基于时频分析的雷达目标识别技术研究
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摘要
本论文主要针对不同微动运动的目标,开展了基于时频分析的雷达目标识别技术的研究。本文的主要工作包括：
1.研究目标的微动特性的时频图。选用三类目标圆锥、柱锥、球锥作为研究模型,通过GRECO建模软件计算出带有径向运动翻滚、旋转、进动等微动的RCS,分别研究在这几种目标运动形式下目标的时频分析图。在MATLAB软件平台上,分别对短时傅里叶变换(STFT)、Wigner-Ville变换、伪Wigner-Ville变换和小波变换等这几种时频分析方法进行了比较,验证了使用伪Wigner-ville变换具备更高的频率分辨性能,且具有更好的频移适应性。
2.提出了直接使用波形熵作为特征分类的方法对目标进行识别,而对于不同径向运动速度的目标提出了通过平移波形熵图的方法来提取稳定的特征,并分析了在不同时频分析方法不同运动状态下的目标的波形熵提取结果,验证了波形熵作为特征分类的可行性。
3.完成了对目标识别算法的性能分析。从信噪比、时频分析方法、运动形式、分类器等四个方面验证了该识别算法的性能。
In this thesis, focused on the targets in air with micro-movement, radar target recognition based on time-frequency analysis is studied. The main research work includes:
1. Using time-frequency method to analysis object with Micro-Motion is studied. In the modelling software of GRECO, three targets with simple structure, cone, combination of sphere and cone, combination of cylinder and cone are taken as research object to caculate their dynamic RCS echo signals in different micromotion ways, such as roll, rotation, precession with the radial motion. Three types of time-frequency analysis methods, including short-time Fourier transform (STFT), Wigner-Ville transform, pseudo-Wigner-Ville transform and wavelet transform, are compared on the platform of MATLAB software. It is verified that pseudo-Wigner-ville transform can have a higher frequency resolution performance and a better adjustability with frequency shift.
2. A classification method by using the waveform entropy feature is proposed. For different radial velocity of the target, the method of moving the waveform entropy diagram in the horizontal direction to extract the stable features is introduced. With the waveform entropy extraction analysis result of the target in different motion state by using different time-frequency analysis methods, waveform entropy as the feasibility of the feature classification is confirmed.
3. The analysis of target classification algorithm performance is completed. From the four aspects of signal-to-noise ratio, frequency analysis methods, form of micromotion, classifiers, the performance of classification algorithm is confirmed respectively.
1.研究目标的微动特性的时频图。选用三类目标圆锥、柱锥、球锥作为研究模型,通过GRECO建模软件计算出带有径向运动翻滚、旋转、进动等微动的RCS,分别研究在这几种目标运动形式下目标的时频分析图。在MATLAB软件平台上,分别对短时傅里叶变换(STFT)、Wigner-Ville变换、伪Wigner-Ville变换和小波变换等这几种时频分析方法进行了比较,验证了使用伪Wigner-ville变换具备更高的频率分辨性能,且具有更好的频移适应性。
2.提出了直接使用波形熵作为特征分类的方法对目标进行识别,而对于不同径向运动速度的目标提出了通过平移波形熵图的方法来提取稳定的特征,并分析了在不同时频分析方法不同运动状态下的目标的波形熵提取结果,验证了波形熵作为特征分类的可行性。
3.完成了对目标识别算法的性能分析。从信噪比、时频分析方法、运动形式、分类器等四个方面验证了该识别算法的性能。
In this thesis, focused on the targets in air with micro-movement, radar target recognition based on time-frequency analysis is studied. The main research work includes:
1. Using time-frequency method to analysis object with Micro-Motion is studied. In the modelling software of GRECO, three targets with simple structure, cone, combination of sphere and cone, combination of cylinder and cone are taken as research object to caculate their dynamic RCS echo signals in different micromotion ways, such as roll, rotation, precession with the radial motion. Three types of time-frequency analysis methods, including short-time Fourier transform (STFT), Wigner-Ville transform, pseudo-Wigner-Ville transform and wavelet transform, are compared on the platform of MATLAB software. It is verified that pseudo-Wigner-ville transform can have a higher frequency resolution performance and a better adjustability with frequency shift.
2. A classification method by using the waveform entropy feature is proposed. For different radial velocity of the target, the method of moving the waveform entropy diagram in the horizontal direction to extract the stable features is introduced. With the waveform entropy extraction analysis result of the target in different motion state by using different time-frequency analysis methods, waveform entropy as the feasibility of the feature classification is confirmed.
3. The analysis of target classification algorithm performance is completed. From the four aspects of signal-to-noise ratio, frequency analysis methods, form of micromotion, classifiers, the performance of classification algorithm is confirmed respectively.
