海杂波建模及其背景下目标检测方法研究
|
摘要
海杂波严重影响着海事雷达的工作性能，海杂波物理特性和统计特性的研究对雷达信号处理、海面目标检测、雷达模拟器设计等应用领域起着关键的作用。随着非线性科学的发展，海杂波被认识到具有混沌分形的许多特征，这使人们开始利用神经网络方法来处理海杂波时间序列和进行目标检测。本文针对电磁散射建模、海杂波统计建模与仿真、海杂波背景下的目标检测三个方面展开研究，主要内容包括：
针对传统电磁散射系数计算的双尺度模型将粗糙海面视为高斯海面，没有考虑迎风和顺风方向上海面的倾斜和不对称性，本文通过引入高阶统计量双频谱刻画海面的这种不对称性，提出了一种基于未充分发展的完全海谱（Non-fullyDeveloped Full-range Sea Spectrum，NDFSS）分形海面的电磁散射系数计算的修正双尺度模型。
针对处理多个脉冲雷达回波数据等情况需要同时考虑海杂波的时间相关特性和空间相关特性，提出了一种基于球不变随机过程（Spherically Invariant RandomProcess，SIRP）的时间空间相关K分布海杂波生成方法，该方法生成的海杂波序列满足指定的时间空间相关性要求，在某外协项目中该方法得到了具体应用。
针对以K分布作为幅度统计模型存在对不均匀区域建模能力不足，及其概率密度函数（Probability Density Function，PDF）表达式中存在修正的贝塞尔函数，算法复杂度较高等问题，而G0分布则具有广泛均匀度变化下的杂波区域建模能力和较强的模型兼容性，同时具有较易的工程可实现性，且其表达式中仅含有指数函数等特点，因此利用G0分布作为幅度统计模型对实测海杂波数据进行拟合，并与K分布进行对比分析，发现G0分布与实测海杂波数据拟合效果更好，通过库尔贝克-莱伯勒（Kullback-Leibler，KL）值度量、均方误差（Mean Square Error，MSE）检验及柯尔莫诺夫-斯米尔诺夫（Kolmogorov-Smirnov，K-S）检验证明了此结论。
根据海杂波具有混沌分形的许多特征，本文使用了回声状态网络（Echo StateNetwork，ESN）预测海杂波时间序列，并利用含有目标和不含目标海杂波时间序列预测值与实测数据之间显著的MSE差异进行目标检测。还利用了ESN的改进方法如解耦回声状态网络（Decoupled Echo State Network，DESN）、具有最大可用信息的解耦回声状态网络（DESN with Maximum Available Information，DESN+MaxInfo）、具有储备池预测的解耦回声状态网络（DESN with ReservoirPrediction，DESN+RP）、以及两种具有简化结构的前馈回声状态网络（Feed ForwardEcho State Network，FF-ESN）和输入抽头延迟线（Tapped Delay Line with Inputs，TDL-I）方法进行了类似地海杂波时间序列预测和目标检测。
使用了ESN方法检测海尖峰，并检测目标所在距离门，为海尖峰的深入研究和分析提供了一种新的思路。
Sea clutter impacts seriously on the performance of maritime radars. The researchon physical and statistical characteristic of sea clutter palys a key role in radar signalprocessing, target detection in sea surface and radar simulator design. Withdevelopment of nonlinear science, sea clutter is realized that it has much character inchaos and fractal. This makes people use neural network method to deal with sea cluttertime series and detect targets. We carry out research on electromagnetic scatteringmodel, statistical model and simulation of sea clutter, target detection in sea clutter. Thework mainly contains:
The conventional two-scale model for electromagnetic scattering coefficentcomputation regards coarse sea surface as Gaussian sea surface, and does not considerslope and asymmetry of sea surface in upwind-downwind direction. In this paper theasymmetry of sea surface is depicted by introducing higher-order statistical bispectrum,and a modified two-scale model for electromagnetic scattering coefficent computationbased on a Non-fully Developed Full-range Sea Spectrum (NDFSS) fractal sea surfaceis presented.
Temporal correlation characteristic and spatial correlation characteristic of seaclutter are considered simultaneously for multi-pulse radar echo data, a generationmethod of temporal-spatial correlated K-distributed sea clutter is proposed based onspherically invariant random processes (SIRP). The generated sea clutter satisfies thedemand of appointed temporal-spatial correlation, and has been applied in a cooperativeproject.
K-distribution as amplitude statistical model for heterogeneous area is deficient.The expression of Probability Density Function (PDF) for K-distribution has themodified Bessel function, and the algorithm complexity is much higher.G0-distribution has modeling ability in clutter regional of widely homogeneous degreevariation and strong model compatibility. This distribution has easily engineeringrealizability, and it’s expression only contains index function. So we useG0-distribution as amplitude statistical model for the sea clutter data fitting. Comparedto K-distribution, we found the results ofG0-distribution and sea clutter real-life datahave better fitting effect. Kullback Leibler (KL) distance, Mean Squared Error (MSE) testand Kolmogorov-Smirnov (K-S) test are used for proving our conclusion.
According to sea clutter with character in chaos and fractal, echo state network(ESN) is used in predicting sea clutter time series, and the great MSE differencesbetween prediction value for sea clutter time series with target and without target andreal-life data are available for detecting target. Improved ESN methods similarly predictsea clutter time setires and detect target, and these methods are decoupled echo state network (DESN), DESN with maximum available information (DESN+MaxInfo),DESN with reservoir prediction (DESN+RP), feed forward echo state network(FF-ESN), and tapped delay line with inputs (TDL-I).
ESN method is used to predict sea spikes, and detect target that exists in one rangebin. This method provides a new route to further research and analyse the sea spikes.
