拟蒙特卡罗粒子滤波改进算法及其在雷达目标跟踪中的应用

doi:10.3969/j.issn.0255-8297.2012.06.008

Abstract

Abstract: 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.

Key words: particle filter, quasi-Monte-Carlo, neural network, target tracking, glint noise

CLC Number:

TP391

CHEN Zhi-min, BO Yu-ming, WU Pan-long, LIU Zheng-fan. Improved Quasi-Monte-Carlo Particle Filtering and Its Application to Radar Target Tracking[J]. Journal of Applied Sciences, 2012, 30(6): 607-612.

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Improved Quasi-Monte-Carlo Particle Filtering and Its Application to Radar Target Tracking

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