Improved probability hypothesis density filter for multi‐target tracking of non‐cooperative bistatic radar

Abstract

Non-cooperative bistatic radar refers to the passive bistatic radar using a non-cooperative radar as the illuminator of opportunity. Limited by the non-cooperation and bistatic configuration, multi-target tracking of the non-cooperative bistatic radar is confronted with challenges of low detection probability and high clutter. Based on the sequential Monte Carlo implementation of probability hypothesis density (PHD) filter, this study proposes an improved PHD (I-PHD) filter. Similar to the tracking gate in data association, whether each candidate target is detected at each time is determined and recorded, and the sequential probability ratio test distinguishes a real target and false target based on this record. To distinguish miss detection and target death, a new survival probability dependent on target position is defined, and the survival probability of a specific target drops rapidly when it is located near the boundary and moves outwards. To label targets and extract their states, a new method of track identification and state estimation is proposed. The simulation results illustrate that the proposed I-PHD filter can effectively track multiple targets of non-cooperative bistatic radar under low detection probability and high clutter and has superior performance than competing methods.