A Nonuniform Clutter Intensity Estimation Algorithm for Random Finite Set Filters

In many cases, target tracking is subjected to a dense, nonuniform, and time-varying clutter background. This will seriously deteriorate the tracking performance under an unknown clutter environment. In this paper, multitarget tracking under the unknown environment is considered. First, the finite mixture distribution is used to fit the unknown clutter distributions and then Gibbs sampling and Bayesian information criterion are adopted to evaluate and estimate the clutter parameters. Besides, the unknown detection profile and clutter rate are also considered. All these issues are solved in the random finite set framework because it is a principled and top-down approach. Finally, several experiments are provided to verify the proposed algorithm.

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