Target tracking based on particle filtering in passive sensor array

被引：0
|
作者：
Li, Liang-Qun [1 ]
Huang, Jing-Xiong [1 ]
Xie, Wei-Xin [1 ]
机构：
[1] ATR Key Laboratory, Shenzhen University, Shenzhen 518060, China
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2009年 / 31卷 / 04期
关键词：
Bandpass filters - Kalman filters - Passive filters - Clutter (information theory) - Monte Carlo methods;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, a new Multiple Model Rao-Blackwellized Particle Filter (MMRBPF) based algorithm is proposed for maneuvering target tracking in passive sensor array. The advantage of the proposed approach is that the Rao-Blackwellization allows the algorithm to be partitioned into target tracking and model selection sub-problems, where the target tracking can be solved by the extend Kalman filter, and the model selection by multiple model Rao-Blackwellized particle filter. The analytical relationship between target state and model is exploited to improve the efficiency and accuracy of the proposed algorithm. Finally, a nonlinear measurement model of multiple passive sensors is founded. The simulation results show that the proposed algorithm results in more accurate tracking than the IMM (Interacting Multiple Model) method.
引用
收藏
页码：844 / 847
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