引文
[1]周万幸.弹道导弹雷达目标识别技术[M].北京：电子工业出版社,2011
[2]姜文利.雷达目标散射信号分析与目标识别.[博士论文].北京：北京理工大学,1997
[3]杜亚娟,潘泉等.雷达目标识别方法概述[J].火控雷达技术,1998,27(4)
[4]丁建江.防空雷达目标识别技术[M].北京：国防工业出版社,2008
[5]王晓丹,王积勤.雷达目标识别技术综述[J].现代雷达,2003,5(10)：23-27
[6]姜卫东.光学区雷达结构成像的理论及雷达目标识别中的应用[D],湖南：国防科技大学,2000
[7]黄培康,殷红成,许小剑.雷达目标特性[M].北京：电子工业出版社,2005
[8]张琳,盛卫星,马晓峰.基于微多普勒效应的雷达目标识别算法[J].现代雷达,2007,29(12)：35-39
[9]潘康.基于时频分析的雷达目标识别技术研究[学位论文].南京：南京理工大学,2010
[10]杜玩文.空中雷达目标识别技术研究[学位论文].南京：南京理工大学,2008
[11]姜卫东,庄钊文,陈曾平.基于一维距离像的目标识别方法[J].现代雷达,1999,20(3)：121-126
[12]Luis B. Almeida. The Fractional Fourier Transform and Time-Frequency Representations[J]. IEEE Transactions On Signal Processing,1994,42(11):3084-3091
[13]Sergio Barbarossa, Alfonso Farina. Space-Time-Frequency Processing of Synthetic Aperture Radar Signals[J]. Ieee Transactions On Aerospace And Electronic Systems,1994, 30(2):341-356
[14]Ming-Qiu Ren, Yuan-Qing Zhu, Yan Mao, Jun Han. Radar Emmiter Signals Classification Using Kernel Principle Component Analysis And Fuzzy Support Vector Machines [J]. IEEE International Conference,2007,1442-1446
[15]GuILLERMo c. GAUNAUm, Signal Analysis by Means of Time-Frequency (Wigner-Type)Distributions-Applications to Sonar and Radar Echoes[J]. PROCEEDINGS OF THE IEEE,84(9),1996
[16]M. Dakovic'.Time-frequency-based non-stationary interference suppression for noise radar systems. [J]. IET Radar Sonar Navig,2008,2 (4):.306-314
[17]Ming-Kuei Hu, Visual Pattern Recognition By Moment Invariants, Ire Transactions On Information Theory 179-187
[18]王蕴红,刘国岁,李玺等.基于短时傅里叶变换及奇异值特征提取的目标识别方法.信号处理[J],1998,14(2)：123-127
[19]Kyung-Tae Kim, ed. Efficient Radar Target Recognition Using the MUSIC Algorithm and Invariant Features[J]. IEEE A.P,2002,50(2):111-115
[20]鲜明,庄钊文,郭桂蓉等.基于时频分析的飞机目标识别.国防科技大学学报[J],1997,19(3)：7-11
[21]Jiajin Lei. Pattern Recognition Based on Time-Frequency Distributions of Radar Micro-Doppler Dynamics [J]. IEEE International Conference,2005
[22]陈行勇,刘永祥,黎湘,郭桂蓉.雷达目标微多普勒特征提取.信号处理,2007,23(2)：222-226
[23]Victor C. Chen, Fayin Li,Shen-Shyang Ho,Harry Wechsler. Analysis of Micro-Doppler Signatures[J]. IEE Proc-Radar Sonar Navig,2003,150(4):498-502
[24]Victor C. Chen, Micro-Doppler Effect in Radar:Phenomenon, Model,and Simulation Study[J]. IEEE Transactions On Aerospace And Electronic Systems,2006,42(1):2-21
[25]V.C. Chen, F. Li, S.-S. Ho and H. Wechsler. Analysis of micro-Doppler signatures[J]. IEE Proceedings online no.2003,7(43):63-69
[26]Victor C. Chen, Fayin. Li, Shen-Shyang Ho, Happy We. Micro-Doppler Effect in Radar:Phenomenon, Model, and Simulation Study[J]. IEEE Trans on aerospace and electronic systems,2006,42(1):149-153
[27]Chen K M. Rradar wave synthesis method-a new radar detection scheme[J]. IEEE Trans.on AP,1981,29(4):553-565
[28]张志强,姚志远.时频分析及其应用[J].华东船舶工业学院学报,2002,16(4)：222-226
[29]李正周.Matlab数字信号处理与应用[M].北京：清华大学出版社,2008
[30]葛哲学,陈仲生.Matlab时频分析技术及其应用[M].北京：人民邮电出版社,2008
[31]唐向宏,李齐良.时频分析与小波变换[M].北京：科学出版社,2008孙晓兵.
