A Two-Filter Integration of MEMS Sensors and WiFi Fingerprinting for Indoor Positioning

摘要

Indoor positioning has become increasingly important in the past decade. Some approaches for the integration of micro-electro-mechanical systems (MEMS) sensors and WiFi fingerprinting (FP) have been proposed for indoor positioning. However, most of the existing integration approaches only focus on aiding MEMS sensors by WiFi FP. This letter proposes a two-filter integration for MEMS sensors and WiFi FP. In the proposed approach, the integrated positioning solution is used to constrain the search space of WiFi FP, and achieve a constrained constrained FP (CFP) solution. Then, a Kalman filter serves for obtaining a smoothed CFP solution (SCFP). Finally, an extended Kalman filter serves for the integration of SCFP and MEMS sensors. Field tests show the proposed integration approach can improve both positioning accuracy and computational efficiency.