Individual aoameasurement detection algorithm for target tracking in mixed LOS/NLOS environments

In this paper, a novel individual angle-of-arrival (AOA) measurement detection method and extended Kalman filter (EKF) based tracking algorithm is proposed. The detection method is used to detect whether an individual AOA measurement is line-of-sight (LOS) or non-line-of-sight (NLOS). After the measurement detection, the selected LOS AOA measurements are then used into an dynamic EKF to track a moving target in mixed LOS/NLOS environments. Different from some traditional NLOS error detection methods, which determine the estimation result of a set of AOA measurements collected at every time step is LOS or not, the proposed method detects each AOA measurement one by one at one time step. This algorithm makes good use of LOS AOA measurements and greatly improves the tracking accuracy of the EKF in mixed LOS/NLOS environments. Simulations implemented under different NLOS percentage scenarios demonstrates the improvement of the classical EKF with the assistance of the proposed measurement detection method for AOA measurement.