A Particle Filter Method for Pedestrian Navigation Using Foot-mounted Inertial Sensors

During GPS outages, the foot-mounted inertial-based sensors are common replacement in pedestrian navigation. The Zero velocity UPdaTe-aided Extended Kalman Filter (ZUPT-aided EKF) is often used to resolve the trajectory of a walking pedestrian with acceleration and angular rate measurements from foot-mounted sensors. However, the trajectory suffers from long-term drifts, which needs to be calibrated. This paper proposes a particle filter based approach for trajectory calibration, which exploits apriori knowledge of building structures to update particle weight. The buildings are supposed to have four domain directions, which is defined by the layout of corridors. The navigation frame is divided by eight directions, including four domain directions and four complementary directions, and the weight is assigned according to the eight directions using a Gaussian function. Finally, several real-scenario experiments are carried out, which can demonstrate that the proposed approach have better accuracy and consistency than the results without calibration or traditional methods, as the proposed approach can reach a location error of 2.7 m in a complex-trajectory walk of 861 m and the accuracy is better than 0.5%; the fact that the location error remains below 2 m in different floors also demonstrates the good consistency of the approach. As a result, the proposed approach can perform stable and continuous positioning.