A Novel Robust Tracking Algorithm in Cluttered Environments for Distributed Sensor Network

Abstract

Tracking has attracted much attention over the past few years, particularly in the field of distributed sensor network. The most challenging issue is nonline of sight (NLOS) problem in cluttered environments such as indoor or urban areas since the presence of NLOS errors leads to severe degradation in the tracking performance. In this paper, we propose a novel robust tracking algorithm to mitigate the measurement noise and NLOS error. The robust localization method is firstly employed to estimate the positions of the mobile node with different subgroups. Then the residual test method is used to remove the larger localization error. Finally, the modified Kalman filter is introduced to improve the tracking accuracy. Simulation results show that the proposed algorithm can track the mobile node and estimate the position with relatively higher accuracy in comparison with existing methods.