雷达海杂波统计特性分析与仿真
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摘要
雷达系统的抗干扰性能是指它在各种噪声,杂波及干扰环境下对感兴趣的目标进行处理的能力。雷达的性能要受到杂波和噪声以及干扰的影响,所以雷达杂波干扰是雷达科技工作者和观测者十分关注的课题。雷达模拟的核心是建立雷达目标回波信号以及各种杂波信号散射、传播特性的模型,通过对杂波性质的研究,建立合适的模型对雷达的设计和分析具有重要的意义。
海面作为雷达波的反射面,性态十分复杂。海风、海流、海浪、潮汐和各异的水质等都对海杂波的产生有着不同的影响。海水大范围地运动、水体内波动、紊流以及相互作用以及雷达波在波峰和波谷间的反射,在波浪边缘的绕射等,使得雷达接收到的反射波不仅频谱形状极不规则,反射单元的概率分布函数也超出常规。而不同海情下的海杂波很难用同一个统计模型来描述。本文的目的是研究实测海杂波数据,找出适用不同海情的统计模型,并用获得的模型对不同海情的海杂波进行模拟。
文中首先介绍了海杂波幅度概率密度分布的有关模型。对IPIX雷达采集到的不同海情下海杂波数据进行分析,利用其I、Q分量的直方图与相同参数下的各种分布模型进行比较,得出低海情所适用的统计模型为K分布模型,高海情所适用的统计模型为K分布模型或是LogNormal分布模型的结论。同时也对高海情与低海情下的海杂波的功率谱进行了分析,得出不同海情下的海杂波功率谱密度符合高斯型。然后使用ZMNL算法产生了给定参数下高海情的LogNormal分布海杂波。并采用SIRP算法产生了给定参数下的高海情与低海情K分布海杂波。最后对实验结果进行了定性与定量的检验。采用作图法进行定性检验,观察知仿真数据与理论分布十分接近。采用χ2检验与K-S检验进行了定量的检验,仿真数据在一定的显著水平下通过检验。快速、准确的模拟雷达海杂波,是海杂波特性研究及雷达目标检测仿真所必需的。仿真所得的海杂波数据可以为干扰抑制以及目标检测算法研究提供理论基础和依据。
The performance capability of radar system is the processing ability to deal with the interested targets under various kinds of noise, clutter, and jam environments. The core of Radar echo simulation is modeling the scatter and transmission of radar echo signal and varied radar clutter signals. So it is very important to study the character of radar clutter, with a proper model. And it is very significant to radar design and analysis.
Sea surface, as the radar backscatter, is very complex. Sea breeze, ocean current, ocean wave, tides and different water quality has a great effect on sea clutter. In addition, seawater movements, water fluctuations inside, turbulence, the reflection of radar wave between waves, diffraction of the edge of the wave, all of these made the radar spectrum not only a very irregular shape but also the reflex unit probability distribution function beyond the conventional function. Different sea-state clutter amplitudes distribution must use different statistics model to represent, such distributions including Weibull, LogNormal, and K. The purpose of this thesis is to study the sea clutter data measured and find which amplitudes distribution models are applicable for different sea-state clutter. Then, the model was used to simulate different sea-state clutter.
First, some models of sea clutter amplitude distribution were introduced. Through the analysis of the IPIX radar sea clutter data, and by the histogram analysis of the I/Q component of the IPIX data, it showed that the low sea-state amplitudes should use K distribution to indicate while the high sea-state amplitudes could use LogNormal or K distribution to describe. At the same time, we analyzed the power spectrum density of different sea-state clutter and found that Gaussian power spectrum density model is suitable to indicate the high and low sea-state clutter. Then the processes of simulation for each clutter were presented. Zero Memory Nonlinearity (ZMNL) transformation is used to simulate the high sea-state LogNormal amplitude distribution clutter; the method of high and low sea-state K amplitude distribution clutter simulation is based on the theory of Spherically Invariant Random Process (SIRP). Finally the experimental results were performed a qualitative and quantitative testing. By using a mapping method for qualitative examination, simulation data was very close to the theoretical distribution of observation. Moreover, using the K-S andχ2 hypothesis testing for the quantitative examination, the simulation data also passed the K-S andχ2 hypothesis testing with a certain confidence level. Actually,a rapid and accurate simulation of radar clutter is necessary for studying sea clutter characteristics and target detection. Simulation of the sea clutter data provides a theoretical basis and foundation for target detection and interference suppression.
