A Reliability-Augmented Particle Filter for Magnetic Fingerprinting Based Indoor Localization on Smartphone

Using magnetic field data as fingerprints for smartphone indoor positioning has become popular in recent years. Particle filter is often used to improve accuracy. However, most of existing particle filter based approaches either are heavily affected by motion estimation errors, which result in unreliable systems, or impose strong restrictions on smartphone such as fixed phone orientation, which are not practical for real-life use. In this paper, we present a novel indoor positioning system for smartphones, which is built on our proposed reliability-augmented particle filter. We create several innovations on the motion model, the measurement model, and the resampling model to enhance the basic particle filter. To minimize errors in motion estimation and improve the robustness of the basic particle filter, we propose a dynamic step length estimation algorithm and a heuristic particle resampling algorithm. We use a hybrid measurement model, combining a new magnetic fingerprinting model and the existing magnitude fingerprinting model, to improve system performance, and importantly avoid calibrating magnetometers for different smartphones. In addition, we propose an adaptive sampling algorithm to reduce computation overhead, which in turn improves overall usability tremendously. Finally, we also analyze the "Kidnapped Robot Problem" and present a practical solution. We conduct comprehensive experimental studies, and the results show that our system achieves an accuracy of 1 similar to 2 m on average in a large building.