Abstract
In unknown clutter environment, traditional Probability Hypothesis Density (PHD) filter in multi-target tracking cannot guarantee a good performance and multitude number of particles leads to time consuming and low efficiency. Aiming at the problems, a new PHD filter tracking algorithm in unknown clutter environment based on interval analysis was proposed. Firstly, radar targets and clutter disjoint union state space modeled were established in random finite set. Next, using measurement model set up clutter model and derived to multi-target updated state function based on box particles. Additionally, the state of multi-target was recursively estimated in utilization of PHD filter box particles. Simulation reveals that the proposed algorithm is able to dramatically lower computational time with better tracking performance compared with traditional box particle filter.
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