海面作为雷达波的反射面,性态十分复杂。海风、海流、海浪、潮汐和各异的水质等都对海杂波的产生有着不同的影响。海水大范围地运动、水体内波动、紊流以及相互作用以及雷达波在波峰和波谷间的反射,在波浪边缘的绕射等,使得雷达接收到的反射波不仅频谱形状极不规则,反射单元的概率分布函数也超出常规。而不同海情下的海杂波很难用同一个统计模型来描述。本文的目的是研究实测海杂波数据,找出适用不同海情的统计模型,并用获得的模型对不同海情的海杂波进行模拟。
文中首先介绍了海杂波幅度概率密度分布的有关模型。对IPIX雷达采集到的不同海情下海杂波数据进行分析,利用其I、Q分量的直方图与相同参数下的各种分布模型进行比较,得出低海情所适用的统计模型为K分布模型,高海情所适用的统计模型为K分布模型或是LogNormal分布模型的结论。同时也对高海情与低海情下的海杂波的功率谱进行了分析,得出不同海情下的海杂波功率谱密度符合高斯型。然后使用ZMNL算法产生了给定参数下高海情的LogNormal分布海杂波。并采用SIRP算法产生了给定参数下的高海情与低海情K分布海杂波。最后对实验结果进行了定性与定量的检验。采用作图法进行定性检验,观察知仿真数据与理论分布十分接近。采用χ2检验与K-S检验进行了定量的检验,仿真数据在一定的显著水平下通过检验。快速、准确的模拟雷达海杂波,是海杂波特性研究及雷达目标检测仿真所必需的。仿真所得的海杂波数据可以为干扰抑制以及目标检测算法研究提供理论基础和依据。
The performance capability of radar system is the processing ability to deal with the interested targets under various kinds of noise, clutter, and jam environments. The core of Radar echo simulation is modeling the scatter and transmission of radar echo signal and varied radar clutter signals. So it is very important to study the character of radar clutter, with a proper model. And it is very significant to radar design and analysis.
Sea surface, as the radar backscatter, is very complex. Sea breeze, ocean current, ocean wave, tides and different water quality has a great effect on sea clutter. In addition, seawater movements, water fluctuations inside, turbulence, the reflection of radar wave between waves, diffraction of the edge of the wave, all of these made the radar spectrum not only a very irregular shape but also the reflex unit probability distribution function beyond the conventional function. Different sea-state clutter amplitudes distribution must use different statistics model to represent, such distributions including Weibull, LogNormal, and K. The purpose of this thesis is to study the sea clutter data measured and find which amplitudes distribution models are applicable for different sea-state clutter. Then, the model was used to simulate different sea-state clutter.
First, some models of sea clutter amplitude distribution were introduced. Through the analysis of the IPIX radar sea clutter data, and by the histogram analysis of the I/Q component of the IPIX data, it showed that the low sea-state amplitudes should use K distribution to indicate while the high sea-state amplitudes could use LogNormal or K distribution to describe. At the same time, we analyzed the power spectrum density of different sea-state clutter and found that Gaussian power spectrum density model is suitable to indicate the high and low sea-state clutter. Then the processes of simulation for each clutter were presented. Zero Memory Nonlinearity (ZMNL) transformation is used to simulate the high sea-state LogNormal amplitude distribution clutter; the method of high and low sea-state K amplitude distribution clutter simulation is based on the theory of Spherically Invariant Random Process (SIRP). Finally the experimental results were performed a qualitative and quantitative testing. By using a mapping method for qualitative examination, simulation data was very close to the theoretical distribution of observation. Moreover, using the K-S andχ2 hypothesis testing for the quantitative examination, the simulation data also passed the K-S andχ2 hypothesis testing with a certain confidence level. Actually,a rapid and accurate simulation of radar clutter is necessary for studying sea clutter characteristics and target detection. Simulation of the sea clutter data provides a theoretical basis and foundation for target detection and interference suppression.
