Abstract:The extended target probability hypothesis density (ET-PHD) filter is a promising approach for multiple extended target tracking. One crucial problem of the ET-PHD filter is partitioning the measurement set. This paper proposes a partitioning algorithm based on clustering by fast search and find density peaks (CFSFDP). Firstly, we adopt CFSFDP algorithm to partition the measurement set and the field theory is introduced to determine the cutoff distance of the CFSFDP algorithm. Then, the cluster center of the C… Show more
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