基于FastICA的雷达信号分选研究
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摘要
现代战争中新体制雷达的大量涌现,电磁环境变得越来越复杂,对雷达信号分选提出了新的挑战。目前的雷达信号分选领域,多采用基于参数容差的传统分选方法,这些方法受参数误差的影响大,对PDW参数相似的雷达无法分选,已经无法适应复杂电磁环境。在对FastICA算法原理分析的基础上,重点研究了将它应用于PDW参数相近的雷达信号和参差脉冲列的分选,并进行了仿真。仿真结果表明,FastICA是建立在源信号统计独立基础上的处理,对信号相关性敏感,受参数误差的影响小,可以有效解决上述问题,为雷达信号分选提供了一种新的思路。
The radar signal sorting faces new challenge with the complicated electromagnetic environment and generation of advanced radars in modern warfare.At present,the traditional methods based on PDW parameter threshold are adopted widely on radar signal sorting.These traditional methods which can not adapt to the complicated electromagnetic environment are affected seriously by parameter error,and can not sort radars which have similar parameters.FastICA algorithm is ana lyzed and chosen to blind separate radar signals which have similar parameters and stagger pulse train.The simulation results show that the algorithm based on the hypothesis of blind source separation also has good recognition effect when the parameter error exists.This method can solve the problem effectively,and provide a new way for radar signal sorting.
The radar signal sorting faces new challenge with the complicated electromagnetic environment and generation of advanced radars in modern warfare.At present,the traditional methods based on PDW parameter threshold are adopted widely on radar signal sorting.These traditional methods which can not adapt to the complicated electromagnetic environment are affected seriously by parameter error,and can not sort radars which have similar parameters.FastICA algorithm is ana lyzed and chosen to blind separate radar signals which have similar parameters and stagger pulse train.The simulation results show that the algorithm based on the hypothesis of blind source separation also has good recognition effect when the parameter error exists.This method can solve the problem effectively,and provide a new way for radar signal sorting.
引文
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[3]李广彪,张剑云.基于负熵最大化FastICA算法的雷达信号分选[J].舰船电子对抗,2005,28(3):23-25.
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[6]HYVARINEN A.Fast and robust fixed-point algorithms for independent component analysis[J].IEEE Trans.on Signal Processing,1999,10(3):626-634.
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[8]张念,刘天佑,李杰.FastICA算法及其在地震信号去噪中的应用[J].计算机应用研究,2009,26(4):1432-1434.
[9]郭武,朱长仁,王润生.一种改进的FastICA算法及其应用[J].计算机应用,2008,28(4):960-962.
[10]王明祥,莫玉龙.一种基于FastICA的运动目标[J].计算机工程,2004,30(3):58-59.
[2]钟兆根,张立民,武恒州.多传感器雷达侦察信号分选新方法研究[J].传感技术学报,2008,21(6):994-996.
[3]李广彪,张剑云.基于负熵最大化FastICA算法的雷达信号分选[J].舰船电子对抗,2005,28(3):23-25.
[4]史习智.盲信号处理:理论与实践[M].上海:上海交通大学出版社,2008.
[5]COMON P.Independent component analysis:a new con-cept[J].Signal Processing,1994,36(3):287-314.
[6]HYVARINEN A.Fast and robust fixed-point algorithms for independent component analysis[J].IEEE Trans.on Signal Processing,1999,10(3):626-634.
[7]LEE T W,GIROLAMIM S.Independent component analy-sis using an extended infomax algorithm for mixed sub-Gaussian and super-Gaussian sourees[J].Neural Networks,2000,13(4/5):411-430.
[8]张念,刘天佑,李杰.FastICA算法及其在地震信号去噪中的应用[J].计算机应用研究,2009,26(4):1432-1434.
[9]郭武,朱长仁,王润生.一种改进的FastICA算法及其应用[J].计算机应用,2008,28(4):960-962.
[10]王明祥,莫玉龙.一种基于FastICA的运动目标[J].计算机工程,2004,30(3):58-59.
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