A Simple Geometric-Aware Indoor Positioning Interpolation Algorithm
  Based on Manifold Learning
Suorong Yang Geng Zhang Jian Zhao Furao Shen
Interpolation methodologies have been widely used within the domain of indoor
positioning systems. However, existing indoor positioning interpolation
algorithms exhibit several inherent limitations, including reliance on complex
mathematical models, limited flexibility, and relatively low precision. To
enhance the accuracy and efficiency of indoor positioning interpolation
techniques, this paper proposes a simple yet powerful geometric-aware
interpolation algorithm for indoor positioning tasks. The key to our algorithm
is to exploit the geometric attributes of the local topological manifold using
manifold learning principles. Therefore, instead of constructing complicated
mathematical models, the proposed algorithm facilitates the more precise and
efficient estimation of points grounded in the local topological manifold.
Moreover, our proposed method can be effortlessly integrated into any indoor
positioning system, thereby bolstering its adaptability. Through a systematic
array of experiments and comprehensive performance analyses conducted on both
simulated and real-world datasets, we demonstrate that the proposed algorithm
consistently outperforms the most commonly used and representative
interpolation approaches regarding interpolation accuracy and efficiency.
Furthermore, the experimental results also underscore the substantial practical
utility of our method and its potential applicability in real-time indoor
positioning scenarios.