Average Weight Optimization RBPF Method for Target Tracking in Multi-Sensor Observations

The reasonable design of particle filter framework in multi-sensor observation system is the key to expand the application domain of sampling nonlinear filters. Aiming at the effective realization of particle filter for multi-sensor target tracking problem, a novel average weight optimization Rao-Bl...

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Bibliographic Details
Published in:电子学报：英文版 2013-04, Vol.22 (2), p.401-404
Main Author: LIU Xianxing HU Zhentao LI Jie
Format: Article
Language:English
Subjects:
优化策略
多传感器
平均重量
目标跟踪
粒子滤波
观测噪声
重量优化
非线性滤波器
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Summary:The reasonable design of particle filter framework in multi-sensor observation system is the key to expand the application domain of sampling nonlinear filters. Aiming at the effective realization of particle filter for multi-sensor target tracking problem, a novel average weight optimization Rao-Blackwellised particle filtering al- gorithm is proposed. Combining with the kinetic equation of target state evolution, RBPF is used as the basic es- timator of algorithm realization. For the rational utiliza- tion from multi-sensor observations and the reduction of the adverse influence from random observations noise in measuring process of particles weight, the average weight optimization strategy is used to improve the reliability and stability of particle weight variance. In addition, we give the concrete flow of RBPF in average weight optimization strategy. Finally, the theoretical analysis and experimental results show the feasibility and eifficiency of the proposed algorithm.
ISSN:1022-4653