引文
[1]米切尔R L.雷达系统模拟.第一版,北京:科学出版社,1992,7
[2] J.B.Billingsley. Low-angle radar land clutter. William Andrew Publishing, 2002,3:256-264
[3] Fred E.Nathanson. Radar design principles. SciTech,1999,7:45-58
[4]汤明.裸地散射特性分析.电波科学学报,Vol 9,No.4,1994:69-75
[5]罗贤云,吉健康等.雷达杂波功率谱模型.电波科学学报,Vol 7,No.4,1992:35-40
[6] A.Farina, A.Russo, etc. Coherent Radar Detection In LogNormal Clutter. IEEE.Proc F, 1986,5:365-374
[7] M.W.朗.陆地和海面的雷达波散射特性.第一版,北京:科学出版社,1981,11
[8] M.Sekine, T.Musha. Weibull-distributed Sea Clutter. IEEE Prceedings-F,Voll30,No.5 1983:471-486
[9] Mark Denny. Simulation Sea Clutter Via the Compound-K Distribution. The institution of electrical engineer, IEEE.1998
[10] W.J.Szajnowski. Generation of Correlated LogNormal Clutter Samples. Electronics Letters, Vol 12, No.19,1979:497-498
[11] E.Conte, M.Longo. On a Coherent Model for LogNormal Clutter. IEEE Procs F, Vol 134, No.2, April 1987:198-201
[12] Gang.Li, Kai-Bor Yu. Modelling and simulation of Coherent Weibull Clutter. IEEE. Proces F, Vol 136, No.1, 1989:2-12
[13] L, James Marier, Jr. Correlated K-distributed Clutter Generation for Radar Detection and Track. IEEE Trans. Aerospace and Electronic Systems 1995,31(2):568-580
[14] MURALIDHAR RANGASWAMY, DONALD WEINER, AYDIN OZTURK. Computer Generation of Correlated Non-Gaussian Radar Clutter. IEEE Trans on AES, Vol 31, No.1,1995:106-115
[15] Simon Haykin, Rembrandt Bakker. Uncovering Nonlinear Dynamics: The Case Study of Sea Clutter. Proceedings of the IEEE, 2002, 90(5):860-881.
[16] BLAKE L V. Radar range-performance analysis. Lexington Book,1980:301-336
[17]何友,关键等.雷达自动检测与恒虚警处理.第一版,北京:清华大学出版社,2003:36-152
[18] Chan.H.C.“Radar Sea-Clutter At Low Grazing Angles”Radar And Signal Processing, IEE Proceeding F, 1990,137(2):102-112
[19] Walker.D.“Doppler Modeling Of Radar Sea Clutter”Radar, Sonar and Navigation, IEE Proceeding E, 2001, 148(2): 73-80.
[20] Baker.C.J.“K-distribute Coherent Sea Clutter”IEE Proceeding, 1991, 138(2): 89-92.
[21] http://soma.ece.mcmaster.ca/ipix
[22] Farina.A.Gini, F.Greco, High Resolution Sea clutter data: A statistical analysis of recorded live data. IEE Proceedings, Radar Sonar Navigation,1997,144(3):121-130
[23] E.CONTE, A.DEMAIO, C.GALDI. Statistical analysis of real clutter at different range resolutions. IEEE Trans. On AES. 2004, 40(3):903-918
[24] William H. Tranter, K. Sam Shanmugan.通信系统仿真原理与无线应用.第一版,北京:机械工业出版社,2005:152-231
[25]颜南霞,卢凌等.基于无记忆非线性变换的相关非高斯雷达杂波的仿真.武汉理工大学学报(交通科学与工程版),2001(1):37-40
[26]蒋咏梅,陆铮.相关非高斯分布杂波的建模与仿真.系统工程与电子技术,1999 ,21(10) :27-30.
