复杂环境下雷达信号分选算法研究
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摘要
雷达信号分选是电子战领域的一项关键技术,信号分选能力决定了雷达侦察系统能否适应现代电子战环境。因此,提高侦察系统的信号分选能力,尤其是在信号密集且复杂,脉冲丢失率较大的情况下进行实时分选和识别,显得非常重要。
本文主要研究现代电子战环境中的雷达信号分选,首先介绍了雷达信号分选的研究背景与意义以及国内外研究发展与现状。然后再介绍了雷达信号分选参数及其测量精度,随后阐述了雷达信号分选的主要方法,重点研究了未知雷达信号的分选流程,包括雷达信号预分选及主分选两个部分,预分选算法采用DOA, PW, RF联合分选,采用的是动态聚类法,具体实现算法是：改进的K-均值聚类算法。最大的优点是：无须事先划分存储空间,无须事先设定类别数目,这正好符合了现代电子战中脉冲密度未知和雷达辐射源数目未知的特点。主分选是基于PRI的分选,主要研究了相关函数法,CDIF, SDIF, PRI变换法,修正的PRI变换法,平面变换法。对于相关函数法子谐波问题,CDIF和SDIF采用避免的策略,PRI变换法采用抑制的策略,对PRI变换法作了两点修正：利用可变的时间起点,利用交迭的PRI箱。使其适应对抖动PRI的估计,仿真实验证明在抖动10%,脉丢失率20%下,估计结果正确,随后进行了序列检索和参差鉴别。
在以上工作的基础上,提出了综合分选方案,方案顺序如下：已知辐射源的匹配与扣除,对剩余脉冲串聚类,对每一类进行SDIF分选,无法分选的,采用修正PRI变换,脉冲的类型鉴别,常规,抖动,还是参差。仿真实验表明,综合分选方案对参数的测量和分选非常准确。
Radar signal sorting is a key area of electronic warfare, signal sorting capacity to determine whether the radar reconnaissance system to adapt to modern electronic warfare environment. Therefore, improving the detection signal sorting system capacity, especially in the dense and complex signal, pulse the case of the larger loss rate for real-time sorting and identification, it is very important.
This paper studies the environment of modern electronic warfare radar signal sorting, first introduced the study of radar signal sorting and significance of the background and current situation of research and development at home and abroad. And then introduces the radar signal sorting parameters and measurement accuracy, and then describes the main radar signal sorting method and sorting system composition. Focus on the unknown radar signal sorting process, including pre-sorting and radar signal separation of two main parts, pre-sorting algorithm of DOA, PW, RF combined sorting, using a dynamic clustering method, the specific algorithm is: Improved K-means clustering algorithm. Biggest advantages are:no prior division of storage space, not the number of pre-set categories, which fits well with the modern electronic warfare and radar emitter pulse density unknown number of unknown characteristics. Sorting is based on the PRI primary sorting, correlation function are studied, CDIF, SDIF, PRI transform, modified PRI transform, plane transformation. Another way for the correlation function of harmonic problems, CDIF and SDIF strategy used to avoid, PRI transform using suppression strategy, made two o'clock on the PRI transform Fixed:use of variable time point, the use of overlap in the PRI case. To adapt the estimated PRI jitter, simulation results show that 10% of the jitter, pulse rate of 20% loss, the estimated results are basically correct. Subsequent identification of sequence retrieval and varied.
Based on the work in the above proposed program of integrated sorting, program in the following order:the matching and less known radiation sources, the remaining burst cluster, the SDIF for each type of separation, not separation, and the modified PRI transform, the type of pulse identification, conventional, jitter, or mixed. Simulation results show that the comprehensive program of separation and sorting of the parameters in the basic accuracy.
