Particle Probability Hypothesis Density Filter Based on Stochastic Perturbation Re-sampling
XU Cong’an① HE You① XIA Shutao① CHENG Juntu② DONG Yunlong①
①(Research Institute of Information Fusion, Naval Aeronautical and Astronautical University, Yantai 264001, China) ②(Unit. 91213 of PLA, Yantai 264000, China)
As a typical implementation of the Probability Hypothesis Density (PHD) filter, Particle PHD (P-PHD) is suitable for highly nonlinear systems and widely used in Multi-Target Tracking (MTT). However, the resampling in P-PHD filter, recommended to avoid particle degeneracy, introduces the problem of diversity loss among the particles, namely particle impoverishment problem. To solve the problem and improve the performance of the P-PHD filter, a novel filter based on stochastic perturbation re-sampling is proposed. First, a comprehensive analysis on the particle impoverishment problem of P-PHD filter is presented. Then for the purpose of keeping the particle diversity, a new stochastic perturbation re-sampling algorithm is developed, which generates new particles according to the position and duplicating times of the original particles, and removes some excessive copied particles. Finally, the re-sampling algorithm is integrated into the P-PHD filter framework and a Stochastic Perturbation Particle PHD (SPP-PHD) filter is proposed. Numerical examples show that the proposed filter can overcome the particle impoverishment problem and improve the estimation performance on the premise of not significantly improving the simulation time.
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