Improved GM-PHD Filter Based on Multi-target Uncertainty

doi:10.12382/bgxb.2021.0693

Abstract

Abstract: Gaussian mixture probability hypothesis density (GM-PHD) filtering based on random finite set (RFS) is an effective method for solving multi-target tracking (MTT) problem. However, the GM-PHD filter in a dense clutter environment may have a poorer tracking performance due to excessive estimation errors. This is mainly because the uncertainty from the multi-target measurement is not fully considered. Every moment, the GM-PHD filter generates a new Gaussian component and a specific measured value. When clutter density is high or the detection probability is low, the accuracy of the estimated value of the target is lower. To solve this problem, this paper proposes a change in the covariance update formula through adjusting the component value with the weight of the Gaussian component considered, and introduces a label to merge the Gaussian components with an adaptive threshold, so as to improve the accuracy of the target number estimation. The proposed algorithm is proven to have a significantly higher target number estimation accuracy and filtering performance than traditional algorithms through tests under tracking scenarios with different clutter and detection probability conditions.

Key words: multi-targettracking, RFS, GM-PHDfilter, gaussianmixture, stateestimation

CLC Number:

V556
TP391

WANG Kuiwu, ZHANG Qin, HU Xiaolong. Improved GM-PHD Filter Based on Multi-target Uncertainty[J]. Acta Armamentarii, 2022, 43(12): 3113-3121.

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