Augmented state GM-PHD filter with registration errors for multi-target tracking by Doppler radars

Abstract

For multi-sensor multi-target tracking, traditional association-based methods treat data association and registration separately. However, they actually affect each other. The probability hypothesis density (PHD) filter has the distinct advantage that it avoids the complicated data association. In this paper, we propose an augmented state Gaussian mixture PHD (GM-PHD) filter with registration errors for multi-target tracking by Doppler radars. First, we construct the linear Gaussian dynamics and measurement model of the augmented state, which is comprised of target states and sensor biases. Then, related equations are derived when the standard GM-PHD filter is applied to the augmented state system. To effectively utilize Doppler measurements in the augmented state GM-PHD, the sequential processing method is adopted, i.e., updating target states and sensor biases with polar measurements first; and then updating sequentially target states with Doppler measurements; finally, computing weights with both polar and Doppler measurements. Simulation results show that the proposed filter is effective, and it has more robust performance in dense clutter.