Target Tracking in Mixed LOS/NLOS Environments Based on Individual Measurement Estimation and LOS Detection

In this paper, a new method based on estimation and line-of-sight (LOS) detection of individual time-of-arrival (TOA) measurement and Kalman filter (KF) is proposed to track a moving target in mixed line-of-sight and non-line-of-sight (LOS/NLOS) environments. In the proposed tracking algorithm, TOA measurements collected by multiple stationary sensors in a wireless sensor network are used. First, a pseudo-measured position is calculated by choosing the point along the circle defined by a given TOA measurement which has the shortest distance to the predicted position of a moving target. The pseudo-measured position is shown to be an approximately unbiased estimate of the true position of the target. Second, each pseudo-measured position calculated is passed to a detector to be identified as either LOS or NLOS. The average of all the selected LOS pseudo-measured positions is then used as a new pseudo-measurement for the KF to track the moving target. Unlike all the existing target tracking algorithms in mixed LOS/NLOS environments, the proposed tracking algorithm is able to perform target tracking even with just one LOS TOA measurement at a given time instance without prior information of the NLOS noise which may be difficult to obtain in practice. Another advantage of the proposed tracking algorithm is its computational efficiency. Simulation results show that the proposed tracking algorithm performs better than some recent tracking algorithms, particularly in severe mixed LOS/NLOS environments.