Abstract
In this correspondence, a new multi-target tracking (MTT) algorithm based on the probability hypothesis density (PHD) filtering framework is designed in order to improve tracking performance via the proposal of two contributions. First, unlike typical existing systems, Doppler information is additionally employed to enhance the clutter rejection capability. Specifically, position and Doppler measurements are iteratively incorporated in a two-step process based on a Gaussian mixture PHD (GMPHD) filter. Second, a concrete initialization process is proposed in the birth intensity design of the GMPHD. The initialization process from consecutive measurements leads to a reliable birth intensity that improves track management performance. Both contributions are subsequently evaluated through MTT simulations, the results of which verify that the proposed algorithm is viable.
Published Version
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