基于互信息熵-改进PHD协同的非合作双基地雷达目标跟踪OA

This study proposes a processing framework based on Mutual Information Entropy(MIE)and an improved probability hypothesis density filter to address the key challenges—high clutter density and low detection probability—in Passive Bistatic Radar(PBR)target tracking.First,statistical differences in the correlation between target and clutter points,as well as between reference models,are quantified as mutual information entropy values,which are then used to eliminate clutter points.Second,the classical probability hypothesis density filter is improved through dynamic weight compensation,mitigating particle weight degeneration and reducing the deletion of false targets.This approach effectively resolves issues such as track fragmentation and target loss caused by discontinuous measurements with random intervals under low detection probability.The effectiveness of the proposed framework was verified through simulation experiments,and field test data demonstrated that the proposed method achieves good target-tracking performance in practical applications.