Improved Gaussian Mixture Probability Hypothesis Density for Tracking Closely Spaced Targets

Open access

Abstract

Probability hypothesis density (PHD) filter is a suboptimal Bayesian multi-target filter based on random finite set. The Gaussian mixture PHD filter is an analytic solution to the PHD filter for linear Gaussian multi-target models. However, when targets move near each other, the GM-PHD filter cannot correctly estimate the number of targets and their states. To solve the problem, a novel reweighting scheme for closely spaced targets is proposed under the framework of the GM-PHD filter, which can be able to correctly redistribute the weights of closely spaced targets, and effectively improve the multiple target state estimation precision. Simulation results demonstrate that the proposed algorithm can accurately estimate the number of targets and their states, and effectively improve the performance of multi-target tracking algorithm.

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International Journal of Electronics and Telecommunications

The Journal of Committee of Electronics and Telecommunications of Polish Academy of Sciences

Journal Information


CiteScore 2016: 0.72

SCImago Journal Rank (SJR) 2016: 0.248
Source Normalized Impact per Paper (SNIP) 2016: 0.542

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