针对传统电磁散射系数计算的双尺度模型将粗糙海面视为高斯海面，没有考虑迎风和顺风方向上海面的倾斜和不对称性，本文通过引入高阶统计量双频谱刻画海面的这种不对称性，提出了一种基于未充分发展的完全海谱（Non-fullyDeveloped Full-range Sea Spectrum，NDFSS）分形海面的电磁散射系数计算的修正双尺度模型。
针对处理多个脉冲雷达回波数据等情况需要同时考虑海杂波的时间相关特性和空间相关特性，提出了一种基于球不变随机过程（Spherically Invariant RandomProcess，SIRP）的时间空间相关K分布海杂波生成方法，该方法生成的海杂波序列满足指定的时间空间相关性要求，在某外协项目中该方法得到了具体应用。
针对以K分布作为幅度统计模型存在对不均匀区域建模能力不足，及其概率密度函数（Probability Density Function，PDF）表达式中存在修正的贝塞尔函数，算法复杂度较高等问题，而G0分布则具有广泛均匀度变化下的杂波区域建模能力和较强的模型兼容性，同时具有较易的工程可实现性，且其表达式中仅含有指数函数等特点，因此利用G0分布作为幅度统计模型对实测海杂波数据进行拟合，并与K分布进行对比分析，发现G0分布与实测海杂波数据拟合效果更好，通过库尔贝克-莱伯勒（Kullback-Leibler，KL）值度量、均方误差（Mean Square Error，MSE）检验及柯尔莫诺夫-斯米尔诺夫（Kolmogorov-Smirnov，K-S）检验证明了此结论。
根据海杂波具有混沌分形的许多特征，本文使用了回声状态网络（Echo StateNetwork，ESN）预测海杂波时间序列，并利用含有目标和不含目标海杂波时间序列预测值与实测数据之间显著的MSE差异进行目标检测。还利用了ESN的改进方法如解耦回声状态网络（Decoupled Echo State Network，DESN）、具有最大可用信息的解耦回声状态网络（DESN with Maximum Available Information，DESN+MaxInfo）、具有储备池预测的解耦回声状态网络（DESN with ReservoirPrediction，DESN+RP）、以及两种具有简化结构的前馈回声状态网络（Feed ForwardEcho State Network，FF-ESN）和输入抽头延迟线（Tapped Delay Line with Inputs，TDL-I）方法进行了类似地海杂波时间序列预测和目标检测。
使用了ESN方法检测海尖峰，并检测目标所在距离门，为海尖峰的深入研究和分析提供了一种新的思路。
Sea clutter impacts seriously on the performance of maritime radars. The researchon physical and statistical characteristic of sea clutter palys a key role in radar signalprocessing, target detection in sea surface and radar simulator design. Withdevelopment of nonlinear science, sea clutter is realized that it has much character inchaos and fractal. This makes people use neural network method to deal with sea cluttertime series and detect targets. We carry out research on electromagnetic scatteringmodel, statistical model and simulation of sea clutter, target detection in sea clutter. Thework mainly contains:
The conventional two-scale model for electromagnetic scattering coefficentcomputation regards coarse sea surface as Gaussian sea surface, and does not considerslope and asymmetry of sea surface in upwind-downwind direction. In this paper theasymmetry of sea surface is depicted by introducing higher-order statistical bispectrum,and a modified two-scale model for electromagnetic scattering coefficent computationbased on a Non-fully Developed Full-range Sea Spectrum (NDFSS) fractal sea surfaceis presented.
Temporal correlation characteristic and spatial correlation characteristic of seaclutter are considered simultaneously for multi-pulse radar echo data, a generationmethod of temporal-spatial correlated K-distributed sea clutter is proposed based onspherically invariant random processes (SIRP). The generated sea clutter satisfies thedemand of appointed temporal-spatial correlation, and has been applied in a cooperativeproject.
K-distribution as amplitude statistical model for heterogeneous area is deficient.The expression of Probability Density Function (PDF) for K-distribution has themodified Bessel function, and the algorithm complexity is much higher.G0-distribution has modeling ability in clutter regional of widely homogeneous degreevariation and strong model compatibility. This distribution has easily engineeringrealizability, and it’s expression only contains index function. So we useG0-distribution as amplitude statistical model for the sea clutter data fitting. Comparedto K-distribution, we found the results ofG0-distribution and sea clutter real-life datahave better fitting effect. Kullback Leibler (KL) distance, Mean Squared Error (MSE) testand Kolmogorov-Smirnov (K-S) test are used for proving our conclusion.
According to sea clutter with character in chaos and fractal, echo state network(ESN) is used in predicting sea clutter time series, and the great MSE differencesbetween prediction value for sea clutter time series with target and without target andreal-life data are available for detecting target. Improved ESN methods similarly predictsea clutter time setires and detect target, and these methods are decoupled echo state network (DESN), DESN with maximum available information (DESN+MaxInfo),DESN with reservoir prediction (DESN+RP), feed forward echo state network(FF-ESN), and tapped delay line with inputs (TDL-I).
ESN method is used to predict sea spikes, and detect target that exists in one rangebin. This method provides a new route to further research and analyse the sea spikes.
引文
[1] Eckmann P, Spizzichino A. The scattering of electromagnetic waves from roughsurfaces [M]. New York: Pergamon,1963.
[2] Jaggard D L, Sun X. Fractal surface scattering: A generalization rayleigh solution [J]. JApplied Phys,1990,68(11):5456-5462.
[3] Berizzi F. Fractal analysis of the signal scattered from the sea surface [J]. IEEE Trans onAntennas Propagation,1999,47(2):324-338.
[4] Guo L X, Wu Z S. Fractal model and electromagnetic scattering from time-varying seasurface [J]. Electronics Letters,2000,36(21):1810-1812.
[5]郭立新,任玉超,吴振森.动态分形粗糙海面散射的遮蔽效应和多普勒谱研究[J].电子与信息学报,2005,27(10):1666-1670.
[6]郭立新，王运华，吴振森.双尺度动态分形粗糙海面的电磁散射及多普勒谱研究[J].物理学报,2005,54(1):96-101.
[7] Chen J. The use of fractals for modeling EM waves scattering from rough sea surface [J].IEEE Trans. Geosci. Remote Sens.,1996,34(4):966-972.
[8] David L, Piotr S. Full-Range Sea Surface Spectrum in Nonfully Developed State forScattering Calculations [J]. IEEE Trans. Geosci. Remote Sens.,1999,37(2):1038-1051.
[9]刘历博，肖景明，吴振森，张忠治，未充分生成海的海面模型及其雷达散射系数[J]，西安电子科技大学学报，2001,28(3):388-392.
[10]杨俊岭，郭立新，万建伟.基于未充分发展海谱的分形海面模型及其电磁散射研究[J].物理学报，2007,56(4):2106-2114.
[11] Thorsos E I. The validity of the Kirchhoff approximation for rough surface scatteringusing a Gaussian roughness spectrum [J]. Journal of the Acoustical Society of America,1988,83(1):78-92.
[12] Ulaby F T. Microwave remote sensing [M]. Addison-Wesley Publishing,1982.
[13] Alexander G V. Wave Scattering from Rough Surfaces [M]. NewYork: spring-Verlag,1994.
[14] Ishimaru A, Chen J S. Scattering from very rough surfaces based on the modifiedsecond-order Kirchhoff approximation with angular and propagation shadowing [J].Joumal of the Acoustical Society of America,1990,88:1877-1883.
[15] Thorsos E, Jackson D R. The validity ofthe perturbation approximation for rough surfacescattering using a Gaussian roughness spectrum [J]. Joumal of the Acoustical Society ofAmerica,1989,86(1):261-277.
[16] Soto-Crespo J M, Nieto-Vesperinas M. Scattering from slightly rough random surfaces:a detailed study on the validity of the small perturbation method [J]. Journal of theOptical Society of America,1990, A7(7):1185-1201.
[17] Valenzuela G R. Depolarization of EM wave by slightly rough surface [J]. IEEETransactions on Antennas and Propagation,1967,15(4):552-557.
[18] Twang L, Kong J A, Shin R T. Theory of microwave remote sensing [M]. New York:Wiley-Interseienee,1985.
