Secure Navigation using Landmark-based Localization in a GPS-denied
  Environment
Ganesh Sapkota Sanjay Madria
In modern battlefield scenarios, the reliance on GPS for navigation can be a
critical vulnerability. Adversaries often employ tactics to deny or deceive GPS
signals, necessitating alternative methods for the localization and navigation
of mobile troops. Range-free localization methods such as DV-HOP rely on
radio-based anchors and their average hop distance which suffers from accuracy
and stability in a dynamic and sparse network topology. Vision-based approaches
like SLAM and Visual Odometry use sensor fusion techniques for map generation
and pose estimation that are more sophisticated and computationally expensive.
This paper proposes a novel framework that integrates landmark-based
localization (LanBLoc) with an Extended Kalman Filter (EKF) to predict the
future state of moving entities along the battlefield. Our framework utilizes
safe trajectory information generated by the troop control center by
considering identifiable landmarks and pre-defined hazard maps. It performs
point inclusion tests on the convex hull of the trajectory segments to ensure
the safety and survivability of a moving entity and determines the next point
forward decisions. We present a simulated battlefield scenario for two
different approaches (with EKF and without EKF) that guide a moving entity
through an obstacle and hazard-free path. Using the proposed method, we
observed a percent error of 6.51% lengthwise in safe trajectory estimation with
an Average Displacement Error (ADE) of 2.97m and a Final Displacement Error
(FDE) of 3.27m. The results demonstrate that our approach not only ensures the
safety of the mobile units by keeping them within the secure trajectory but
also enhances operational effectiveness by adapting to the evolving threat
landscape.