Improved quasi-Monte-Carlo particle filtering and its application to radar target tracking

被引：0
|
作者：
Chen, Zhi-Min [1 ]
Bo, Yu-Ming [1 ]
Wu, Pan-Long [1 ]
Liu, Zheng-Fan [1 ]
机构：
[1] School of Automation, Nanjing University of Science and Technology
来源：
Yingyong Kexue Xuebao/Journal of Applied Sciences | 2012年 / 30卷 / 06期
关键词：
Glint noise; Neural network; Particle filter; Quasi-Monte-Carlo; Target tracking;
D O I：
10.3969/j.issn.0255-8297.2012.06.008
中图分类号：
学科分类号：
摘要：
To address the difficulties in meeting the needs of precise and real-time radar maneuvering target tracking due to low precision and high computation complexity of quasi-Monte-Carlo particle filter (QMCPF), a new quasi-Monte-Carlo particle filter algorithm base on BP neural network (NQMC-PF) is proposed. Through QMC fission sampling, this algorithm generates low-discrepancy progeny particles to replace the low-weight particles to guarantee validity and diversity of the samples. Meanwhile, the algorithm uses BP neural network to calculate the weight of offspring of particles. With different models, the algorithm is tested. Experimental results show that, compared to QMC-PF, the proposed algorithm can enhance precision and increase calculation speed, and thus is applicable to radar for tracking maneuvering targets.
引用
收藏
页码：607 / 612
页数：5
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