A Robust Tracking Algorithm Based on Modified Generalized Probability Data Association for Wireless Sensor Network

Abstract

Wireless sensor network (WSN) is composed of many micro sensor nodes, and the localization technology is one of the most important applications of the WSN technology. At present, many positioning algorithms have high positioning accuracy in line-of-sight environment, but poor positioning accuracy in non-line-of-sight (NLOS) environment. In this article, we propose a modified generalized probability data association algorithm based on arrival of time. We divided the range measurements into <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> different groups, and each group obtained the corresponding position estimation, model probabilities, and covariance matrix of the mobile node through IMM-EKF. We used model probability and hypothesis test to perform NLOS identification for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> groups, in which the model probability provided by each group was used for the first NLOS identification, and the innovation and innovation covariance matrix were used for the second NLOS identification in the hypothesis test. Position estimation contaminated by NLOS error is discarded. The correct position estimation is weighted with the corresponding association probability. The simulation and experimental results show that the proposed algorithm can mitigate the influence of NLOS errors and achieve higher localization accuracy when compared with the existing methods.