[19] Ogilvy J A. Theory of wave scattering from random rough surfaces [M]. NewYork:Adam Hilger,1997.
[20] Voronovich A G. Small slope approximation for electromagnetic wave scattering at arough interface of two dielectric half-spaces [J]. Waves in Random Media,1994,4(3):337-367.
[21] Voronovich A G. Small slope approximation in wave scattering by rough surfaces [J].Sov. Phys.–JETP,1985,62(1):65-70.
[22] Thorsos E I, Broschat S L. An investigation of the small slope approximation forscattering from rough surfaces: Part I-Theory [J]. Journal of the Acoustical Society ofAmerica,1995,97(4):2082-2093.
[23] Voronovich A G. Non-local small slope approximation for wave scattering fromroughsurfaces [J]. Waves Random Media,1996,6(2):151-167.
[24] McDaniel S T. An extension ofthe small slope approximation for roughsurfaceScattering [J]. Waves Random Media,1995,5(2):201-214.
[25] Tatarskii V I. Statistical description of rough surface scattering using the quasi-small-slope approximation for random surfaces with a Gaussian multivariate probabilitydistribution [J]. Waves Random Media,1994,4(2):191-214.
[26] Gilbert M S, Johnson J T. A study of rough surface scattering effects using thehigher-order small slope approximation [C]. International Antennas and PropagationSymposium,2003,1:557-560.
[27] Bahar E. Scattering cross sections for random surfaces: Full Wave Analysis [J]. RadioScience,1981,16(3):331-341.
[28] Bahar E, Lee B S. Radar scattering crross sections for two-dimensional random roughsurfaccs-Full wave solutions and comparisons with experiments [J]. Waves in RandomMedia,1996,6(1):1-23.
[29] Bahar E, Hung G, Lee B S. Electromagnetic scattering and depolarization across roughsurfaces: Full wave analysis [J]. Radio Science,1995,30(3):525-544.
[30] Bahar E, Lee B S. Full wave solutions for rough-surface bistatic radar cross sections:Comparison with small perturbation, physical optics, numerical, and experimentalresults [J]. Radio Science,1994,29(2):407-429.
[31] Collin R E. Full wave theories for rough surface scattering: An updated assessment [J].Radio Science,1994,29(5):1237-1254.
[32] Bahar E. Full wave solutions for the depolarization of the scattered radiation fields byrough surfaces with arbitrary slope [J]. IEEE Transactions on Antennas and Propagation,1981,29(3):443-454.
[33] Collin R E. Scattering of an incident Gaussian beam by a perfectly conducting roughsurface [J]. IEEE Transactions on Antennas and Propagation,1994,42(1):70-74.
[34] Fung A K, Pan G. An integral equation method for rough surface scattering [C]. Proc.Int. Symp. on Multiple Scattering of Waves in Random Media and Random Surface.1986,701-714.
[35] Alvarez P, Jose L. An extension of the IEM/IEMM surface scattering model [J]. Wavesin Random Media,2001,11(3):307-329.
[36] Wu Z S, Zhang Y D. Backscattering enhancement from very rough surfaces based onintegral equation model [C]. Proceedings of the SPIE,2003,4892:455-461.
[37] Hsieh C Y, Fung A K. Application of an extended IEM to multiple surface scatteringand backscatter enhancement [J]. Journal of Electromagnetic Waves and Applications,1999,13(1):121-136.
[38] Hsieh C Y, Fung A K. Nesti G. A further study of the IEM surface scattering model:Remote sensing for a sustainable future [J]. IEEE transactions on Geoscienee andRemote Sensing,1997,35(4):795-979.
[39] Koudogbo F, Combes P F. Numerical and experimental validations of IEM for bistaticscattering from natural and manmade rough surfaces [J]. Progress in EleetromagneticsResearch,2004,46(1):203-244.
[40] Chen K S, Wu T D, Tsay M K. A note on the multiple scattering in an IEM Model [J].IEEE Transactions on Geoscience and Remote Sensing,2000,38(1):249-256.
[41] Johnson J T, Shin R T. A numerical study of the composite surface model for oceanbackscattcring [J]. IEEE Transactions on Geoscience and Remote Sensing,1998,36(1):72-83.
[42] Durden S L, Vesecky J F. A numerical study of the separation wavenumber in the twoscale scattering approximation [J]. IEEE Transactions on Geoscience and RemoteSensing,1990,28(2):271-272.
[43] Plant W J. A two-scale model of short wind generated waves and scatterometry [J]. J.Geophys. Res.,1986,91(C9):10735-10749.
[44] Wang Y H, Guo L X, Wu Z S. Modified two-scale model for electromagnetic scatteringfrom the non-Guassian oceanic surface [J]. Chin. Phys. Lett.,2005,22(11):2808-2811.
[45]郭立新，王运华，吴振森.修正双尺度模型在非高斯海面散射中的应用[J].电波科学学报，2007，22(2):212-218.
[46] Ulaby F T, Dobson M C. Handbook of radar scattering statistics for terrain [M].Norwood: MA: Artech House,1989.
[47] Trunck G V, George S F. Detection of targets in non-gaussian sea clutter [J]. IEEE Trans.AES-6, Sep1970,620-628.
[48] Sekine M, Mao Y. Weibull radar clutter [M]. London: Peter Peregrinus,1990.
[49] Sekine M. Weibull distributed ground clutter [J]. IEEE Trans. AES, Jul1981,17:596-598.
[50] Sekine M. Weibull distributed sea clutter [C]. IEE Proc.130, Pt. F,1983:476.
[51] Schleher D C. Radar detection in weibull clutter [J]. IEEE Trans. AES, Nov1976,12:736-743.
[52] Rey M, Drosopoulos T, Petrovic D. A search procedure for ships in radar sat imagery[R]. Dreo report,1996, No.1305.
[53] Ward K D. A radar sea clutter model and its application to performance assessment [C].IEEE international Radar Confrence,1982:203-207.
[54] Sheen D R, Johnston L P. Statistical and spatial properties of forest clutter measuredwith polarimetric synthetic aperture radar [J]. IEEE Trans. GPS,1992,30(3):578-585.
[55]王盈.相关K分布杂波的建模与仿真[J],信号处理,1997,13(2):141-146.
[56]韩兴斌，胡卫东，郁文贤.海面目标ISAR成像的杂波仿真与抑制[J].现代雷达，2007,29(10):44-48.
[57] Watts S. Cell-averaging CFAR gain in spatially correlated K-distributed clutter [J].Radar, Sonar and Navigation, IEE Proceedings,1996,143(5):321-327.
[58] Wise G L, Traganitis A P, Thomas J B. The effect of a memoryless nonlinearity on thespectrum of a random process [J]. IEEE Trans. Inform. Theory,1977,23(1):84-89.
[59] L J Marier. Correlated K-distributed clutter generation for radar detection and track [J].IEEE Trans. Aerosp. Electron. Syst.,1995,31(2):568-580.
