Abstract
The random finite set (RFS)-based Gaussian mixture probability hypothesis density (GM-PHD) filter is a promising and efficient suboptimal approximation for the multi-target Bayes filter. However, the GM-PHD filter is unable to track nearby targets caused by the improper position distribution of target-originated measurements. Aiming at the problem, a multi-target GM-PHD filter with an improved component merging method is proposed. Based on a proposed adaptive threshold-based component similarity measure scheme, the improved component merging method is able to avoid incorrect fusion of the components of targets in close proximity and optimize the target components within the target posterior intensity. Experimental results illustrate that the proposed algorithm not only can achieve better estimation accuracy in terms of the target states and its number but also has high computation efficiency when compared against the related GM-PHD-based filters.
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