[32]非平稳信号的时频分析方法及其应用[D].西安：西安电子科技大学,1996年
[33]曾勇虎,王雪松,肖顺平等.瞬态极化Wigner-Ville分布相关法及其性能分析[J].雷达科学与技术,2004,14(2)：257-261
[34]郑生华.基于时频分析的高雷达侦察信号处理技术[J].重庆大学学报,2006,29(11)：339-342
[35]邹红星,周小波.时频分析：回溯与前沿电子学报[J],2000,28(9)：78-84
[36]冯瑞林.基于时频分析的目标回波识别方法研究[D].西安：西北工业大学,2001
[37]Zediker M.S.,Rice R.R.,Hollister J.H. Method for extending range and sensitivity of a fiber optic micro-Doppler ladar system and apparatus therefor[J]. US Patent 1998, 6(8):817-847
[38]H.J.Li, S.H.Yang. Using range profiles as feature vectors to identify aerospace objects, IEEE Trans.A.P,1993,41(6):261-268
[39]Kyung-Tae Kim, ed. Efficient Radar Target Recognition Using the MUSIC Algorithm and Invariant Features[J]. IEEE A.P,2002,50(2):111-115
[40]Victor. C, Chen Hao Ling. Time Frequency for Radar Imaging and Signal Analysis[J]. Artech House,2002,3(5):369-373
[41]Chengjie Cai, Weixian Liu, Jeffrey S. Empirical Mode Decomposition of Micro-Doppler Signature[J]. Radar Conference 2005 IEEE International,2002, 9(12):895-899
[42]N.E. Huang,Z. Shen,S.R.Long et al. The Empirical Mode Decomposition and Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis[C]. Proc. R.Soc. London A, 1998,9(3):903-995
[43]Ervin Sejdi', Igor Djurovi', Jin Jiang. Time-frequency feature representation using energy concentration:An overview of recent advances.Elsevier Inc,2008
[44]Peng Lei*, JunWang*, Peng Guo, Duoduo CaiAutomatic classification of radar targets with micro-motions using entropy segmentation and time-frequency features. International Journal of Electronics and Communications (AEU),2011
[45]边肇祺,张学工.模式识别[M].北京：清华大学出版社,2000
[46]Richard O. Duda, Peter E. Hart, David G.Stork.模式分类[M].北京：机械工业出版社,2005
[47]Vladimir N. Vapnik著,张学工译.统计学习的本质[M].北京：清华大学出版社,2000
[2]姜文利.雷达目标散射信号分析与目标识别.[博士论文].北京：北京理工大学,1997
[3]杜亚娟,潘泉等.雷达目标识别方法概述[J].火控雷达技术,1998,27(4)
[4]丁建江.防空雷达目标识别技术[M].北京：国防工业出版社,2008
[5]王晓丹,王积勤.雷达目标识别技术综述[J].现代雷达,2003,5(10)：23-27
[6]姜卫东.光学区雷达结构成像的理论及雷达目标识别中的应用[D],湖南：国防科技大学,2000
[7]黄培康,殷红成,许小剑.雷达目标特性[M].北京：电子工业出版社,2005
[8]张琳,盛卫星,马晓峰.基于微多普勒效应的雷达目标识别算法[J].现代雷达,2007,29(12)：35-39
[9]潘康.基于时频分析的雷达目标识别技术研究[学位论文].南京：南京理工大学,2010
[10]杜玩文.空中雷达目标识别技术研究[学位论文].南京：南京理工大学,2008
[11]姜卫东,庄钊文,陈曾平.基于一维距离像的目标识别方法[J].现代雷达,1999,20(3)：121-126
[12]Luis B. Almeida. The Fractional Fourier Transform and Time-Frequency Representations[J]. IEEE Transactions On Signal Processing,1994,42(11):3084-3091
[13]Sergio Barbarossa, Alfonso Farina. Space-Time-Frequency Processing of Synthetic Aperture Radar Signals[J]. Ieee Transactions On Aerospace And Electronic Systems,1994, 30(2):341-356
[14]Ming-Qiu Ren, Yuan-Qing Zhu, Yan Mao, Jun Han. Radar Emmiter Signals Classification Using Kernel Principle Component Analysis And Fuzzy Support Vector Machines [J]. IEEE International Conference,2007,1442-1446
[15]GuILLERMo c. GAUNAUm, Signal Analysis by Means of Time-Frequency (Wigner-Type)Distributions-Applications to Sonar and Radar Echoes[J]. PROCEEDINGS OF THE IEEE,84(9),1996
[16]M. Dakovic'.Time-frequency-based non-stationary interference suppression for noise radar systems. [J]. IET Radar Sonar Navig,2008,2 (4):.306-314
[17]Ming-Kuei Hu, Visual Pattern Recognition By Moment Invariants, Ire Transactions On Information Theory 179-187
