Cubature Kalman probability hypothesis density filter based on multi-sensor consistency fusion① |
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基金项目: | the National Natural Science Foundation of China(61300214),the Science and Technology Innovation Team Support Plan of Education Department of Henan Province(13IRTSTHN021),the Post-doctoral Science Foundation of China(2014M551999),the Outstanding Young Cultivation Foundation of Henan University (No.0000A40366). |
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摘 要: | The GM-PHD framework as recursion realization of PHD filter is extensively applied to multi-target tracking system .A new idea of improving the estimation precision of time-varying multi-target in non-linear system is proposed due to the advantage of computation efficiency in this paper .First, a novel cubature Kalman probability hypothesis density filter is designed for single sensor measure -ment system under the Gaussian mixture framework .Second , the consistency fusion strategy for multi-sensor measurement is proposed through constructing consistency matrix .Furthermore, to take the advantage of consistency fusion strategy , fused measurement is introduced in the update step of cubature Kalman probability hypothesis density filter to replace the single-sensor measurement .Then a cubature Kalman probability hypothesis density filter based on multi-sensor consistency fusion is proposed .Capabilily of the proposed algorithm is illustrated through simulation scenario of multi-sen-sor multi-target tracking .
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关 键 词: | multi-target tracking probability hypothesis density (PHD) cubature Kalman filter consistency fusion |
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