[27]许稼,卢凌等.一种基于球不变随机过程的雷达K分布杂波模拟方法.武汉理工大学学报(交通科学与工程版),2000(5):469-472
[28] MARIER L J, Jr. Correlated K- distributed clutter generation for radar detection and track.IEEE Trans on AES, 1995, 31(2):568 - 580.
[29] Rangaswamy M, Weiner D. Computer generation of correlated non-gaussian radar clutter. IEEE Trans. on E. S, 1995, 31: 106-115.
[30] Oliver C J. Correlated K-distributed clutter Models. Optica Acta, 1985, 32 (12): 1515-1547.
[31] Jakeman E, Pusey P N. A model for non-Rayley sea clutter. IEEE Trans. on A. P, 1976: 806-814.
[32] Ward KD. Compound representation of high resolution sea clutter. ElectronicsLetters, 1981: 561-563.
[33] Liu Bede, David C. Generation of a Random Sequence Giving a Jointly Specified Marginal Distribution and Autoconvarince. IEEE Trans, On ASSP, Dec.1982, Vol 30, No.6 :973-983
[34] A.Farina, Arusso. Coherent Radar Detection in LogNormal Clutter. IEE Proc.pt.F, 1986,133, (1) : 39-54
[35]王颖,毛二可.相关K分布杂波的建模与仿真.信号处理,2000,13(2):141-146
[36] E.Conte, M.Longo. Modelling and Simulation of Non-rayleigh Radar Clutter. IEEE Procs F, Vol 138,No.2,1991:121-130
[37] N.C.Currie, F.B.Dyer and R..D.Hayes. Analysis of Radar Return at Frequencies of 9.5,35,70 and 95GHz. Technical Report No.2 on Contract DAA25-73-0625, Georgia Institute of Technology, Atlanta, 1975,2:28-30
[38]盛聚,谢式千.概率论与数理统计.第2版,北京:高等教育出版社,1989
[39]尹志盈.非高斯雷达杂波的建模与模拟:[硕士学位论文]。青岛:中国电波传播研究所,2001
[40]王国玉.电子系统建模仿真与评估.第一版,长沙:国防科技大学出版社,1999
[41]齐欢,王小平.系统建模与仿真.第一版,北京:清华大学出版社,2004
[2] J.B.Billingsley. Low-angle radar land clutter. William Andrew Publishing, 2002,3:256-264
[3] Fred E.Nathanson. Radar design principles. SciTech,1999,7:45-58
[4]汤明.裸地散射特性分析.电波科学学报,Vol 9,No.4,1994:69-75
[5]罗贤云,吉健康等.雷达杂波功率谱模型.电波科学学报,Vol 7,No.4,1992:35-40
[6] A.Farina, A.Russo, etc. Coherent Radar Detection In LogNormal Clutter. IEEE.Proc F, 1986,5:365-374
[7] M.W.朗.陆地和海面的雷达波散射特性.第一版,北京:科学出版社,1981,11
[8] M.Sekine, T.Musha. Weibull-distributed Sea Clutter. IEEE Prceedings-F,Voll30,No.5 1983:471-486
[9] Mark Denny. Simulation Sea Clutter Via the Compound-K Distribution. The institution of electrical engineer, IEEE.1998
[10] W.J.Szajnowski. Generation of Correlated LogNormal Clutter Samples. Electronics Letters, Vol 12, No.19,1979:497-498
[11] E.Conte, M.Longo. On a Coherent Model for LogNormal Clutter. IEEE Procs F, Vol 134, No.2, April 1987:198-201
[12] Gang.Li, Kai-Bor Yu. Modelling and simulation of Coherent Weibull Clutter. IEEE. Proces F, Vol 136, No.1, 1989:2-12
[13] L, James Marier, Jr. Correlated K-distributed Clutter Generation for Radar Detection and Track. IEEE Trans. Aerospace and Electronic Systems 1995,31(2):568-580
[14] MURALIDHAR RANGASWAMY, DONALD WEINER, AYDIN OZTURK. Computer Generation of Correlated Non-Gaussian Radar Clutter. IEEE Trans on AES, Vol 31, No.1,1995:106-115
[15] Simon Haykin, Rembrandt Bakker. Uncovering Nonlinear Dynamics: The Case Study of Sea Clutter. Proceedings of the IEEE, 2002, 90(5):860-881.