本文主要研究现代电子战环境中的雷达信号分选,首先介绍了雷达信号分选的研究背景与意义以及国内外研究发展与现状。然后再介绍了雷达信号分选参数及其测量精度,随后阐述了雷达信号分选的主要方法,重点研究了未知雷达信号的分选流程,包括雷达信号预分选及主分选两个部分,预分选算法采用DOA, PW, RF联合分选,采用的是动态聚类法,具体实现算法是：改进的K-均值聚类算法。最大的优点是：无须事先划分存储空间,无须事先设定类别数目,这正好符合了现代电子战中脉冲密度未知和雷达辐射源数目未知的特点。主分选是基于PRI的分选,主要研究了相关函数法,CDIF, SDIF, PRI变换法,修正的PRI变换法,平面变换法。对于相关函数法子谐波问题,CDIF和SDIF采用避免的策略,PRI变换法采用抑制的策略,对PRI变换法作了两点修正：利用可变的时间起点,利用交迭的PRI箱。使其适应对抖动PRI的估计,仿真实验证明在抖动10%,脉丢失率20%下,估计结果正确,随后进行了序列检索和参差鉴别。
在以上工作的基础上,提出了综合分选方案,方案顺序如下：已知辐射源的匹配与扣除,对剩余脉冲串聚类,对每一类进行SDIF分选,无法分选的,采用修正PRI变换,脉冲的类型鉴别,常规,抖动,还是参差。仿真实验表明,综合分选方案对参数的测量和分选非常准确。
Radar signal sorting is a key area of electronic warfare, signal sorting capacity to determine whether the radar reconnaissance system to adapt to modern electronic warfare environment. Therefore, improving the detection signal sorting system capacity, especially in the dense and complex signal, pulse the case of the larger loss rate for real-time sorting and identification, it is very important.
This paper studies the environment of modern electronic warfare radar signal sorting, first introduced the study of radar signal sorting and significance of the background and current situation of research and development at home and abroad. And then introduces the radar signal sorting parameters and measurement accuracy, and then describes the main radar signal sorting method and sorting system composition. Focus on the unknown radar signal sorting process, including pre-sorting and radar signal separation of two main parts, pre-sorting algorithm of DOA, PW, RF combined sorting, using a dynamic clustering method, the specific algorithm is: Improved K-means clustering algorithm. Biggest advantages are:no prior division of storage space, not the number of pre-set categories, which fits well with the modern electronic warfare and radar emitter pulse density unknown number of unknown characteristics. Sorting is based on the PRI primary sorting, correlation function are studied, CDIF, SDIF, PRI transform, modified PRI transform, plane transformation. Another way for the correlation function of harmonic problems, CDIF and SDIF strategy used to avoid, PRI transform using suppression strategy, made two o'clock on the PRI transform Fixed:use of variable time point, the use of overlap in the PRI case. To adapt the estimated PRI jitter, simulation results show that 10% of the jitter, pulse rate of 20% loss, the estimated results are basically correct. Subsequent identification of sequence retrieval and varied.
Based on the work in the above proposed program of integrated sorting, program in the following order:the matching and less known radiation sources, the remaining burst cluster, the SDIF for each type of separation, not separation, and the modified PRI transform, the type of pulse identification, conventional, jitter, or mixed. Simulation results show that the comprehensive program of separation and sorting of the parameters in the basic accuracy.
引文
[1]赵国庆.雷达对抗原理.西安电子科技大学出版社,1999：1-35
[2]张永顺,童宁宁,赵国庆.雷达电子战原理.国防工业出版社,2006：1-20
[3]刁鸣.雷达对抗技术.哈尔滨工程大学出版社,2005：1-19
[4]国强.雷达信号分选理论研究.科学出版社,2010：4
[5]何明浩.雷达对抗信息处理.清华大学出版社,2010：34
[6]J.W.Campbell,Saperstein,S.Signal Recognition in Complex Radar Environments.Watkins-Johnson Tech Notes,1976,3(6)
[7]Davies, C L.Holland P.Automatic Processing for ESM.IEEproc.F, Commun.Proc.F, Commun.Radar&Signal Process,1982,129(3):164-171