[60] Conte E, Longo M, Lops M. Modeling and simulation of non-Rayleigh radar clutter [J].IEE Proceedings F,1991,138(2):121-130.
[61] Frery A C, Muller H J, Yanasse C F. A model for extremely heterogeneous clutter [J].IEEE Trans. on GRS,1997,35(3):648-659.
[62] Moser G, Zerubia J, Serpico S B. Dictionary-based stochastic expectation-maximizationfor SAR amplitude probability density function estimation [J]. IEEE Trans. on GRS,2006,44(1):188-200.
[63] Eltoft T. Modeling the amplitude statistics of ultrasonic images [J]. IEEE Trans. onMedical Imaging,2006,25(2):229-240.
[64] Moser G, Zerubia J, Serpico S B. SAR amplitude probability density function estimationbased on a generalized Gaussian model [J]. IEEE Trans. on IP,2006,15(6):1429-1442.
[65] Greco M S, Gini F. Statistical analysis of high-resolution SAR ground clutter data [J].IEEE Trans. on GRS,2007,45(3):566-575.
[66]高贵. SAR图像目标ROI自动获取技术研究[D].长沙：国防科技大学博士学位论文，2007:18-55.
[67]时公涛.基于Mellin变换的G0分布参数估计方法[J]，自然科学进展，2009，19(6):677-690.
[68]皇甫堪,陈建文,楼生强.现代数字信号处理[M]，北京:电子工业出版社,2003:282-288.
[69] Tison C. A new statistical model for Markovian classification of urban areas inhigh-resolution SAR images [J]. IEEE Transactions on Geoscience and RemoteSensoring,2004,42:2046-2057.
[70] Nicolas J M, Tupin F. Gamma mixture modeled with second kind statistics applicationto SAR image processing [C]. IEEE conference,2002:2489-249.
[71] Abdelfattah R, Nicolas J M. Interferometric SAR coherence magnitude estimation usingsecond kind statistics [J]. IEEE Trans. on GRS,2006,44(7):1942-1953.
[72] Watts S. The performance of cell-averaging CFAR systems in sea clutter [C], IEEEInternational radar conference,2000,398-403.
[73]胡文琳，王永良，王首勇. Log-normal分布杂波背景下有序统计恒虚警检测器性能分析[J].电子与信息学报，2007，29(3):517-520.
[74] Zaimbashi A, Taban M R, Nayebi M M, Norouzi Y. Weighted order statistic and fuzzyrules CFAR detector for weibull clutter [J], Signal processing,2008,88(3):558-570.
[75]武楠，徐艳国，李宗武.海杂波背景下的双参数单元平均恒虚警检测器[J]，现代雷达，2010，32(2):52-56.
[76]陈建军，黄孟俊，邱伟，赵宏钟，付强.海杂波下的双门限恒虚警目标检测新方法[J].电子学报，2011，39(9):2135-2141.
[77] Haykin S, Puthusserypady S. Chaotic Dynamics of Sea Clutter [M]. John Wiley&Sons,Inc,1999.
[78] Leung H, Haykin S. Neural Network Modeling of Radar Backscatter from an OceanSurface Using Chaos Theory [C]. Adaptive Signal Processing, SPIE Proceedings,1991,1565:279-286.
[79] He N, Haykin S. Chaotic Modeling of Sea Clutter [J]. Electronics Letters,1992,28(22):2076-2077.
[80] Haykin S. Radar Clutter Attractor: Implications for Physics, Signal Processing andControl [J]. IEE Proc-Radar, Sonar Navig,1999,146(2):177-188.
[81] Haykin S, Bakker R, Currie B W. Uncovering Nonlinear Dynamics-the Case Study ofSea Clutter [J]. Proceedings of the IEEE,2002,90(5):860-881.
[82] Leung H, Lo T. Chaotic Radar Signal Processing over the Sea [J]. IEEE Journal ofOceanic Engineering,1993,18(3):287-295.
[83] Haykin S, Li X B. Detection of Signals in Chaos [J]. Proceeding of the IEEE,1995,183(1):94-122.
[84] Chakravarthi P R. Radar Target Detection in Chaotic Clutter [C]. IEEE National RadarConference,1997:367-371.
[85] Morrison A I, Srokosz M A. Estimating of the fractal dimension of the sea surface-a1stattempt [R]. Annales Geophysical-Atmospheres HY drosspheres and Space, Sep11,1993:648-658.
[86]杜干.目标检测的分形方法及应用[D].西安：西安电子科技大学博士学位论文，2000.
[87]石志广.基于统计与复杂性理论的杂波特性分析及信号处理方法研究[D].长沙：国防科学技术大学博士学位论文，2007.
[88] Xu X K. Low observable targets detection by Low Observable Targets Detection byJoint Fractal Properties of Sea Clutter: An Experimental Study of IPIX OHGR Datasets[J], IEEE Transactions on antennas and propagation.2010,58(4):1425-1429.
[89]关键，李秀友，黄勇，张建.基于海杂波分形特性的目标检测新方法[J].电光与控制，2008，15(12):5-9.
[90]刘宁波，李晓俊，李秀友，关键.基于分形自仿射预测的海杂波中目标检测[J].现代雷达，2009，31(4):43-50.
[91]关键，刘宁波，张建，宋杰.海杂波的多重分形关联特性与微弱目标检测[J].电子与信息学报，2010,32(1):54-61.
[92]关键，刘宁波，张建，宋杰.基于LGF的海杂波中微弱目标检测方法[J].信号处理.2010,26(1):69-73.
[93]刘宁波，关键，张建.基于分形可变步长LMS算法的海杂波中微弱目标检测[J].电子与信息学报，2010，32(2):371-376.
[94]张建，关键，刘宁波，宋杰.基于EMD和盒维数的固定微弱目标检测[J].信号处理，2010，26(4):492-496.
[95] Haykin S, Puthusserypady S, Yee P. Dynamic Reconstruction of Sea Clutter UsingRegularized RBF Networks [C]. The thirty-second asilomar conference on signals,systems&computers,1998:19-23.
[96] Leung H, Dubash N and Xie N. Detection of small objects in clutter using a GA-RBFneural network [J]. IEEE transactions on aerospace and electronic systems.2002,38(1):98-116.
[97] Lopez-Risueno G, Grajal J, Diaz-Oliver R. Target detection in sea clutter usingconvolutional neural networks [C]. Proceedings of IEEE radar conference,2003:321-328.
[98]谢红梅.基于混沌理论的信号处理方法研究[D].西安：西北工业大学博士学位论文.2003.
[99]陈瑛，罗鹏飞，曾勇虎.基于RBF神经网络的海杂波建模[J].系统仿真学报，2007，19(3):524-526.
[100] Thayaparan T, Kennedy S. Detection of a manoeuvring air target in sea-clutter usingjoint time–frequency analysis [C], IEE Proceedings-Radar Sonar&Navigation,151(1),2004,19-30.
