采用粒子滤波和模糊聚类法的非线性多目标跟踪

doi:10.3969/j.issn.1001-2400.2010.04.010

Abstract

Abstract:

A novel method based on fuzzy clustering and independent particle filtering is proposed for nonlinear multi-target tracking. Firstly, the association of target with measurement is carried out by the use of the maximum entropy fuzzy clustering. Then the joint association probability matrix is reconstructed by utilizing the fuzzy membership degree of the target and measurement. Since particle filtering performs well in the nonlinear tracking system, this paper employs it and the joint association innovations to update each target state independently. Finally, the proposed method is applied to multi-sensor multi-target bearings-only tracking. Simulation results show that the method can obtain a higher tracking precision than JPDAF and MEF-JPDAF.

Key words: nonlinear multi-target tracking, data association, maximum entropy fuzzy clustering, independent particle filtering, bearings-only tracking

ZHANG Jun-gen;JI Hong-bing. Passive multi-target tracking based on independent particle filtering and fuzzy clustering[J].J4, 2010, 37(4): 639-641.

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Passive multi-target tracking based on independent particle filtering and fuzzy clustering

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