基于微多普勒周期相关性的弹道群目标信号分离
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摘要
针对弹道群目标微多普勒信号分离问题,提出了一种基于微多普勒周期相关性的弹道群目标信号分离算法。对进动目标和旋转目标进行了微动建模,分析了其散射中心在窄带信号下的微多普勒效应,并给出了其参数化表达式。利用STFT对窄带回波进行了时频变换,获取了回波时频图,并采用奇异值分解(SVD)和高斯空间掩模方法分别对时频图进行了消噪和平化处理。再依据散射中心微多普勒瞬时频率的变化规律,采用Viterbi算法对群目标微多普勒曲线进行了提取。最后将提取出的曲线进行傅里叶变换,根据群目标微多普勒周期的相关性,实现了群目标信号的分离。仿真验证了所提方法的有效性和准确性。
Aimed at the problem that ballistic multi-target micro-Doppler signal separation,an algorithm based on micro-Doppler cycle correlation is proposed for separating the ballistic multi-aspect signal.Cone-shaped precession model and rotating target model are established. After that,targets' scattering centers are analyzed in the narrowband signal and their parametric expressions are given.Using Short-Time Fourier transformed scattering centers echo into time-frequency figure. Then,applying SVD method to eliminate time-frequency figure's noise and using Gaussian space mask method to smooth time-frequency curve. Moreover,Viterbi algorithm is used to extract multi-target micro-Doppler curves based on the variation law of micro-Doppler instantaneous frequency. Finally,the extracted curves are transformed by FFT,and multi-target signal are separated based on micro-Doppler cycle correlation. Simulations verify the effectives and accuracy of the proposed method.
Aimed at the problem that ballistic multi-target micro-Doppler signal separation,an algorithm based on micro-Doppler cycle correlation is proposed for separating the ballistic multi-aspect signal.Cone-shaped precession model and rotating target model are established. After that,targets' scattering centers are analyzed in the narrowband signal and their parametric expressions are given.Using Short-Time Fourier transformed scattering centers echo into time-frequency figure. Then,applying SVD method to eliminate time-frequency figure's noise and using Gaussian space mask method to smooth time-frequency curve. Moreover,Viterbi algorithm is used to extract multi-target micro-Doppler curves based on the variation law of micro-Doppler instantaneous frequency. Finally,the extracted curves are transformed by FFT,and multi-target signal are separated based on micro-Doppler cycle correlation. Simulations verify the effectives and accuracy of the proposed method.
引文
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[12]赵双,鲁卫红,冯存前,等.基于窄带雷达网的弹道目标三维进动特征提取[J].雷达学报,2017,6(1):98-105.
[2]冯德军,徐乐涛,艾小锋.空间复杂目标群的雷达目标识别技术[J].现代防御技术,2015,43(4):1-6.
[3]CAMP W W,MAYHAN J T,O’DONNELL R M. Widebandradar for ballistic missile defense and range doppler imagingof satellites[J]. Lincoln Laboratory Journal,2000,12(2):267-280.
[4]杨有春,童宁宁,冯存前,等.利用最强散射点信息的平动补偿与微多普勒提取[J].西安电子科技大学学报(自然科学版),2012,39(6):147-153.
[5]李靖卿,冯存前,贺思三,等.基于最近邻域的弹道多目标分辨及micro-Doppler提取[J].中国科学信息科学,2015,45(12):1640-1650.
[6]胡晓伟,童宁宁,董会旭,等.弹道中段群目标平动补偿与分离方法[J].电子与信息学报,2015,37(2):291-296.
[7]FANG H Q,CHEN L,Raghavendiran Srinivasan. Influence oftime and length size feature selections for human activity se-quences recognition[J]. ISA Transactions,2014,53(1):134-140.
[8]关永胜,左群声,刘宏伟.高噪声环境下微动多目标分辨[J].电子与信息学报,2010,32(11):2630-2635.
[9]赵炳秋,阳洪灿,吴垚.弹道中段导弹目标进动特征提取方法研究[J].电光与控制,2016,23(7):44-49.
[10]许丹,田波,冯存前,等.基于窄带雷达网的弹道目标微动特征提取[J].空军工程大学学报(自然科学版),2017,18(6):31-35.
[11]艾小锋,李永桢,赵锋,等.基于多视角一维距离像序列的进动目标特征提取[J].电子与信息学报,2011,33(12):2846-2851.
[12]赵双,鲁卫红,冯存前,等.基于窄带雷达网的弹道目标三维进动特征提取[J].雷达学报,2017,6(1):98-105.