[101] Davidson G, Griffiths H D. Wavelet detection scheme for small targets in sea clutter [J],Electronics letters,2002,38(19):1128-1130.
[102] Melino R, Tran H T. Application of the Fractional Fourier Transform in the Detection ofAccelerating Targets in Clutter [R], DSTO Defence Science and TechnologyOrganisation, PO Box1500Edinburgh, South Australia5111, Australia,2011.
[103]陈小龙，刘宁波，宋杰，关键，何友.海杂波FRFT域分形特征判别及动目标检测方法[J].电子与信息学报，2011，33(4):823-830.
[104]王福友，袁赣南，周卫东，白文娟.基于时间-Doppler分析的海杂波背景下小目标检测[J]，海洋测绘，2009，29(5):33-36.
[105] Phillips O M. Radar returns from the sea surface [J]. J. Phys. Oceanogr.,1988,18(4):1065-1074.
[106] Farina A, Gini F, Greco M V, Lee P H Y. Improvement factor for real sea-clutterdoppler frequency spectra [J]. IEE Proceedings Radar, Sonar, Navigation,1996,143(5):341-344.
[107] Farina A, Gini F, Greco M V, Verrazzani L. High resolution sea clutter data: statisticalanalysis of recorded live data [J]. IEE Proceedings Radar, Sonar, Navigation,1997,144(3):121-130.
[108] Posner F L. Spiky sea clutter at low grazing angles and high range resolutions [C]. IEEEProceedings of Radar Conference.1998:405-410.
[109] Kulemin G P. Spike characteristics of radar sea clutter for extremely small grazingangles (part1)[R]. Proc. SPIE, Radar Sensor Technology V,2000,4033-006:52-60.
[110] Gutnik, V G, Kulemin G P, Sharapov L I. Spike statistics features of the radar sea clutterin the millimeter wave band at extremely small grazing angles [J]. Physics andEngineering of Millimeter and Sub-Millimeter Waves,2001,1(4):426-428.
[111] Posner F. Spiky sea clutter at high range resolutions and very low grazing angles [J].IEEE Transactions on aerospace and electronic systems,2002,38(1):58-73.
[112] Keller M R, Gotwols B L, Chapman R D. Multiple sea spike definitions: reducing theclutter [C]. IEEE Geoscience and Remote Sensing Symposium,2002,2:940-942.
[113] Posner F, Gerlach K. Sea spike demographics at high range resolutions and very lowgrazing angles [C]. IEEE Proceedings of the Radar Conference,2003:38-45.
[114] Melief H W, H Greidanus, P Genderen, P Hoogeboom. Analysis of sea spikes in radarsea clutter data [J]. IEEE Transactions on geoscience and remote sensing,2006,44(4):985-993.
[115] Liu Y, Frasier S J, Mclntosh R E. Measurement and classification of low-grazing angleradar sea spikes [J]. IEEE Trans on Antennas Propagation,1998,46(1):27-40.
[116] Holliday D, Deraad L L, Stcyr G J. Sea spike backscatter from a steepening wave [J].IEEE Trans on Antennas Propagation,1998,46(1):108-113.
[117] Rino C L, Ngo H D. Numerical simulation of low-grazing angle ocean microwavebackscatter and its relation to sea spikes [J]. IEEE Trans on Antennas Propagation,1998,46(1):133-141.
[118] Sletten M A. Multipath scattering in ultrawide band radar sea spikes [J]. IEEE Trans onAntennas Propagation,1998,46(1):45-56.
[119] Greco M, Stinco P, Gini F. Identification and analysis of sea radar clutter spikes [J]. IETRadar Sonar Navig.,2010,4(2):239-250.
[120] Jaggard D L, Sun X. Scattering from fractally corrugated surfaces[J]. J. Opt. Soc. Amer.A,1990,1131-1139.
[121] Berizzi F. Fractal analysis of the signal scattered from the sea surface [J]. IEEE Trans onAntennas Propagation,1999,47(2):324-338.
[122] Mandelbrot B B. The fractal geometry of nature [M]. San Francisco: Freeman,1982.
[123] Jaggard D L, Kim Y. Diffraction by band-limited fractal screens [J]. J. Opt. Soc. Am. A.,1987,4(6):1055-1062.
[124] Jaggard D L, Sum X. Scattaiag from fractal corrugated surface [J]. J. Opt. Soc. Am. A.,1990,7(6):1131-1139.
[125] Frenceschotti G. Scattering from natural rough surfaces modeled by fractional Brownianmotion two-dimensional processes [J]．IEEE Trans. Antennas Propag.,1999,47(9):1405-1415.
[126] Jakemen E. Scattering by a corrugated random surface with fractal slope [J]. J. Phys. A:Math. Gen.,1982,15(2):55-59.
[127]郭立新，吴振森．二维导体粗糙面电磁散射的分形特征研究[J]．物理学报．2000，49(6):1064-1069.
[128] Guo L X, Jiao L C, Wu Z S．Electromagnetic scattering from two-dimeosional roughsurface using the Kirchhoff approximation [J]. Chin. Phys. Lett,2001,18(1):214-216.
[129]郭立新，吴振森．fBm随机粗糙面电磁散射的微扰法近似[J]．微波学报，2001，17(2):60-66.
[130]郭立新，徐燕，吴振森．分形粗糙海面高斯波束散射特性模拟[J]．电子学报，2005，33(3):534-537.
[131]郭立新，金彩英．脉冲波入射时分形粗糙海面的双频散射截面和脉冲展宽[J]．红外与毫米波学报，2003，22(2):132-136.
[132]杨超，郭立新.粗糙海面电磁散射的小斜率近似方法[J].系统工程与电子技术，2009，31(8):1814-1818.
[133]王运华，郭立新，吴振森.改进的一维分形模型在海面电磁散射中的应用[J].电子学报，2007，35(3):478-483.
[134] Lemaire D, Sobieski P, Guissard A. Scattering by the ocean surface under nonfullydeveloped state: Two scale model validation and inversion of radar measurements [J]. InProc.3rd ERS Symp., Florence, Italy, Mar.1997:6.
[135] Donelan M A, Pierson W. Radar scattering and equilibrium ranges in wind-generatedwaves with application to scatterometry [J]. J. Geophys. Res.,1987,92(5):4971-5029.
[136] Hasselmann K, Ross D B, Muller P, Sell W. A parametric wave prediction model [J]. J.Phys. Oceanogr.,1976,2:200-228.
[137] Guissard A, Baufays C, Sobieski P. Fully and nonfully developed sea models formicrowave remote sensing applications [J], Remote Sens. Environ.,1994,48:25-38.
[138] Toba Y. Local balance in the air-sea boundary processes: On the spectrum of windwaves [J]. J. Oceanogr. Soc. Jpn.,1973,29:209-220.
[139] Mitsuyasu H. Measurement of the high-frequency spectrum of ocean surface waves [J]. J.Phys. Oceanogr.,1977,7:882-891.
