Target tracking in mixed LOS/NLOS environments based on individual TOA measurement detection

Non-line-of-sight (NLOS) signal propagation is one of the most important factors affecting the accuracy of localization or tracking especially in urban or indoor environments. This paper presents a method of alleviating the influence of the NLOS signal propagation using a novel individual measurement detection (IMD) approach based on the prior probability density function (pdf) of a moving target´s state. The IMD approach can effectively identify line-of-sight (LOS) time-of-arrival (TOA) measurements from all mixed LOS/NLOS measurements. The extended Kalman Filter (EKF) algorithms is adopted to estimate the positions of the moving target using all selected LOS measurements. Simulation results show that the proposed approach outperforms the recent EKF semi-parametric modified residuals (SPMR) approach both in tracking and in computational efficiency.