[18]王蕴红,刘国岁,李玺等.基于短时傅里叶变换及奇异值特征提取的目标识别方法.信号处理[J],1998,14(2)：123-127
[19]Kyung-Tae Kim, ed. Efficient Radar Target Recognition Using the MUSIC Algorithm and Invariant Features[J]. IEEE A.P,2002,50(2):111-115
[20]鲜明,庄钊文,郭桂蓉等.基于时频分析的飞机目标识别.国防科技大学学报[J],1997,19(3)：7-11
[21]Jiajin Lei. Pattern Recognition Based on Time-Frequency Distributions of Radar Micro-Doppler Dynamics [J]. IEEE International Conference,2005
[22]陈行勇,刘永祥,黎湘,郭桂蓉.雷达目标微多普勒特征提取.信号处理,2007,23(2)：222-226
[23]Victor C. Chen, Fayin Li,Shen-Shyang Ho,Harry Wechsler. Analysis of Micro-Doppler Signatures[J]. IEE Proc-Radar Sonar Navig,2003,150(4):498-502
[24]Victor C. Chen, Micro-Doppler Effect in Radar:Phenomenon, Model,and Simulation Study[J]. IEEE Transactions On Aerospace And Electronic Systems,2006,42(1):2-21
[25]V.C. Chen, F. Li, S.-S. Ho and H. Wechsler. Analysis of micro-Doppler signatures[J]. IEE Proceedings online no.2003,7(43):63-69
[26]Victor C. Chen, Fayin. Li, Shen-Shyang Ho, Happy We. Micro-Doppler Effect in Radar:Phenomenon, Model, and Simulation Study[J]. IEEE Trans on aerospace and electronic systems,2006,42(1):149-153
[27]Chen K M. Rradar wave synthesis method-a new radar detection scheme[J]. IEEE Trans.on AP,1981,29(4):553-565
[28]张志强,姚志远.时频分析及其应用[J].华东船舶工业学院学报,2002,16(4)：222-226
[29]李正周.Matlab数字信号处理与应用[M].北京：清华大学出版社,2008
[30]葛哲学,陈仲生.Matlab时频分析技术及其应用[M].北京：人民邮电出版社,2008
[31]唐向宏,李齐良.时频分析与小波变换[M].北京：科学出版社,2008孙晓兵.
[32]非平稳信号的时频分析方法及其应用[D].西安：西安电子科技大学,1996年
[33]曾勇虎,王雪松,肖顺平等.瞬态极化Wigner-Ville分布相关法及其性能分析[J].雷达科学与技术,2004,14(2)：257-261
[34]郑生华.基于时频分析的高雷达侦察信号处理技术[J].重庆大学学报,2006,29(11)：339-342
[35]邹红星,周小波.时频分析：回溯与前沿电子学报[J],2000,28(9)：78-84
[36]冯瑞林.基于时频分析的目标回波识别方法研究[D].西安：西北工业大学,2001
[37]Zediker M.S.,Rice R.R.,Hollister J.H. Method for extending range and sensitivity of a fiber optic micro-Doppler ladar system and apparatus therefor[J]. US Patent 1998, 6(8):817-847
[38]H.J.Li, S.H.Yang. Using range profiles as feature vectors to identify aerospace objects, IEEE Trans.A.P,1993,41(6):261-268
[39]Kyung-Tae Kim, ed. Efficient Radar Target Recognition Using the MUSIC Algorithm and Invariant Features[J]. IEEE A.P,2002,50(2):111-115
[40]Victor. C, Chen Hao Ling. Time Frequency for Radar Imaging and Signal Analysis[J]. Artech House,2002,3(5):369-373
[41]Chengjie Cai, Weixian Liu, Jeffrey S. Empirical Mode Decomposition of Micro-Doppler Signature[J]. Radar Conference 2005 IEEE International,2002, 9(12):895-899
[42]N.E. Huang,Z. Shen,S.R.Long et al. The Empirical Mode Decomposition and Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis[C]. Proc. R.Soc. London A, 1998,9(3):903-995
[43]Ervin Sejdi', Igor Djurovi', Jin Jiang. Time-frequency feature representation using energy concentration:An overview of recent advances.Elsevier Inc,2008
[44]Peng Lei*, JunWang*, Peng Guo, Duoduo CaiAutomatic classification of radar targets with micro-motions using entropy segmentation and time-frequency features. International Journal of Electronics and Communications (AEU),2011
[45]边肇祺,张学工.模式识别[M].北京：清华大学出版社,2000
[46]Richard O. Duda, Peter E. Hart, David G.Stork.模式分类[M].北京：机械工业出版社,2005
[47]Vladimir N. Vapnik著,张学工译.统计学习的本质[M].北京：清华大学出版社,2000