Wenzhao Chi
ABSTRACT
Indoor pedestrian localization accuracy is unsatisfactory and unreliable in a complex environment where some non-line-of-sight (NLOS) channels may persist for a long term while some other line-of-sight (LOS) channels may exist for a short period. In this paper, we propose an adaptive power-driven parallel interacting multiple model (APIMM) algorithm, which is applied into an extended Kalman filter (EKF) based ultra-wideband (UWB) localization system for indoor pedestrian tracking. We refer to this localization system as APIMM-UWB-EKF system. In the proposed APIMM-UWB-EKF system, two parallel IMMs execute simultaneously and are connected by power-driven mechanism. For each IMM, both KF-based LOS and NLOS ranging models are constructed, the observation values of which is obtained by using UWB ranging data. Furthermore, the APIMM algorithm can adaptively adjust the Markov state transition probability matrix according to the received power. Finally, an EKF is utilized to estimate the position information based on the predicated location from the ranges processed by APIMM algorithm. The experiments demonstrate that the proposed system is more accurate and robust in complex NLOS indoor environments compared with the existing schemes.
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