[140] Hara T, Bock E J, Donelan M. Frequency-wavenumber spectrum of wind-generatedgravity-capillary waves [J]. J. Geophys. Res.,1997,102:1061-1072.
[141] Hwang P A, Trizna D B, Wu J. Spatial measurements of short wind waves using ascanning slope sensor [J]. Dyn. Atmosph. Oceans,1993,20:1-23.
[142] Jahne B, Riemer K S. Two-dimensional wavenumber spectra of small-scale watersurface waves [J]. J. Geophys. Res.,1990,95(C7):11531-11546.
[143] Klinke J, Jahne B.2D wavenumber spectra of short wind waves-Results from wind wavefacilities and extrapolation to the ocean [J]. Opt. Air-Sea Interface,1992,1749:245-257.
[144] Pierson W J, Stacy R A. The elevation, slope and curvature spectra of a wind roughenedsea surface [R]. Final Rep. NASA-CR-2247, CUNY Inst. Marine Atmosph. Sci., CityUniv., New York,1973:129.
[145] Cox C S. Measurements of slopes of high frequency wind waves [J]. J. Marine Res.,1958,16(3):199-229.
[146] Long M W. Radar reflectivity of land and sea [M].2nd ed. Artech House,1983.
[147] Chen K S, Fung A K, Weissman D E. A backscattering model for ocean surface [J].IEEE Trans. Geosci. Remote Sens.,1992,30(4):811-817．
[148] Guissard A. Multispectra for ocean-like random rough surface scattering [J]. J.Electromagn. Waves Appl.,1996,10:1413-1443.
[149] George S. The detection of monfluctuating targets in log-normal clutter [R]. NRL Report,1968, No.6796.
[150] Fay F A, Clarke J, Peters R S. Weibull distribution applied to sea-clutter [C]. Proc. IEEConf. Radar, London,1977:101-104.
[151] Rangaswamy M, Weiner D. Computer generation of correlated non-gaussian radarclutter [J]. IEEE Trans. Aerosp. Electron. Syst.,1995,3l(1):106-115.
[152] Yanhui H, Feng L, Baobao Zhang. Simulation of coherent correlation K-distribution seaclutter based on SIRP [C]. Radar CIE Conference2006:1-4.
[153]周万幸，基于SIRP的时空相关K分布海杂波仿真研究，电子学报，2009，37(12):2672-2676.
[154] Armstrong B C, Griffiths H D. Modeling Spatially Correlated K-Distributed RandomClutter[J]. Electron. Letters,1991,27(15):1355-1356.
[155] Freitas C C, Frery A C, Correia A H. The polarimetric G distribution for SAR dataanalysis. Environmetrics,2005,16(1):13-31.
[156]石志广，周剑雄，付强. K分布海杂波参数估计方法研究.信号处理，2007，3:420-424.
[157] Blacknell D, Tough R J A. Parameter estimation for the K-distribution based on [z log(z)][C]. IEE Proc.-Radar.Sonar Naving,2001, No.6.
[158] Iskander D R, Zoubir A M. Estimation of the Parameters of the K-Distribution UsingHigher Order and Fractional Moments [C]. IEEE Trans. on AES,1999, No.4.
[159] DeVore M D. Analytical performance evaluation of SAR ATR with inaccurate orestimated models [J]. Proceedings of SPIE,2004,5427:407-417.
[160] DeVore M D, O’Sullivan J A. Quantitative statistical assessment of conditional modelsfor synthetic aperture radar [J]. IEEE Trans. on IP,2004,13(2):113-124.
[161] Fung A K. Microwave scattering and emission models and their application[M]. ArtechHouse,1994.
[162]张剑.基于插件技术的雷达信号仿真软件的设计与实现[D]，长沙：国防科学技术大学硕士学位论文，2009.
[163]丁鹭飞，耿富录.雷达原理[M]，西安:西安电子科技大学出版社,2002:128-129.
[164]于宵辉.基于平台/插件软件结构的雷达视频回波模拟器的研究与实现[D]，长沙：国防科学技术大学硕士学位论文，2009.
[165] Irina Antipov. Simulation of sea clutter returns[R]. Defence science and technologyorganisation, Australia, ADA352675,1998.
[166] Billingsley J B, Larrabee J F. Multifrequency measurements of radar ground clutter at42Sites[R]. MIT Lincoln Laboratory, ADA312399,1996.
[167]刘剑，赵艳丽，罗鹏飞.基于海杂波混沌特性的目标检测[J].现代雷达，2004，26(2):56-60.
[168]陈瑛，罗鹏飞，海杂波背景下基于RBF神经网络的目标检测[J].雷达科学与技术，2005，3(5):271-275.
[169]陈晓楠，许小可，索继东，基于径向基函数的海杂波时间序列预测方法[J].大连海事大学学报，2007，33(1):115-118.
[170] Hu J, Gao J B, Posner F L, Zheng Y, Tung W W. Target detection within sea clutter: acomparative study by fractal scaling analyses [J], Fractals,2006,14(3):187-204.
[171] Gao J B, Cao Y H, Tung W W, Hu J. Multiscale analysis of complex time series [M].New York: Wiley,2007.
[172] Hu J, Tung W W, Gao J B. Detection of Low Observable Targets Within Sea Clutter byStructure Function Based Multifractal Analysis [J]. IEEE Transactions on antennas andpropagation,2006,54(1):136-143.
[173] Talkner P, Weber R O. Power spectrum and detrended fluctuation analysis: applicationto daily temperatures [J]. Phys. Rev. E,2000,62:150-160.
[174]王福友，卢志忠，袁赣南，周卫东.基于时空混沌的海杂波背景下小目标检测[J].仪器仪表学报，2009，30(6):1180-1185.
[175] Jaeger H. The echo state approach to analysing and training recurrent neural networks[R]. Tech. Rep. Fraunhofer Institute for Autonomous Intelligent Systens: GermanNational Research Center for Information Technology (GMD Report148),2001.
[176] Jarger H, Haas H. Harnessing nonlinearity: Predicting chaotic systems and saving energyin wireless communication [J]. Science,2004,304(5667):78-80.
[177]袁长清，李俊峰，邓志东，宝音贺西.基于ESN网络的航天器姿态跟踪鲁棒控制[J].清华大学学报，2008,48(8):1362-1367.
[178]史志伟，韩敏. ESN岭回归学习算法及混沌时间序列预测[J].控制与决策，2007，22(3):258-267.
[179]彭宇，王建民，彭喜元.基于回声状态网络的时间序列预测方法研究[J].电子学报，2010，38(2A):148-154.
[180]韩敏，穆大芸.回声状态网络LM算法及混沌时间序列预测，控制与决策，2011，26(10):1469-1472.
[181] Gunturkun U. Toward the Development of radar scene analyzer for cognitive radar [J].IEEE journal of oceanic engineering,2010,35(2):303-313.
[182] Xue Y B, Yang L, Haykin S. Decoupled echo state networks with lateral inhibition [J].Neural Networks,2007,20:365-376.
