一种改进的多伯努利多目标跟踪算法

doi:10.3969/j.issn.1001-2400.2016.06.030

Abstract

Abstract:

The particle cardinality-balanced multi-target multi-bernoulli(P-CBMeMBer) filter needs large numbers of particles and has serious particles degradation. To solve this problem, we combine the square-rooted cubature Kalman filter(SCKF) with the P-CBMeMBer filter, called square-rooted cubature Kalman P-CBMeMBer(SCP-CBMeMBer) filter. The SCP-CBMeMBer filter obtains the predicted particles by sampling the importance density function generated by the SCKF in order to alleviate particles degradation. Compared to the P-CBMeMBer filter based on the unscented Kalman filter(UP-CBMeMBer), the proposed method is more stable and its performance is unrestricted by the dimension of the target states. The results show that the proposed method has a higher accuracy than the P-CBMeMBer filter and the UP-CBMeMBer filter.

Key words: multi-target tracking, cardinality-balanced multi-bernoulli filter, particle filter, importance density function, square-rooted cubature Kalman filter

WANG Haihuan;WANG Jun. Multi-target tracking with the cubature Kalman multi-bernoulli filter[J].Journal of Xidian University, 2016, 43(6): 176-182.

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Multi-target tracking with the cubature Kalman multi-bernoulli filter

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