consumption of PCs, monitors, and lights in an office environment through the use of a mobile app and Bluetooth Low Energy based Beacons (BLE Beacons). BLE Beacons placed at several places in an office and a mobile app are used to determine whether a user enters or exits the office to change the power saving mode of the user's PCs, monitors, and lights. The proposed system can reduce energy consumption in the office without causing user inconvenience.
---
paper_title: RFID tags: Positioning principles and localization techniques

paper_content:

RFID is an automatic identification technology that enables tracking of people and objects. Both identity and location are generally key information for indoor services. An obvious and interesting method to obtain these two types of data is to localize RFID tags attached to devices or objects or carried by people. However, signals in indoor environments are generally harshly impaired and tags have very limited capabilities which pose many challenges for positioning them. In this work, we propose a classification and survey the current state-of-art of RFID localization by first presenting this technology and positioning principles. Then, we explain and classify RFID localization techniques. Finally, we discuss future trends in this domain.
---
paper_title: RFID localization algorithms and applications—a review

paper_content:

Object localization based on radio frequency identification (RFID) technology has promising potentials. By combining localization with its identification capability, existing applications can be enhanced and new applications can be developed for this technology. This paper starts with an overview introducing the available technologies for localization with a focus on radio frequency based technologies. The existing and potential applications of RFID localization in various industries are then summarized. Moreover, RFID localization algorithms are reviewed, which can be categorized into multilateration, Bayesian inference, nearest-neighbor, proximity, and kernel-based learning methods. Also, we present a localization case study using passive RFID technology, and it shows that objects can be successfully localized using either multilateration or Bayesian inference methods. The survey also discusses the challenges and future research on RFID localization.
---
paper_title: GPS-less Low Cost Outdoor Localization For Very Small Devices

paper_content:

Instrumenting the physical world through large networks of wireless sensor nodes, particularly for applications like environmental monitoring of water and soil, requires that these nodes be very small, lightweight, untethered, and unobtrusive. The problem of localization, that is, determining where a given node is physically located in a network, is a challenging one, and yet extremely crucial for many of these applications. Practical considerations such as the small size, form factor, cost and power constraints of nodes preclude the reliance on GPS of all nodes in these networks. We review localization techniques and evaluate the effectiveness of a very simple connectivity metric method for localization in outdoor environments that makes use of the inherent RF communications capabilities of these devices. A fixed number of reference points in the network with overlapping regions of coverage transmit periodic beacon signals. Nodes use a simple connectivity metric, which is more robust to environmental vagaries, to infer proximity to a given subset of these reference points. Nodes localize themselves to the centroid of their proximate reference points. The accuracy of localization is then dependent on the separation distance between two-adjacent reference points and the transmission range of these reference points. Initial experimental results show that the accuracy for 90 percent of our data points is within one-third of the separation distance. However, future work is needed to extend the technique to more cluttered environments.
---
paper_title: Performance Evaluation of Radio Map Construction Methods for Wi-Fi Positioning Systems

paper_content:

A radio map is a collection of signal fingerprints labeled with their collected locations. It is known that the performance of a fingerprint-based positioning systems is closely related to the precision and accuracy of the underlying radio maps. However, little has been studied on the performance of radio maps in relation to the fingerprint collection methods and the radio map models, which determine the accuracy and precision of radio maps, respectively. This paper evaluates the performance of various radio map construction methods in both indoor and outdoor environments. Four radio map construction methods, i.e., a point-by-point manual calibration, a walking survey, a semisupervised learning-based method, and an unsupervised learning-based method, have been compared. We also evaluate the performance of various types of radio map models that represent the characteristics of collected fingerprints. To demonstrate the importance of the radio map model, a new model named signal fluctuation matrix (SFM) was developed, and its performance was compared with that of the three conventional radio map models, respectively. The evaluation revealed that the performance of the radio maps was very sensitive to the design of radio map models and the number of fingerprints collected at each location. The performance achieved by SFM-based positioning was comparable with that of the other models despite using a small number of fingerprints.
---
paper_title: Building a Practical Wi-Fi-Based Indoor Navigation System

paper_content:

This article presents the seven-step process involved in building a practical Wi-Fi-based indoor navigation system, which was implemented at the COEX complex in Seoul, Korea, in October 2010. The article describes the primary activities in each step using the COEX example. More than 200,000 users have downloaded the system since its first release. The successful launch of the COEX indoor navigation system suggests that indoor navigation is becoming a reality.
---
paper_title: Probability kernel regression for WiFi localisation

paper_content:

Various methods have been developed for indoor localisation using WLAN signals. Algorithms that fingerprint the received signal strength indicators RSSI of WiFi for different locations can achieve tracking accuracies of the order of a few metres. RSSI fingerprinting suffers from two main limitations: first, as the signal environment changes, so does the fingerprint database, which requires regular updates; second, it has been reported that, in practice, certain devices record more complex e.g bimodal distributions of WiFi signals, precluding algorithms based on the mean RSSI. Mirowski et al . [2011. KL-divergence kernel regression for non-Gaussian fingerprint based localization. In : International conference on indoor positioning and indoor navigation , Guimaraes, Portugal] have recently introduced a simple methodology that takes into account the full distribution for computing similarities among fingerprints using the Kullback–Leibler KL divergence, and then performs localisation through kernel regression. Their algorithm provides a natural way of smoothing over time and motion trajectories and can be applied directly to histograms of WiFi connections to access points, ignoring RSSI distributions, hence removing the need for fingerprint recalibration. It has been shown to outperform nearest neighbours or Kalman and particle filtres, achieving up to 1 m accuracy in office environments. In this article, we focus on the relevance of Gaussian or non-Gaussian distributions for modelling RSSI distributions by considering additional probabilistic kernels for comparing Gaussian distributions and by evaluating them on three contrasting datasets. We discuss their limitations and formulate how the KL-divergence kernel regression algorithm bridges the gap with other WiFi localisation algorithms, notably Bayesian networks, support vector machines and K nearest neighbours. Finally, we revisit the assumptions on the fingerprint maps and overview practical WiFi localisation software implementation.
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paper_title: How feasible is the use of magnetic field alone for indoor positioning?