[183] Jaeger H. Adaptive nonlinear system identification with echo state networks [C]. In S. T.S. Becher,&K. Obermayer (Eds.), Advances in neural information processing systems,Cambrige, MA: MIT Press,2003:593-600.
[184]时飞飞.基于改进ESN的高炉煤气系统预测方法的研究[D]，大连：大连理工大学硕士学位论文，2009.
[185] Ishii K, Zant T van der, Becanovic V, Ploger P. Identification of motion with echo statenetwork [C]. In Proc. OCEANS’04,2004:1205-1210.
[186] Ploger P G, Arghir A, Gunther T, Hosseiny R. Echo state networks for mobile robotmodeling and control [C]. In Proc. RoboCup,2003:157-168.
[187] Schmidhuber J, Gagliolo M, Wierstra D, Gomez F. Evolino for recurrent support vectormachines [R]. Technical report no. IDSIA-19-15, IDSIA/USI-SUPSI, Manno,Switzerland,2005.
[188] Fischler M A, Firschein O. Intelligence: The eye, the brain and the computer, Reading[M], MA: Addison-Wesley,1987.
[189] Diamantaras K I, Kung S K. Principal component neural network: theory and application
[M]. New York: Wiley,1996.
[190] Cernansky M, Tino P. Predictive modeling with echo state networks [EB/OL].http://www2.fiit.stuba.sk/~cernans/main/publications/icann2008_paper_cernansky.pdf,2008.
[191] Posner F, Gerlach K. Sea spike demographics at high range resolutions and very lowgrazing angles [C]. IEEE Radar Conference,2003:38-45.
[192] Greco M, Stinco P, Gini F. Identification and analysis of sea radar clutter spikes [J]. IETRadar Sonar Navig.,2010,4(2):239-250.
[2] Jaggard D L, Sun X. Fractal surface scattering: A generalization rayleigh solution [J]. JApplied Phys,1990,68(11):5456-5462.
[3] Berizzi F. Fractal analysis of the signal scattered from the sea surface [J]. IEEE Trans onAntennas Propagation,1999,47(2):324-338.
[4] Guo L X, Wu Z S. Fractal model and electromagnetic scattering from time-varying seasurface [J]. Electronics Letters,2000,36(21):1810-1812.
[5]郭立新,任玉超,吴振森.动态分形粗糙海面散射的遮蔽效应和多普勒谱研究[J].电子与信息学报,2005,27(10):1666-1670.
[6]郭立新，王运华，吴振森.双尺度动态分形粗糙海面的电磁散射及多普勒谱研究[J].物理学报,2005,54(1):96-101.
[7] Chen J. The use of fractals for modeling EM waves scattering from rough sea surface [J].IEEE Trans. Geosci. Remote Sens.,1996,34(4):966-972.
[8] David L, Piotr S. Full-Range Sea Surface Spectrum in Nonfully Developed State forScattering Calculations [J]. IEEE Trans. Geosci. Remote Sens.,1999,37(2):1038-1051.
[9]刘历博，肖景明，吴振森，张忠治，未充分生成海的海面模型及其雷达散射系数[J]，西安电子科技大学学报，2001,28(3):388-392.
[10]杨俊岭，郭立新，万建伟.基于未充分发展海谱的分形海面模型及其电磁散射研究[J].物理学报，2007,56(4):2106-2114.
[11] Thorsos E I. The validity of the Kirchhoff approximation for rough surface scatteringusing a Gaussian roughness spectrum [J]. Journal of the Acoustical Society of America,1988,83(1):78-92.
[12] Ulaby F T. Microwave remote sensing [M]. Addison-Wesley Publishing,1982.
[13] Alexander G V. Wave Scattering from Rough Surfaces [M]. NewYork: spring-Verlag,1994.
[14] Ishimaru A, Chen J S. Scattering from very rough surfaces based on the modifiedsecond-order Kirchhoff approximation with angular and propagation shadowing [J].Joumal of the Acoustical Society of America,1990,88:1877-1883.
[15] Thorsos E, Jackson D R. The validity ofthe perturbation approximation for rough surfacescattering using a Gaussian roughness spectrum [J]. Joumal of the Acoustical Society ofAmerica,1989,86(1):261-277.
[16] Soto-Crespo J M, Nieto-Vesperinas M. Scattering from slightly rough random surfaces:a detailed study on the validity of the small perturbation method [J]. Journal of theOptical Society of America,1990, A7(7):1185-1201.
[17] Valenzuela G R. Depolarization of EM wave by slightly rough surface [J]. IEEETransactions on Antennas and Propagation,1967,15(4):552-557.
[18] Twang L, Kong J A, Shin R T. Theory of microwave remote sensing [M]. New York:Wiley-Interseienee,1985.
[19] Ogilvy J A. Theory of wave scattering from random rough surfaces [M]. NewYork:Adam Hilger,1997.
[20] Voronovich A G. Small slope approximation for electromagnetic wave scattering at arough interface of two dielectric half-spaces [J]. Waves in Random Media,1994,4(3):337-367.
[21] Voronovich A G. Small slope approximation in wave scattering by rough surfaces [J].Sov. Phys.–JETP,1985,62(1):65-70.
[22] Thorsos E I, Broschat S L. An investigation of the small slope approximation forscattering from rough surfaces: Part I-Theory [J]. Journal of the Acoustical Society ofAmerica,1995,97(4):2082-2093.
[23] Voronovich A G. Non-local small slope approximation for wave scattering fromroughsurfaces [J]. Waves Random Media,1996,6(2):151-167.
[24] McDaniel S T. An extension ofthe small slope approximation for roughsurfaceScattering [J]. Waves Random Media,1995,5(2):201-214.
[25] Tatarskii V I. Statistical description of rough surface scattering using the quasi-small-slope approximation for random surfaces with a Gaussian multivariate probabilitydistribution [J]. Waves Random Media,1994,4(2):191-214.
[26] Gilbert M S, Johnson J T. A study of rough surface scattering effects using thehigher-order small slope approximation [C]. International Antennas and PropagationSymposium,2003,1:557-560.
[27] Bahar E. Scattering cross sections for random surfaces: Full Wave Analysis [J]. RadioScience,1981,16(3):331-341.
[28] Bahar E, Lee B S. Radar scattering crross sections for two-dimensional random roughsurfaccs-Full wave solutions and comparisons with experiments [J]. Waves in RandomMedia,1996,6(1):1-23.
[29] Bahar E, Hung G, Lee B S. Electromagnetic scattering and depolarization across roughsurfaces: Full wave analysis [J]. Radio Science,1995,30(3):525-544.
[30] Bahar E, Lee B S. Full wave solutions for rough-surface bistatic radar cross sections:Comparison with small perturbation, physical optics, numerical, and experimentalresults [J]. Radio Science,1994,29(2):407-429.
[31] Collin R E. Full wave theories for rough surface scattering: An updated assessment [J].Radio Science,1994,29(5):1237-1254.