[8]Mardia H K.Adaptive Clustering for ESM.IEE colloquium on Signal Processing for ESM systems,1988,62:5/1-5/4
[9]Mardia H K.Digital Signal Processing for Radar Recognition in Dense Radar Environments.PhD thesis,Electrical Eng.Dept., Leeds University,1988:32-48,108-115
[10]Rogers J A V. ESM Processor for High Pulse Density Radar Environments IEE Proc.F,Commun Radar & Signal Process.,1985,132(7):621-625
[11]Mardia H K.New Techniques for the Deinter leaving of Repetive Sequences.IEE Proc.F,Radar & Siganl Process.,1989,136(4):149-154
[12]Milojevic D J,Popovic B M.Improved Algorithm for the Deinterleaving of Radar Pulses,IEE Proc.F,Radar & Signal Process.,1992,139(1):98-101
[13]I.N.Ken,K.Massaak.Improved Algorithm for Estimating Pulse Repetition Intervals.IEEE,Tran.on Aerospace and Electronic Systems,2000(2):409-421
[14]赵建仁,龙德浩,熊平.密集信号分选的平面变换技术.电子学报,1998,26(1)：77-82
[15]Chandra V, BajPai R C. ESM Data Processing Parametric Deinterleaving Approach [C].Technology Enabling Tomorrow:Computers, Communiations and Automation towards the 21st Century.1992 IEEE Region 10 International Conference(TENCON'92),11-13 Nov.1992, vol.1:26-30
[16]Eric G,Yvon S,Pierre L. A Pattern Reordering Approach Based on Aillbiguity Detection for Online Category Learning [J].IEEE Trans.Pattern Analysis and Machine Intelligence, 2003,25(4):524-528
[17]张万军,樊甫华,谭营.聚类方法在雷达信号分选中的应用[J].雷达科学与技术,2004,2(4)：219-223
[18]许丹,姜文利,周一宇.辐射源脉冲分选的二次聚类方法[J].航天电子对抗,2004,3：26-29
[19]祝正威.雷达信号的聚类分选方法[J].电子对抗,2005,6：6-10
[20]国强,王常虹,郭立民等.分段聚类在雷达信号分选中的应用[J].北京邮电大学学报,2008,31(2)：132-136
[21]Anderson James A,Gately Michael T,Penz P Anderew,et al.Radar Signal Categorization Using a Network[J].Proceedings of the IEEE,1990,78(16)
[22]Shieh C S,Lin C T. A Vector Network for Emitter Identification[J].IEEE Trans.on Antennas and Propagation,2002,50(8):1120-1127
[23]梁百川.神经网络用于辐射源分选、识别[J].航天电子对抗,1994,(2)
[24]万建伟,宋小全,黄浦堪等.神经网络在雷达信号分选中的应用[J].系统工程与电子技术,1996,24(9)：92-94
[25]毛五星,朱元清,王建刚.支持矢量分析在雷达信号分选中的应用[J].空军雷达学院学报,2003,17(3)：19-21
[26]张葛祥,荣海娜,金炜东.支持向量机在雷达信号识别中的应用[J].西南交通大学学报,2006,(1)
[27]Del part N.Asymptotic wavelet and Gabor analysis:extraction of instantaneous frequencies[J].IEEE TranS.InformationTheory,1992,38(3):644-664
[28]Moraitakis I.Feature extraction of intrapulse modulated signals using time-frequencies analysis[J]. Proceedings of 21st century military communications conference 2000:737-741
[29]Gustavo L R,Jesus G,Alvora S O.Digital channelized reeeiver based on time-frequency analysis for signal interception[J].IEEE Trans.Aerospace and Electronic Systems,2005,41(3):879-898
[30]巫胜洪.雷达脉内特征提取方法研究[J].舰船电子对抗,2002,25(1)：25-28
[31]黄知涛,周一宇,姜文利.基于相对无模糊相位重构的自动脉内调制特性分析[J].通信学报,2003,24(4)：153-160
[32]魏跃敏,黄知涛,王丰华等.基于单脉冲相关积累的PSK信号相位编码调制规律分析[J].信号处理,2006,22(2)：281-284
[33]Zhang G X,Jin W D,Hu L Z.Application of Wavelet Packet Transform to Signal Recognition.Proc.ofICMA[C],2004:542-547
[34]张葛祥,金炜东,胡来招.基于相像系数的雷达辐射源信号特征选择[J].信号处理, 2005,24(4)：663-667
[35]Zhang G X,Jin W D,Hu L Z. Radar Emitter Signal Recognition Based on Complexity Feature[J] of Southwest Jiaotong Univ.,2004,12(2):116-122
[36]张葛祥,胡来招,金炜东.雷达辐射源信号脉内特征分析[J].红外与毫米波学报,2004,23(6)：477-480
[37]张葛祥,胡来招,金炜东.基于熵特征的雷达辐射源信号识别[J].电波科学学报,2005,20(4)：440-445
[38]张葛祥.雷达辐射源信号智能识别方法研究[D].成都：西南交通大学,2005.
[39]毕大平,董晖,姜秋喜.基于瞬时频率的脉内调制识别技术[J].电子对抗技术,2005,20(2)：6-9
[40]普运伟,金炜东,胡来招.基于瞬时频率二次特征提取的辐射源信号分类[J].西南交通大学学报,2007,3(42)：373-379
[41]普运伟.复杂体制雷达辐射源信号分选模型与算法研究[D].成都：西南交通大学,2007.
