Adaptive Particle Filter with Abnormal Detection for Wearable Indoor Pedestrian Navigation

Abstract Currently, indoor location-based services (ILBSs) have increasing requirements in people’s daily life. In the meanwhile, the wearable devices are becoming more popular. In this paper, we studied a wearable system for indoor localization mainly based on INS/UWB. In order to achieve high-precision, stable, and continuous positioning, a sensor fusion method with anomaly detection is proposed. In the method, the sensor fusion method is derived from Bayesian estimation and a particle filter is developed to solve the nonlinearity problem and non-Gaussian errors for indoor positioning. In addition, the anomaly detection eliminates effects of NLoS and multipath effects significantly with the Mahalanobis distance. Two field experiments are conducted, and the results demonstrate that the 90% error of the proposed adaptive particle filter is 0.53 m, which is a 40% decrease compared with the PDR-only and UWB-only and classic PF, indicating better robustness and stability.