paper_content:

The use of magnetic field variations for positioning and navigation has been suggested by several researchers. In most of the applications, the magnetic field is used to determine the azimuth or heading. However, for indoor applications, accurate heading determination is difficult due to the presence of magnetic field anomalies. Here location fingerprinting methodology can take advantage of these anomalies. In fact, the more significant the local anomalies, the more unique the magnetic “fingerprint”. In general, the more elements in each fingerprint, the better for positioning. Unfortunately, magnetic field intensity data only consists of three components. Since true north (or magnetic north) is generally unknown, even with help of the accelerometer to detect the direction of the gravity, only two components can be extracted, i.e. the horizontal intensity and the vertical intensity (or total intensity and inclination). Furthermore, moving objects containing ferromagnetic materials and electronic devices may affect the magnetic field. Tests were carried out to investigate the feasibility of using magnetic field alone for indoor positioning. Possible solutions are discussed.
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paper_title: On the calibration and performance of RSS-based localization methods

paper_content:

This paper analyzes the performance of several Received Signal Strength (RSS) based localization methods as a function of the calibration effort, hence as a function of deployment and maintenance costs. The deployment and maintenance costs determine the scalability and thus the applicability of a localization algorithm, and this is still a topic of research. This paper analyzes and compares the best available localization algorithms of the following localization methods: fingerprinting-, range- and proximity-based localization. An extensive amount of RSS measurements, performed in a realistic indoor environment show that range-based algorithms outperform fingerprinting-and proximity-based localization algorithms when there is a limited amount of calibration measurements available. In that case, range-based algorithms have ∼ 30% smaller errors, ∼ 1.3 meter compared to ∼ 1.9 meter. Our measurements show that fingerprinting-based algorithms approximate the performance of range-based algorithms as the number of calibration measurements increases from 1 to 80.
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paper_title: Indoor localization through dynamic time warping

paper_content:

Identifying and locating oneself in different hallways of high rise buildings forms the classic indoor localization problem. GPS does not work indoors and WiFi may not be omnipresent. This paper presents a novel approach to ambient magnetic fields based indoor localization. We present a system that classifies magnetic signatures using dynamic time warping. Specifically, by aligning similar magnetic signatures that differ in magnitude or time, we classify the signatures and infer the location irrepective of the person and his/her mode of commuting. A Nexus One smartphone was employed, utilizing its builtin magnetic field sensor to create a user friendly localization application solely on the phone. By using a variety of subjects including sighted, blindfolded and people using wheelchairs to handle the human speed variation problem, we evaluated the system across 26 and 15 hallways of two different buildings and obtained accuracies of 92.6%, and 91.1% respectively. With these encouraging results, we believe our proposed solution is user independent and caters to a wide range of hallways.
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paper_title: Efficient Sequence Matching and Path Construction for Geomagnetic Indoor Localization

paper_content:

Geomagnetic field is highly applicable to indoor localization due to its pervasive spatial presence, high signal stability and infrastructure-less support. Previous work in the area often fuses it with pedometer (step counter) via particles. These approaches are computationally intensive and require strong assumptions on user behavior. To overcome the weaknesses, we propose Magil, a pedometer-free approach leveraging solely upon magnetic field for indoor localization. To the best of our knowledge, this is the first piece of work using only geomagnetism for smartphone localization. Magil is applicable to any open or complex indoor environment. In the offline phase, it continuously collects and stores geomagnetic fingerprints while a surveyor is walking in pre-defined paths covering the indoor area. In the online phase, it identifies the indoor segments whose fingerprint variations highly match with the target observations. With a modified shortest path formulation, Magil selects and connects these matched segments and obtains the target locations and paths. We have implemented Magil, and conducted extensive experiments in our university campus. Our results show that Magil outperforms many state-of-the-art schemes by a wide margin (cutting localization error by more than 30%).
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paper_title: Indoor location sensing using geo-magnetism

paper_content:

We present an indoor positioning system that measures location using disturbances of the Earth's magnetic field caused by structural steel elements in a building. The presence of these large steel members warps the geomagnetic field in a way that is spatially varying but temporally stable. To localize, we measure the magnetic field using an array of e-compasses and compare the measurement with a previously obtained magnetic map. We demonstrate accuracy within 1 meter 88% of the time in experiments in two buildings and across multiple floors within the buildings. We discuss several constraint techniques that can maintain accuracy as the sample space increases.
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paper_title: The Horus WLAN location determination system

paper_content:

We present the design and implementation of the Horus WLAN location determination system. The design of the Horus system aims at satisfying two goals: high accuracy and low computational requirements. The Horus system identifies different causes for the wireless channel variations and addresses them to achieve its high accuracy. It uses location-clustering techniques to reduce the computational requirements of the algorithm. The lightweight Horus algorithm helps in supporting a larger number of users by running the algorithm at the clients.We discuss the different components of the Horus system and its implementation under two different operating systems and evaluate the performance of the Horus system on two testbeds. Our results show that the Horus system achieves its goal. It has an error of less than 0.6 meter on the average and its computational requirements are more than an order of magnitude better than other WLAN location determination systems. Moreover, the techniques developed in the context of the Horus system are general and can be applied to other WLAN location determination systems to enhance their accuracy. We also report lessons learned from experimenting with the Horus system and provide directions for future work.
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paper_title: Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges

paper_content:

Wireless Local Area Network (WLAN) has become a promising choice for indoor positioning as the only existing and established infrastructure, to localize the mobile and stationary users indoors. However, since WLAN has been initially designed for wireless networking and not positioning, the localization task based on WLAN signals has several challenges. Amongst the WLAN positioning methods, WLAN fingerprinting localization has recently achieved great attention due to its promising results. WLAN fingerprinting faces several challenges and hence, in this paper, our goal is to overview these challenges and the state-of-the-art solutions. This paper consists of three main parts: 1) Conventional localization schemes; 2) State-of-the-art approaches; 3) Practical deployment challenges. Since all the proposed methods in WLAN literature have been conducted and tested in different settings, the reported results are not equally comparable. So, we compare some of the main localization schemes in a single real environment and assess their localization accuracy, positioning error statistics, and complexity. Our results depict illustrative evaluation of WLAN localization systems and guide to future improvement opportunities.
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paper_title: A survey of calibration-free indoor positioning systems

paper_content:

Last decade observed a significant research effort directed towards indoor localization utilizing location fingerprinting techniques. Fingerprinting solutions generally require a pre-deployment site survey procedure during which a radio-map is constructed by laboriously collecting signal strength samples (e.g., Wi-Fi) over the whole localization area. However, such localization efforts have certain shortcomings. For example, it is time consuming, labor intensive, vulnerable to environmental changes, and the process requires certain pedigree on the surveyor that may deem the fingerprinting techniques impractical to be deployed over large areas (e.g., shopping malls, multi-storey offices/residences, etc.). Newer emerging techniques try to bypass this expensive pre-deployment effort of fingerprinting solutions altogether. They may build the radio-map through the implicit participation of the building occupants, office employee, shoppers, visitors, etc. Apart from the traditional performance comparison criteria like accuracy, precision, robustness, scalability, algorithmic complexity based on which the localization techniques were evaluated, these newer approaches warrant some additional ones. For example, whether they require an actual geographical map of the localization area, the percentage of occasional location fix to ensure reasonable accuracy, the usage of explicit/implicit user participation to construct the radio map, the usage of building landmarks (e.g., entrance, conference room, elevator, escalator, etc.) or additional sensors (e.g., accelerometer, gyroscope, compass, etc.), whether they address device heterogeneity, etc. In this article, we survey the newer emerging fingerprinting solutions that try to relieve the pre-deployment woes. We also identify some newer performance comparison criteria based on these solutions’ inherent characteristics, and apply them together with the traditional ones in order to evaluate a number of such proposed systems.
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paper_title: WLAN location determination in e-home via support vector classification

paper_content:

Because of the advanced development in computer technology, home automation system could provide a variety of convenient and novel services to people. But only providing many kinds of services is not enough; instead, upgrading the quality of services is also a very important issue. One way to upgrade the service quality is to customize the service according to the inhabitant's personal situation, and the user location is the key information for the home automation system to customize the services. Another impact of the advanced computer technology is to make the personal digital device to commonly have the capability to communicate through the wireless networks, and the popularity of wireless networks in home has increased in recent years. As a result, home automation system can bring services to personal digital devices held by people through any wireless network, and customize the services according to the location of personal digital device in home. In this paper, we present a location determination system for the home automation system to provide location aware services. This location determination system uses support vector machine to classify the location of a wireless client from its signal strength measures, and we describe its architecture and discuss its performance.
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paper_title: Bayesian indoor positioning systems

paper_content:

In this paper, we introduce a new approach to location estimation where, instead of locating a single client, we simultaneously locate a set of wireless clients. We present a Bayesian hierarchical model for indoor location estimation in wireless networks. We demonstrate that our model achieves accuracy that is similar to other published models and algorithms. By harnessing prior knowledge, our model eliminates the requirement for training data as compared with existing approaches, thereby introducing the notion of a fully adaptive zero profiling approach to location estimation.
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paper_title: A New Location Estimation System for Wireless Networks Based on Linear Discriminant Functions and Hidden Markov Models

paper_content:

Location estimation is a recent interesting research area that exploits the possibilities of modern communication technology. In this paper, we present a new location system for wireless networks that is especially suitable for indoor terminal-based architectures, as it improves both the speed and the memory requirements. The algorithm is based on the application of linear discriminant functions and Markovian models and its performance has been compared with other systems presented in the literature. Simulation results show a very good performance in reducing the computing time and memory space and displaying an adequate behavior under conditions of few a priori calibration points per position.
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paper_title: RADAR: an in-building RF-based user location and tracking system

paper_content:

The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy.
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paper_title: Comparison of Opportunistic Signals for Localisation

paper_content:

Abstract This paper presents a brief overview of how opportunistic signals can be utilised for localisation. Two different techniques for using opportunistic signals have been implemented and are validated through a number of experiments. These experiments show the performance of the navigation system when configured to use different signal types including Medium Wave radio, Digital Audio Broadcast, GSM and 3G. The same dataset is used for each experiment and uses data simulated from real transmitters operational in the Sydney region which enables a comparison of the performance benefits of using different signal types in this environment. Results show that there are three dominant factors that contribute to the quality of the solution; accuracy of the timing information contained in the signal, and the quantity and location of the transmitters used for the estimation.
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paper_title: A Survey of Indoor Inertial Positioning Systems for Pedestrians

paper_content:

With the continual miniaturisation of sensors and processing nodes, Pedestrian Dead Reckoning (PDR) systems are becoming feasible options for indoor tracking. These use inertial and other sensors, often combined with domain-specific knowledge about walking, to track user movements. There is currently a wealth of relevant literature spread across different research communities. In this survey, a taxonomy of modern PDRs is developed and used to contextualise the contributions from different areas. Techniques for step detection, characterisation, inertial navigation and step-and-heading-based dead-reckoning are reviewed and compared. Techniques that incorporate building maps through particle filters are analysed, along with hybrid systems that use absolute position fixes to correct dead-reckoning output. In addition, consideration is given to the possibility of using smartphones as PDR sensing devices. The survey concludes that PDR techniques alone can offer good short- to medium- term tracking under certain circumstances, but that regular absolute position fixes from partner systems will be needed to ensure long-term operation and to cope with unexpected behaviours. It concludes by identifying a detailed list of challenges for PDR researchers.
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paper_title: Unscented Kalman filter and Magnetic Angular Rate Update (MARU) for an improved Pedestrian Dead-Reckoning

paper_content:

The Extended Kalman Filter (EKF) has been the state of the art in Pedestrian Dead-Reckoning for foot-mounted Inertial Measurements Units. However due to the non-linearity in the propagation of the orientation the EKF is not the optimal Bayesian filter. We propose the usage of the Unscented Kalman Filter (UKF) as the integration algorithm for the inertial measurements. The UKF improves the mean and covariance propagation needed for the Kalman filter. Although the UKF provides a better estimate of the orientation, with Zero velocity UPdaTes (ZUPT) measurements, the yaw and the bias in the gyroscope associated with it becomes unobserved and might generate errors in the positioning. We studied the changes in the magnetic field during the stance phase and their relationship with the turn rates to propose three measurements using the magnetometer signal that will be called Magnetic Angular Rate Updates (MARUs). The first measurement uses the change in the angle of the magnetic field in the horizontal plane to measure the change in the yaw and provides a simple measurement for the UKF implementation. The second measurement relates the change in the magnetic field vector to the turn rate and provides information on the bias of the gyroscope for an UKF. The last measurement uses a first order approximation to generate a linear relationship with the gyroscope bias and therefore it can be used in an EKF. Finally we proposed a metric for the reliability of the stance as a way to use the pre and post stance information but adjusting the covariance of the measurements gradually from swing to stance. These methods were tested on real and simulated signals and they have shown improvements over the original PDR algorithms.
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paper_title: Heuristic reduction of gyro drift in IMU-based personnel tracking systems

paper_content:

The paper pertains to the reduction of measurement errors in gyroscopes used for tracking the position of walking persons. Some of these tracking systems commonly use inertial or other means to measure distance traveled, and one or more gyros to measure changes in heading. MEMS-type gyros or IMUs are best suited for this task because of their small size and low weight. However, these gyros have large drift rates and can be sensitive to accelerations. The Heuristic Drift Reduction (HDR) method presented in this paper estimates the drift component and eliminates it, reducing heading errors by almost one order of magnitude.
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paper_title: Personal Dead Reckoning System with Shoe Mounted Inertial Sensors

paper_content:

Method and machine for pelleting dry pellets from the mass (11) containing thermo-hardening substances, particularly for use as fish feed, where the mass is first compressed and then heated to a thermo-hardening temperature and finally cooled. The bi-products from heating, such as fat, other liquids or gas, are drained off in or near the heating zone (6) and are brought in contact with and thereby returned to the thermo-hardened mass after this has been cooled.
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paper_title: Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU

paper_content:

The estimation of the position of a person in a building is a must for creating Intelligent Spaces. State-of-the-art Local Positioning Systems (LPS) require a complex sensornetwork infrastructure to locate with enough accuracy and coverage. Alternatively, Inertial Measuring Units (IMU) can be used to estimate the movement of a person; a methodology that is called Pedestrian Dead-Reckoning (PDR). In this paper, we describe and implement a Kalman-based framework, called INS-EKF-ZUPT (IEZ), to estimate the position and attitude of a person while walking. IEZ makes use of an Extended Kalman filter (EKF), an INS mechanization algorithm, a Zero Velocity Update (ZUPT) methodology, as well as, a stance detection algorithm. As the IEZ methodology is not able to estimate the heading and its drift (non-observable variables), then several methods are used for heading drift reduction: ZARU, HDR and Compass. The main contribution of the paper is the integration of the heading drift reduction algorithms into a Kalman-based IEZ platform, which represents an extended PDR methodology (IEZ+) valid for operation in indoor spaces with local magnetic disturbances. The IEZ+ PDR methodology was tested in several simulated and real indoor scenarios with a low-performance IMU mounted on the foot. The positioning errors were about 1% of the total travelled distance, which are good figures if compared with other works using IMUs of higher performance.
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paper_title: A comparison of Pedestrian Dead-Reckoning algorithms using a low-cost MEMS IMU

paper_content:

Human localization is a very valuable information for smart environments. State-of-the-art Local Positioning Systems (LPS) require a complex sensor-network infrastructure to locate with enough accuracy and coverage. Alternatively, Inertial Measuring Units (IMU) can be used to estimate the movement of a person, by detecting steps, estimating stride lengths and the directions of motion; a methodology that is called Pedestrian Dead-Reckoning (PDR). In this paper, we use low-performance Micro-Electro-Mechanical (MEMS) inertial sensors attached to the foot of a person. This sensor has triaxial orthogonal accelerometers, gyroscopes and magnetometers. We describe, implement and compare several of the most relevant algorithms for step detection, stride length, heading and position estimation. The challenge using MEMS is to provide location estimations with enough accuracy and a limited drift. Several tests were conducted outdoors and indoors, and we found that the stride length estimation errors were about 1%. The positioning errors were almost always below 5% of the total travelled distance. The main source of positioning errors are the absolute orientation estimation.
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paper_title: Recognition of walking behaviors for pedestrian navigation

paper_content:

This paper presents a method for detecting and classifying walking behaviors based on acceleration measurements of a pedestrian, and is employed in an indoor navigation system currently being developed. The prototype navigation system uses a set of inexpensive and wearable sensors: a bi-axial accelerometer, a digital compass, and an infrared light detector. Using the measured acceleration data, the proposed method can detect forward steps and classify the steps as: "level ground", "up", and "down". The objective of the detection is to count steps for estimating the current position by dead-reckoning using heading measurements. The capability in detecting "up/down" steps can be used to correct estimated position errors. The effectiveness of the proposed method is demonstrated by experiments on six persons.
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paper_title: A Survey of Indoor Inertial Positioning Systems for Pedestrians

paper_content:

With the continual miniaturisation of sensors and processing nodes, Pedestrian Dead Reckoning (PDR) systems are becoming feasible options for indoor tracking. These use inertial and other sensors, often combined with domain-specific knowledge about walking, to track user movements. There is currently a wealth of relevant literature spread across different research communities. In this survey, a taxonomy of modern PDRs is developed and used to contextualise the contributions from different areas. Techniques for step detection, characterisation, inertial navigation and step-and-heading-based dead-reckoning are reviewed and compared. Techniques that incorporate building maps through particle filters are analysed, along with hybrid systems that use absolute position fixes to correct dead-reckoning output. In addition, consideration is given to the possibility of using smartphones as PDR sensing devices. The survey concludes that PDR techniques alone can offer good short- to medium- term tracking under certain circumstances, but that regular absolute position fixes from partner systems will be needed to ensure long-term operation and to cope with unexpected behaviours. It concludes by identifying a detailed list of challenges for PDR researchers.
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paper_title: SmartPDR: Smartphone-Based Pedestrian Dead Reckoning for Indoor Localization

paper_content:

Indoor pedestrian tracking extends location-based services to indoor environments where GPS signal is rarely detected. Typical indoor localization method is Wi-Fi-based positioning system, which is practical showing accuracy and extending coverage. However, it involves significant costs of installing and managing wireless access points. A practical indoor pedestrian-tracking approach should consider the absence of any infrastructure or pretrained database. In this paper, we present a smartphone-based pedestrian dead reckoning, SmartPDR, which tracks pedestrians through typical dead reckoning approach using data from inertial sensors embedded in smartphones. SmartPDR does not require any complex and expensive additional device or infrastructure that most existing pedestrian tracking systems rely on. The proposed system was implemented on off-the-shelf smartphones and the performance was evaluated in several buildings. Despite inherent localization errors from low-cost noisy sensors and complicated human movements, SmartPDR successfully tracks indoor user's location, which is confirmed from the experimental results with reasonable location accuracy. Indoor pedestrian tracking system using smartphone inertial sensors can be a promising methodology validating its practical usage through real deployment.
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paper_title: On foot navigation: continuous step calibration using both complementary recursive prediction and adaptive Kalman filtering

paper_content:

Keywords: topotraj Reference TOPO-CONF-2000-007 Record created on 2004-12-16, modified on 2016-08-08
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paper_title: A Wireless Sensor Network for Real-Time Indoor Localisation and Motion Monitoring

paper_content:

This paper describes the development and deployment of a wireless sensor network for monitoring human motion and position in an indoor environment. Mobile sensor nodes comprising mote-type devices, along with inertial sensors are worn by persons moving inside buildings. Motion data is preprocessed onboard mobile nodes and transferred to a static network of seed nodes using a delay tolerant protocol with minimal radio packet overhead. A Monte Carlo based localisation algorithm is implemented, which uses a person's pedometry data, indoor map information and seed node positions to provide accurate, real-time indoor location information. The performance of the network protocols and localisation algorithm are evaluated using simulated and real experimental data.
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paper_title: Collaborative Mapping for Pedestrian Navigation in Security Applications

paper_content:

In rescue missions or law enforcement applications, accurate determination of every team member’s position and providing this information on a map may significantly improve mutual situation awareness and potentially reduce the risk of accidentally harming a team member. Furthermore, it could help keep track of the areas that have been already visited, helping the coordination of the mission at hand.
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paper_title: FootSLAM: Pedestrian Simultaneous Localization and Mapping Without Exteroceptive Sensors—Hitchhiking on Human Perception and Cognition

paper_content:

In this paper, we describe FootSLAM, a Bayesian estimation approach that achieves simultaneous localization and mapping for pedestrians. FootSLAM uses odometry obtained with foot-mounted inertial sensors. Whereas existing approaches to infrastructure-less pedestrian position determination are either subject to unbounded growth of positioning error, or require either a priori map information, or exteroceptive sensors, such as cameras or light detection and ranging (LIDARs), FootSLAM achieves long-term error stability solely based on inertial sensor measurements. An analysis of the problem based on a dynamic Bayesian network (DBN) model reveals that this surprising result becomes possible by effectively hitchhiking on human perception and cognition. Two extensions to FootSLAM, namely, PlaceSLAM, for incorporating additional measurements or user provided hints, and FeetSLAM, for automated collaborative mapping, are discussed. Experimental data that validate FootSLAM and its extensions are presented. It is foreseeable that the sensors and processing power of future devices such as smartphones are likely to suffice to position the bearer with the same accuracy that FootSLAM achieves with foot-mounted sensors already today.
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paper_title: Simultaneous localization and mapping: part I

paper_content:

This paper describes the simultaneous localization and mapping (SLAM) problem and the essential methods for solving the SLAM problem and summarizes key implementations and demonstrations of the method. While there are still many practical issues to overcome, especially in more complex outdoor environments, the general SLAM method is now a well understood and established part of robotics. Another part of the tutorial summarized more recent works in addressing some of the remaining issues in SLAM, including computation, feature representation, and data association
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paper_title: Simultaneous localization and mapping (SLAM): part II

paper_content:

This paper discusses the recursive Bayesian formulation of the simultaneous localization and mapping (SLAM) problem in which probability distributions or estimates of absolute or relative locations of landmarks and vehicle pose are obtained. The paper focuses on three key areas: computational complexity; data association; and environment representation
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paper_title: PocketSLAM based on the principle of the FootSLAM algorithm

paper_content:

Infrastructureless indoor navigation remains a challenging research area in spite of the fact that multiple low cost sensors suitable for positioning recently became available even in mobile phones. Since Global Navigation Satellite Signals (GNSS) are often unavailable indoors, the use of Inertial Measurement Units (IMUs) seems to be promising for indoor navigation. Auspicious results are available when the sensor is placed on the foot in indoor positioning applications, but still many problems remain when the drift error during stance phases cannot be reduced. An indoor navigation solution is the so called FootSLAM algorithm, where reliable positioning (positioning error below 1 meter in the case of loop closures) is possible assuming that the sensor is fixed on the foot. More recently, another navigation system was proposed where the sensor is located in the pocket of a pedestrian. Since no special fixation or no special shoe is needed, this location of the sensor is more convenient for mass market users. But also this system suffers from a remaining drift that needs to be compensated. In this paper, we propose a combination of the pocket navigation system with the FootSLAM approach and show that it is also possible to reduce the remaining drift with this combination.
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paper_title: Accurate Pedestrian Indoor Navigation by Tightly Coupling Foot-Mounted IMU and RFID Measurements

paper_content:

We present a new method to accurately locate persons indoors by fusing inertial navigation system (INS) techniques with active RFID technology. A foot-mounted inertial measuring units (IMUs)-based position estimation method, is aided by the received signal strengths (RSSs) obtained from several active RFID tags placed at known locations in a building. In contrast to other authors that integrate IMUs and RSS with a loose Kalman filter (KF)-based coupling (by using the residuals of inertial- and RSS-calculated positions), we present a tight KF-based INS/RFID integration, using the residuals between the INS-predicted reader-to-tag ranges and the ranges derived from a generic RSS path-loss model. Our approach also includes other drift reduction methods such as zero velocity updates (ZUPTs) at foot stance detections, zero angular-rate updates (ZARUs) when the user is motionless, and heading corrections using magnetometers. A complementary extended Kalman filter (EKF), throughout its 15-element error state vector, compensates the position, velocity and attitude errors of the INS solution, as well as IMU biases. This methodology is valid for any kind of motion (forward, lateral or backward walk, at different speeds), and does not require an offline calibration for the user gait. The integrated INS+RFID methodology eliminates the typical drift of IMU-alone solutions (approximately 1% of the total traveled distance), resulting in typical positioning errors along the walking path (no matter its length) of approximately 1.5 m.
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paper_title: On the fusion of inertial data for signal strength localization

paper_content:

Localization in wireless sensor networks (WSN) based on the evaluation of received signal strength (RSS) values of communication packets has received a considerable amount of research interest in the last years. In spite of the number of approaches that have been presented, the performance of most systems remains rather poor due to the unpredictable behavior of RSS values and the resulting fluctuating position estimations. In this paper, an approach to cope with these fluctuations for the purpose of ad-hoc person localization in WSN is presented. A hip-mounted inertial measurement unit (IMU), carried by the person to localize, is used to collect data on the current movement. This data is used to stabilize the RSS position estimation by means of a Kalman filter. The experimental evaluation shows that the proposed method carries the potential to improve RSS localization methods. We present results of experiments in in- and outdoor environments and quantify the improvements that can be achieved by fusing inertial data with RSS position estimation schemes for person localization. It can be concluded that fusing inertial data can improve the performance of RSS-based localization systems.
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paper_title: Enhanced Inertial-Aided Indoor Tracking System for Wireless Sensor Networks: A Review

paper_content:

In recent years, there has been a growing interest in localization algorithms for indoor environments. In this paper, we have developed an enhanced filtering method for indoor positioning and tracking applications using a wireless sensor network. The method combines position, speed, and heading measurements with the aim of achieving more accurate position estimates both in the short and the long term. Using as a base, the well-known extended Kalman filter, we have incorporated two novel measurement covariance matrix tuning methods. The power threshold covariance matrix tuning method and the distance statistics covariance matrix tuning method, both based on the statistical characteristics of the distance estimations. In addition, we take into account the inertial measurements obtained from a nine-degrees of freedom inertial measurement unit. The system has been validated in real scenarios and results show that it provides long-term accuracy, that is, the accuracy remains below 1 m during a 20-min test. In summary, our methods benefit from the reduced observation error of the inertial sensors in the short term and extend it over a long period of time.
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paper_title: Fusion of RSS and inertial measurements for calibration-free indoor pedestrian tracking

paper_content:

RSS-based localization techniques are widely used in indoor environments to estimate the position of people or objects. Recent work proposes to combine this localization technique with a dead reckoning strategy (using inertial sensors on the person or object to be tracked) to improve the accuracy of the localization results and, at the same time, track the position of the user continuously. The RSS-based subsystem of most of the proposed hybrid systems rely on a time consuming calibration phase to build a radio map of the environment, which is used to find a correspondence between the RSS measurements and the position. However, due to the changing environmental dynamics, the behavior of the channel may change after some time, thus, recalibration processes are necessary to maintain the positioning accuracy. In order to avoid these calibration and re-calibration needs, this paper proposes to use a nearly calibration-free RSS channel modeling localization approach and fuse it with the inertial measurements through a Kalman filter. The proposed strategy is tested through numerical simulation and experimental tests, showing accuracy gains with respect to other strategies. Furthermore, the proposed method has a low deployment cost, as very little calibration is needed prior to the operation.
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paper_title: Development and Evaluation of a Combined WLAN & Inertial Indoor Pedestrian Positioning System

paper_content:

In this paper we have presented an indoor positioning system for pedestrians combining Wireless LAN fingerprinting with foot mounted inertial and magnetometer sensors. To achieve a system capable of real-time processing we have employed a hierarchical Bayesian filtering approach using cascaded extended Kalman filters. The accuracy of the combined system was quantitatively evaluated in a real building against ground-truth. Our results show that accuracy is much higher than using Wireless LAN fingerprinting alone; in our experiment we achieved a positioning error (standard deviation) of roughly 1.6 meters. The approach requires only minimal fingerprinting effort since the high accuracy is achieved by the support of the inertial-based step estimation in the overall estimation process.
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paper_title: WLAN-Based Pedestrian Tracking Using Particle Filters and Low-Cost MEMS Sensors

paper_content:

Indoor positioning systems based on wireless LAN (WLAN) are being widely investigated in academia and industry. Meanwhile, the emerging low-cost MEMS sensors can also be used as another independent positioning source. In this paper, we propose a pedestrian tracking framework based on particle filters, which extends the typical WLAN-based indoor positioning systems by integrating low-cost MEMS accelerometer and map information. Our simulation and real world experiments indicate a remarkable performance improvement by using this fusion framework.
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paper_title: Pedestrian localisation for indoor environments

paper_content:

Location information is an important source of context for ubiquitous computing systems. This paper looks at how a foot-mounted inertial unit, a detailed building model, and a particle filter can be combined to provide absolute positioning, despite the presence of drift in the inertial unit and without knowledge of the user's initial location. We show how to handle multiple floors and stairways, how to handle symmetry in the environment, and how to initialise the localisation algorithm using WiFi signal strength to reduce initial complexity. We evaluate the entire system experimentally, using an independent tracking system for ground truth. Our results show that we can track a user throughout a 8725 m2 building spanning three floors to within 0.5m 75% of the time, and to within 0.73 m 95% of the time.
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paper_title: A Wireless Sensor Network for Real-Time Indoor Localisation and Motion Monitoring

paper_content:

This paper describes the development and deployment of a wireless sensor network for monitoring human motion and position in an indoor environment. Mobile sensor nodes comprising mote-type devices, along with inertial sensors are worn by persons moving inside buildings. Motion data is preprocessed onboard mobile nodes and transferred to a static network of seed nodes using a delay tolerant protocol with minimal radio packet overhead. A Monte Carlo based localisation algorithm is implemented, which uses a person's pedometry data, indoor map information and seed node positions to provide accurate, real-time indoor location information. The performance of the network protocols and localisation algorithm are evaluated using simulated and real experimental data.
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paper_title: Advanced Integration of WiFi and Inertial Navigation Systems for Indoor Mobile Positioning

paper_content:

This paper presents an aided dead-reckoning navigation structure and signal processing algorithms for self localization of an autonomous mobile device by fusing pedestrian dead reckoning and WiFi signal strength measurements. WiFi and inertial navigation systems (INS) are used for positioning and attitude determination in a wide range of applications. Over the last few years, a number of low-cost inertial sensors have become available. Although they exhibit large errors, WiFi measurements can be used to correct the drift weakening the navigation based on this technology. On the other hand, INS sensors can interact with the WiFi positioning system as they provide high-accuracy real-time navigation. A structure based on a Kalman filter and a particle filter is proposed. It fuses the heterogeneous information coming from those two independent technologies. Finally, the benefits of the proposed architecture are evaluated and compared with the pure WiFi and INS positioning systems.
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paper_title: A modularized real-time indoor navigation algorithm on smartphones

paper_content:

This paper outlines an indoor navigation algorithm that uses multiple kinds of sensors and technologies, such as 9-axis sensors (i.e., 3D gyros, accelerometers, and magnetometers), WiFi, and magnetic matching. The corresponding real-time software on smartphones includes modules such attitude determination and gyro bias estimation, pedestrian dead-reckoning (PDR), WiFi positioning, and magnetic matching. The heading from the attitude-determination module is fed into the PDR-based position-tracking module. Then, PDR is used for providing continuous position solutions and for the blunder detection of both WiFi fingerprinting and magnetic matching. Meanwhile, WiFi fingerprinting utilizes a point-by-point matching technology, while magnetic matching is based on profile-matching. Finally, WiFi and magnetic matching results are passed into the position-tracking module as updates. This algorithm was tested with two smartphones in two indoor environments. The results indicates the proposed navigation algorithm provided better navigation results than those of PDR, WiFi, or magnetic matching by itself, and better than the results of PDR/WiFi and PDR/magnetic matching (MM) in challenging indoor environment. The proposed using off-the-shelf sensors available in consumer portable devices and existing WiFi infrastructures, and have been realized on smartphones.
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paper_title: Smartphone Inertial Sensor-Based Indoor Localization and Tracking With iBeacon Corrections

paper_content:

The Global Positioning System (GPS) can be readily used for outdoor localization, but GPS signals are degraded in indoor environments. How to develop a robust and accurate indoor localization system is an emergent task. In this paper, we propose a smartphone inertial sensor-based indoor localization and tracking system with occasional iBeacon corrections. Some important issues in a smartphone-based pedestrian dead reckoning (PDR) approach, i.e., step detection, walking direction estimation, and initial point estimation, are studied. One problem of the PDR approach is the drift with walking distance. We apply a recent technology, iBeacon, to occasionally calibrate the drift of the PDR approach. By analyzing iBeacon measurements, we define an efficient calibration range where an extended Kalman filter is utilized. The proposed localization and tracking system can be implemented in resource-limited smartphones. To evaluate the performance of the proposed approach, real experiments under two different environments have been conducted. The experimental results demonstrated the effectiveness of the proposed approach. We also tested the localization accuracy with respect to the number of iBeacons.
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paper_title: Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization

paper_content:

Location-based services (LBS) have attracted a great deal of attention recently. Outdoor localization can be solved by the GPS technique, but how to accurately and efficiently localize pedestrians in indoor environments is still a challenging problem. Recent techniques based on WiFi or pedestrian dead reckoning (PDR) have several limiting problems, such as the variation of WiFi signals and the drift of PDR. An auxiliary tool for indoor localization is landmarks, which can be easily identified based on specific sensor patterns in the environment, and this will be exploited in our proposed approach. In this work, we propose a sensor fusion framework for combining WiFi, PDR and landmarks. Since the whole system is running on a smartphone, which is resource limited, we formulate the sensor fusion problem in a linear perspective, then a Kalman filter is applied instead of a particle filter, which is widely used in the literature. Furthermore, novel techniques to enhance the accuracy of individual approaches are adopted. In the experiments, an Android app is developed for real-time indoor localization and navigation. A comparison has been made between our proposed approach and individual approaches. The results show significant improvement using our proposed framework. Our proposed system can provide an average localization accuracy of 1 m.
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paper_title: Advanced Pedestrian Positioning System to Smartphones and Smartwatches

paper_content:

In recent years, there has been an increasing interest in the development of pedestrian navigation systems for satellite-denied scenarios. The popularization of smartphones and smartwatches is an interesting opportunity for reducing the infrastructure cost of the positioning systems. Nowadays, smartphones include inertial sensors that can be used in pedestrian dead-reckoning (PDR) algorithms for the estimation of the user’s position. Both smartphones and smartwatches include WiFi capabilities allowing the computation of the received signal strength (RSS). We develop a new method for the combination of RSS measurements from two different receivers using a Gaussian mixture model. We also analyze the implication of using a WiFi network designed for communication purposes in an indoor positioning system when the designer cannot control the network configuration. In this work, we design a hybrid positioning system that combines inertial measurements, from low-cost inertial sensors embedded in a smartphone, with RSS measurements through an extended Kalman filter. The system has been validated in a real scenario, and results show that our system improves the positioning accuracy of the PDR system thanks to the use of two WiFi receivers. The designed system obtains an accuracy up to 1.4 m in a scenario of 6000 m 2 .
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paper_title: Accurate indoor localization and tracking using mobile phone inertial sensors, WiFi and iBeacon