[42]朱明,金炜东,胡来招.基于原子分解的辐射源信号二次特征提取[J].西南交通大学学报,2007,Vol.42(6)：659-664.
[43]柴娟芳.复杂环境下雷达信号的分选识别技术研究[D].哈尔滨：哈尔滨工程大学,2009.
[44]陈韬伟.基于脉内特征的雷达辐射源信号分选技术研究[D].成都：西南交通大学,2010.
[45]曹阳.基于PRI的高密度脉冲信号分选算法研究[D].哈尔滨：哈尔滨工业大学,2008.
[46]田芬芳.宽带数字信道化接收机技术研究[D].西安：西安电子科技大学,2010.
[47]张蓉.数据聚类技术的研究.计算机工程与应用.2002.16：145-147
[48]高新波.模糊聚类分析及其应用[M].西安：西安电子科技大学出版社,2004.
[49]符锐.密集信号条件下的雷达信号分选[D].成都：电子科技大学,2008.
[50]雍元红.雷达信号分选算法研究[D].成都：电子科技大学,2008.
[51]何炜.雷达信号分选关键算法研究[D].成都：电子科技大学,2007.
[52]李峰.复杂环境下雷达辐射源信号分选算法研究[D].西安：西安电子科技大学,2009.
[2]张永顺,童宁宁,赵国庆.雷达电子战原理.国防工业出版社,2006：1-20
[3]刁鸣.雷达对抗技术.哈尔滨工程大学出版社,2005：1-19
[4]国强.雷达信号分选理论研究.科学出版社,2010：4
[5]何明浩.雷达对抗信息处理.清华大学出版社,2010：34
[6]J.W.Campbell,Saperstein,S.Signal Recognition in Complex Radar Environments.Watkins-Johnson Tech Notes,1976,3(6)
[7]Davies, C L.Holland P.Automatic Processing for ESM.IEEproc.F, Commun.Proc.F, Commun.Radar&Signal Process,1982,129(3):164-171
[8]Mardia H K.Adaptive Clustering for ESM.IEE colloquium on Signal Processing for ESM systems,1988,62:5/1-5/4
[9]Mardia H K.Digital Signal Processing for Radar Recognition in Dense Radar Environments.PhD thesis,Electrical Eng.Dept., Leeds University,1988:32-48,108-115
[10]Rogers J A V. ESM Processor for High Pulse Density Radar Environments IEE Proc.F,Commun Radar & Signal Process.,1985,132(7):621-625
[11]Mardia H K.New Techniques for the Deinter leaving of Repetive Sequences.IEE Proc.F,Radar & Siganl Process.,1989,136(4):149-154
[12]Milojevic D J,Popovic B M.Improved Algorithm for the Deinterleaving of Radar Pulses,IEE Proc.F,Radar & Signal Process.,1992,139(1):98-101
[13]I.N.Ken,K.Massaak.Improved Algorithm for Estimating Pulse Repetition Intervals.IEEE,Tran.on Aerospace and Electronic Systems,2000(2):409-421
[14]赵建仁,龙德浩,熊平.密集信号分选的平面变换技术.电子学报,1998,26(1)：77-82
[15]Chandra V, BajPai R C. ESM Data Processing Parametric Deinterleaving Approach [C].Technology Enabling Tomorrow:Computers, Communiations and Automation towards the 21st Century.1992 IEEE Region 10 International Conference(TENCON'92),11-13 Nov.1992, vol.1:26-30
[16]Eric G,Yvon S,Pierre L. A Pattern Reordering Approach Based on Aillbiguity Detection for Online Category Learning [J].IEEE Trans.Pattern Analysis and Machine Intelligence, 2003,25(4):524-528
[17]张万军,樊甫华,谭营.聚类方法在雷达信号分选中的应用[J].雷达科学与技术,2004,2(4)：219-223
[18]许丹,姜文利,周一宇.辐射源脉冲分选的二次聚类方法[J].航天电子对抗,2004,3：26-29
[19]祝正威.雷达信号的聚类分选方法[J].电子对抗,2005,6：6-10
