Abstract：In multi-target tracking algorithms under the Bayesian filtering framework, it is usually assumed that the priori knowledge of clutter is known. However, in practice, the knowledge of clutter is usually unknown, and the assumption of clutter may not agree with the truth, resulting in the filtering precision declining. For this problem, this paper addresses the problem of Cardinalized Probability Hypothesis Density (CPHD) filter with clutter estimation. Firstly, this paper presents a new CPHD filter with clutter estimation based on Dirichlet Process Mixture Model (DPMM). Thus, this DPMM--CPHD algorithm can reduce the estimation error of the clutter spatial distribution effectively by selecting an appropriate class number. Secondly, to solve the clutter overestimation and cardinality underestimation problems, a correction idea of the sample set via CPHD filter recursion is proposed. By introducing this idea to the DPMM--CPHD algorithm, an improved DPMM--CPHD algorithm is proposed to solve this intractability of errors on clutter number and target number. Simulation results show that the proposed algorithm can effectively estimate the unknown parameters of clutter and has a good performance of multi-target tracking.
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