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In this paper, we propose a robust and accurate indoor localization and tracking system using smartphone built-in inertial measurement unit (IMU) sensors, WiFi received signal strength measurements and opportunistic iBeacon corrections based on particle filter. We utilize Pedestrian Dead Reckoning (PDR) approach which leverages smartphone equipped accelerometers, gyroscope and magnetometer to estimate the walking distance and direction of user. The position estimated by WiFi fingerprinting based approach is fused with PDR to reduce its drifting error. Since the number of WiFi routers is usually limited for localization in large-scale indoor environment, we employ the emerging iBeacon technology to occasionally correct the drifting error of PDR in poor WiFi coverage area. Extensive experiments have been conducted and verified the superiority of the proposed system in terms of localization accuracy and robustness.
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paper_title: Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning

paper_content:

The paper presents an indoor navigation solution by combining physical motion recognition with wireless positioning. Twenty-seven simple features are extracted from the built-in accelerometers and magnetometers in a smartphone. Eight common motion states used during indoor navigation are detected by a Least Square-Support Vector Machines (LS-SVM) classification algorithm, e.g., static, standing with hand swinging, normal walking while holding the phone in hand, normal walking with hand swinging, fast walking, U-turning, going up stairs, and going down stairs. The results indicate that the motion states are recognized with an accuracy of up to 95.53% for the test cases employed in this study. A motion recognition assisted wireless positioning approach is applied to determine the position of a mobile user. Field tests show a 1.22 m mean error in “Static Tests” and a 3.53 m in “Stop-Go Tests”.
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paper_title: A Hybrid Smartphone Indoor Positioning Solution for Mobile LBS

paper_content:

Smartphone positioning is an enabling technology used to create new business in the navigation and mobile location-based services (LBS) industries. This paper presents a smartphone indoor positioning engine named HIPE that can be easily integrated with mobile LBS. HIPE is a hybrid solution that fuses measurements of smartphone sensors with wireless signals. The smartphone sensors are used to measure the user’s motion dynamics information (MDI), which represent the spatial correlation of various locations. Two algorithms based on hidden Markov model (HMM) problems, the grid-based filter and the Viterbi algorithm, are used in this paper as the central processor for data fusion to resolve the position estimates, and these algorithms are applicable for different applications, e.g., real-time navigation and location tracking, respectively. HIPE is more widely applicable for various motion scenarios than solutions proposed in previous studies because it uses no deterministic motion models, which have been commonly used in previous works. The experimental results showed that HIPE can provide adequate positioning accuracy and robustness for different scenarios of MDI combinations. HIPE is a cost-efficient solution, and it can work flexibly with different smartphone platforms, which may have different types of sensors available for the measurement of MDI data. The reliability of the positioning solution was found to increase with increasing precision of the MDI data.
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paper_title: Smartphone-based indoor positioning utilizing motion recognition

paper_content:

More than half of the population in Western Europe and North America own a smartphone, providing a large market for both indoor and outdoor location-based services. In order to gain a ubiquitous solution for a smartphone-based indoor positioning, motion recognition may be utilized. Motion recognition can be used to adapt relative positioning solutions as well as the position filtering process. The presented motion recognition is based on classic machine learning techniques, filtered within the time and motion domain to gain a more robust estimation. The outcome of the motion recognition is used within a Pedestrian Dead Reckoning (PDR) algorithm as well as in a particle filter, but is especially helpful within the step detection process of the PDR. Within the step length estimation of PDR, the step length is strongly overestimated when walking on stairs. Contrary, when walking fast, the step length is underestimated by standard step length models. This estimation can be improved using motion recognition.
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Title: A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications

Section 1: Introduction
Description 1: Introduce the problem of indoor positioning in contrast to outdoor positioning, discuss the need for a standardized Indoor Positioning System (IPS), and provide an overview of the paper's objectives.

Section 2: Indoor Positioning Systems
Description 2: Classify different IPS methods and provide a general description of each group: network-based systems, inertial-based systems, and hybrid systems.

Section 3: Network Based Systems
Description 3: Review the different wireless networks that can be used for IPS, and detail the range-based and range-free methods within this category.

Section 4: Range Based
Description 4: Explain the fundamentals and differences of time-based, angle-based, and RSS-based localization methods, including their advantages and disadvantages.

Section 5: Range Free
Description 5: Describe the proximity methods and fingerprinting methods including their working principles and performance concerns.

Section 6: Inertial Based Systems
Description 6: Discuss the principles and types of inertial-based systems, specifically strapdown systems and step-and-heading systems, along with their challenges related to drift.

Section 7: Simultaneous Localization and Mapping
Description 7: Explain SLAM's application to indoor environments, its methodology, and the challenges of computational complexity and scalability.

Section 8: Hybrid Positioning Systems
Description 8: Discuss the concept of hybrid systems, focusing on the most promising ones for mass market applications, breaking them into RSS-IMU hybrid systems, map hybrid systems, and smartphone hybrid systems.

Section 9: RSS-IMU Hybrid Systems
Description 9: Provide examples and explain how the combination of RSS with inertial measurements can improve positioning accuracy and its implementation details.

Section 10: Map Hybrid Systems
Description 10: Detail how map information can be used to enhance IPS, usually within particle filter structures to avoid impossible locations.

Section 11: Smartphone Hybrid Systems
Description 11: Describe the importance of smartphone-based IPS, the technologies involved, and examples of hybrid systems specifically designed for smartphones.

Section 12: Conclusions
Description 12: Summarize the survey's findings, emphasizing the advantages of RSS-IMU hybrid systems for mass-market applications, and suggest future research directions focusing on smartphone adaptability, heterogeneity of transceivers, battery life, and potential of smartwatches.