[20]国强,王常虹,郭立民等.分段聚类在雷达信号分选中的应用[J].北京邮电大学学报,2008,31(2)：132-136
[21]Anderson James A,Gately Michael T,Penz P Anderew,et al.Radar Signal Categorization Using a Network[J].Proceedings of the IEEE,1990,78(16)
[22]Shieh C S,Lin C T. A Vector Network for Emitter Identification[J].IEEE Trans.on Antennas and Propagation,2002,50(8):1120-1127
[23]梁百川.神经网络用于辐射源分选、识别[J].航天电子对抗,1994,(2)
[24]万建伟,宋小全,黄浦堪等.神经网络在雷达信号分选中的应用[J].系统工程与电子技术,1996,24(9)：92-94
[25]毛五星,朱元清,王建刚.支持矢量分析在雷达信号分选中的应用[J].空军雷达学院学报,2003,17(3)：19-21
[26]张葛祥,荣海娜,金炜东.支持向量机在雷达信号识别中的应用[J].西南交通大学学报,2006,(1)
[27]Del part N.Asymptotic wavelet and Gabor analysis:extraction of instantaneous frequencies[J].IEEE TranS.InformationTheory,1992,38(3):644-664
[28]Moraitakis I.Feature extraction of intrapulse modulated signals using time-frequencies analysis[J]. Proceedings of 21st century military communications conference 2000:737-741
[29]Gustavo L R,Jesus G,Alvora S O.Digital channelized reeeiver based on time-frequency analysis for signal interception[J].IEEE Trans.Aerospace and Electronic Systems,2005,41(3):879-898
[30]巫胜洪.雷达脉内特征提取方法研究[J].舰船电子对抗,2002,25(1)：25-28
[31]黄知涛,周一宇,姜文利.基于相对无模糊相位重构的自动脉内调制特性分析[J].通信学报,2003,24(4)：153-160
[32]魏跃敏,黄知涛,王丰华等.基于单脉冲相关积累的PSK信号相位编码调制规律分析[J].信号处理,2006,22(2)：281-284
[33]Zhang G X,Jin W D,Hu L Z.Application of Wavelet Packet Transform to Signal Recognition.Proc.ofICMA[C],2004:542-547
[34]张葛祥,金炜东,胡来招.基于相像系数的雷达辐射源信号特征选择[J].信号处理, 2005,24(4)：663-667
[35]Zhang G X,Jin W D,Hu L Z. Radar Emitter Signal Recognition Based on Complexity Feature[J] of Southwest Jiaotong Univ.,2004,12(2):116-122
[36]张葛祥,胡来招,金炜东.雷达辐射源信号脉内特征分析[J].红外与毫米波学报,2004,23(6)：477-480
[37]张葛祥,胡来招,金炜东.基于熵特征的雷达辐射源信号识别[J].电波科学学报,2005,20(4)：440-445
[38]张葛祥.雷达辐射源信号智能识别方法研究[D].成都：西南交通大学,2005.
[39]毕大平,董晖,姜秋喜.基于瞬时频率的脉内调制识别技术[J].电子对抗技术,2005,20(2)：6-9
[40]普运伟,金炜东,胡来招.基于瞬时频率二次特征提取的辐射源信号分类[J].西南交通大学学报,2007,3(42)：373-379
[41]普运伟.复杂体制雷达辐射源信号分选模型与算法研究[D].成都：西南交通大学,2007.
[42]朱明,金炜东,胡来招.基于原子分解的辐射源信号二次特征提取[J].西南交通大学学报,2007,Vol.42(6)：659-664.
[43]柴娟芳.复杂环境下雷达信号的分选识别技术研究[D].哈尔滨：哈尔滨工程大学,2009.
[44]陈韬伟.基于脉内特征的雷达辐射源信号分选技术研究[D].成都：西南交通大学,2010.
[45]曹阳.基于PRI的高密度脉冲信号分选算法研究[D].哈尔滨：哈尔滨工业大学,2008.
[46]田芬芳.宽带数字信道化接收机技术研究[D].西安：西安电子科技大学,2010.
[47]张蓉.数据聚类技术的研究.计算机工程与应用.2002.16：145-147
[48]高新波.模糊聚类分析及其应用[M].西安：西安电子科技大学出版社,2004.
[49]符锐.密集信号条件下的雷达信号分选[D].成都：电子科技大学,2008.
[50]雍元红.雷达信号分选算法研究[D].成都：电子科技大学,2008.
[51]何炜.雷达信号分选关键算法研究[D].成都：电子科技大学,2007.
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