[16] BLAKE L V. Radar range-performance analysis. Lexington Book,1980:301-336
[17]何友,关键等.雷达自动检测与恒虚警处理.第一版,北京:清华大学出版社,2003:36-152
[18] Chan.H.C.“Radar Sea-Clutter At Low Grazing Angles”Radar And Signal Processing, IEE Proceeding F, 1990,137(2):102-112
[19] Walker.D.“Doppler Modeling Of Radar Sea Clutter”Radar, Sonar and Navigation, IEE Proceeding E, 2001, 148(2): 73-80.
[20] Baker.C.J.“K-distribute Coherent Sea Clutter”IEE Proceeding, 1991, 138(2): 89-92.
[21] http://soma.ece.mcmaster.ca/ipix
[22] Farina.A.Gini, F.Greco, High Resolution Sea clutter data: A statistical analysis of recorded live data. IEE Proceedings, Radar Sonar Navigation,1997,144(3):121-130
[23] E.CONTE, A.DEMAIO, C.GALDI. Statistical analysis of real clutter at different range resolutions. IEEE Trans. On AES. 2004, 40(3):903-918
[24] William H. Tranter, K. Sam Shanmugan.通信系统仿真原理与无线应用.第一版,北京:机械工业出版社,2005:152-231
[25]颜南霞,卢凌等.基于无记忆非线性变换的相关非高斯雷达杂波的仿真.武汉理工大学学报(交通科学与工程版),2001(1):37-40
[26]蒋咏梅,陆铮.相关非高斯分布杂波的建模与仿真.系统工程与电子技术,1999 ,21(10) :27-30.
[27]许稼,卢凌等.一种基于球不变随机过程的雷达K分布杂波模拟方法.武汉理工大学学报(交通科学与工程版),2000(5):469-472
[28] MARIER L J, Jr. Correlated K- distributed clutter generation for radar detection and track.IEEE Trans on AES, 1995, 31(2):568 - 580.
[29] Rangaswamy M, Weiner D. Computer generation of correlated non-gaussian radar clutter. IEEE Trans. on E. S, 1995, 31: 106-115.
[30] Oliver C J. Correlated K-distributed clutter Models. Optica Acta, 1985, 32 (12): 1515-1547.
[31] Jakeman E, Pusey P N. A model for non-Rayley sea clutter. IEEE Trans. on A. P, 1976: 806-814.
[32] Ward KD. Compound representation of high resolution sea clutter. ElectronicsLetters, 1981: 561-563.
[33] Liu Bede, David C. Generation of a Random Sequence Giving a Jointly Specified Marginal Distribution and Autoconvarince. IEEE Trans, On ASSP, Dec.1982, Vol 30, No.6 :973-983
[34] A.Farina, Arusso. Coherent Radar Detection in LogNormal Clutter. IEE Proc.pt.F, 1986,133, (1) : 39-54
[35]王颖,毛二可.相关K分布杂波的建模与仿真.信号处理,2000,13(2):141-146
[36] E.Conte, M.Longo. Modelling and Simulation of Non-rayleigh Radar Clutter. IEEE Procs F, Vol 138,No.2,1991:121-130
[37] N.C.Currie, F.B.Dyer and R..D.Hayes. Analysis of Radar Return at Frequencies of 9.5,35,70 and 95GHz. Technical Report No.2 on Contract DAA25-73-0625, Georgia Institute of Technology, Atlanta, 1975,2:28-30
[38]盛聚,谢式千.概率论与数理统计.第2版,北京:高等教育出版社,1989
[39]尹志盈.非高斯雷达杂波的建模与模拟:[硕士学位论文]。青岛:中国电波传播研究所,2001
[40]王国玉.电子系统建模仿真与评估.第一版,长沙:国防科技大学出版社,1999
[41]齐欢,王小平.系统建模与仿真.第一版,北京:清华大学出版社,2004