Probability hypothesis density filter for phased array radar working in TAS mode

Abstract

The probability hypothesis density (PHD) filter is a multi-target tracking (MTT) method based on random finite set (RFS), providing an approximation implementation of the multi-target Bayes filter by propagating the first order moment of multi-target posterior. The PHD filter not only takes into account the accuracy of target state estimation, but also has good real-time performance, which is very suitable for radar MTT applications. In order to apply the PHD filter to scenarios with phased array radar working in track-and-search (TAS) mode, this paper proposes a time-matching recursive PHD filter. In order to handle the MTT in a TAS scenario where adjacent directions of the surveillance area are not continuously scanned, the proposed filter propagate PHD of each sector independent of others. It also introduces the time-matching filtering framework and multi-sensor multi-target recursive filtering framework to update PHD multiple times for each single sector at each time step, which can handle the MTT in scenarios where sectors have different sampling rates. The proposed method is simulated using the Gaussian mixture model, and the results, compared with those of standard PHD filter, demonstrate the effectiveness of it.