[32] Bahar E. Full wave solutions for the depolarization of the scattered radiation fields byrough surfaces with arbitrary slope [J]. IEEE Transactions on Antennas and Propagation,1981,29(3):443-454.
[33] Collin R E. Scattering of an incident Gaussian beam by a perfectly conducting roughsurface [J]. IEEE Transactions on Antennas and Propagation,1994,42(1):70-74.
[34] Fung A K, Pan G. An integral equation method for rough surface scattering [C]. Proc.Int. Symp. on Multiple Scattering of Waves in Random Media and Random Surface.1986,701-714.
[35] Alvarez P, Jose L. An extension of the IEM/IEMM surface scattering model [J]. Wavesin Random Media,2001,11(3):307-329.
[36] Wu Z S, Zhang Y D. Backscattering enhancement from very rough surfaces based onintegral equation model [C]. Proceedings of the SPIE,2003,4892:455-461.
[37] Hsieh C Y, Fung A K. Application of an extended IEM to multiple surface scatteringand backscatter enhancement [J]. Journal of Electromagnetic Waves and Applications,1999,13(1):121-136.
[38] Hsieh C Y, Fung A K. Nesti G. A further study of the IEM surface scattering model:Remote sensing for a sustainable future [J]. IEEE transactions on Geoscienee andRemote Sensing,1997,35(4):795-979.
[39] Koudogbo F, Combes P F. Numerical and experimental validations of IEM for bistaticscattering from natural and manmade rough surfaces [J]. Progress in EleetromagneticsResearch,2004,46(1):203-244.
[40] Chen K S, Wu T D, Tsay M K. A note on the multiple scattering in an IEM Model [J].IEEE Transactions on Geoscience and Remote Sensing,2000,38(1):249-256.
[41] Johnson J T, Shin R T. A numerical study of the composite surface model for oceanbackscattcring [J]. IEEE Transactions on Geoscience and Remote Sensing,1998,36(1):72-83.
[42] Durden S L, Vesecky J F. A numerical study of the separation wavenumber in the twoscale scattering approximation [J]. IEEE Transactions on Geoscience and RemoteSensing,1990,28(2):271-272.
[43] Plant W J. A two-scale model of short wind generated waves and scatterometry [J]. J.Geophys. Res.,1986,91(C9):10735-10749.
[44] Wang Y H, Guo L X, Wu Z S. Modified two-scale model for electromagnetic scatteringfrom the non-Guassian oceanic surface [J]. Chin. Phys. Lett.,2005,22(11):2808-2811.
[45]郭立新，王运华，吴振森.修正双尺度模型在非高斯海面散射中的应用[J].电波科学学报，2007，22(2):212-218.
[46] Ulaby F T, Dobson M C. Handbook of radar scattering statistics for terrain [M].Norwood: MA: Artech House,1989.
[47] Trunck G V, George S F. Detection of targets in non-gaussian sea clutter [J]. IEEE Trans.AES-6, Sep1970,620-628.
[48] Sekine M, Mao Y. Weibull radar clutter [M]. London: Peter Peregrinus,1990.
[49] Sekine M. Weibull distributed ground clutter [J]. IEEE Trans. AES, Jul1981,17:596-598.
[50] Sekine M. Weibull distributed sea clutter [C]. IEE Proc.130, Pt. F,1983:476.
[51] Schleher D C. Radar detection in weibull clutter [J]. IEEE Trans. AES, Nov1976,12:736-743.
[52] Rey M, Drosopoulos T, Petrovic D. A search procedure for ships in radar sat imagery[R]. Dreo report,1996, No.1305.
[53] Ward K D. A radar sea clutter model and its application to performance assessment [C].IEEE international Radar Confrence,1982:203-207.
[54] Sheen D R, Johnston L P. Statistical and spatial properties of forest clutter measuredwith polarimetric synthetic aperture radar [J]. IEEE Trans. GPS,1992,30(3):578-585.
[55]王盈.相关K分布杂波的建模与仿真[J],信号处理,1997,13(2):141-146.
[56]韩兴斌，胡卫东，郁文贤.海面目标ISAR成像的杂波仿真与抑制[J].现代雷达，2007,29(10):44-48.
[57] Watts S. Cell-averaging CFAR gain in spatially correlated K-distributed clutter [J].Radar, Sonar and Navigation, IEE Proceedings,1996,143(5):321-327.
[58] Wise G L, Traganitis A P, Thomas J B. The effect of a memoryless nonlinearity on thespectrum of a random process [J]. IEEE Trans. Inform. Theory,1977,23(1):84-89.
[59] L J Marier. Correlated K-distributed clutter generation for radar detection and track [J].IEEE Trans. Aerosp. Electron. Syst.,1995,31(2):568-580.
[60] Conte E, Longo M, Lops M. Modeling and simulation of non-Rayleigh radar clutter [J].IEE Proceedings F,1991,138(2):121-130.
[61] Frery A C, Muller H J, Yanasse C F. A model for extremely heterogeneous clutter [J].IEEE Trans. on GRS,1997,35(3):648-659.
[62] Moser G, Zerubia J, Serpico S B. Dictionary-based stochastic expectation-maximizationfor SAR amplitude probability density function estimation [J]. IEEE Trans. on GRS,2006,44(1):188-200.
[63] Eltoft T. Modeling the amplitude statistics of ultrasonic images [J]. IEEE Trans. onMedical Imaging,2006,25(2):229-240.
[64] Moser G, Zerubia J, Serpico S B. SAR amplitude probability density function estimationbased on a generalized Gaussian model [J]. IEEE Trans. on IP,2006,15(6):1429-1442.
[65] Greco M S, Gini F. Statistical analysis of high-resolution SAR ground clutter data [J].IEEE Trans. on GRS,2007,45(3):566-575.
[66]高贵. SAR图像目标ROI自动获取技术研究[D].长沙：国防科技大学博士学位论文，2007:18-55.
[67]时公涛.基于Mellin变换的G0分布参数估计方法[J]，自然科学进展，2009，19(6):677-690.
[68]皇甫堪,陈建文,楼生强.现代数字信号处理[M]，北京:电子工业出版社,2003:282-288.
[69] Tison C. A new statistical model for Markovian classification of urban areas inhigh-resolution SAR images [J]. IEEE Transactions on Geoscience and RemoteSensoring,2004,42:2046-2057.
[70] Nicolas J M, Tupin F. Gamma mixture modeled with second kind statistics applicationto SAR image processing [C]. IEEE conference,2002:2489-249.
[71] Abdelfattah R, Nicolas J M. Interferometric SAR coherence magnitude estimation usingsecond kind statistics [J]. IEEE Trans. on GRS,2006,44(7):1942-1953.
[72] Watts S. The performance of cell-averaging CFAR systems in sea clutter [C], IEEEInternational radar conference,2000,398-403.
[73]胡文琳，王永良，王首勇. Log-normal分布杂波背景下有序统计恒虚警检测器性能分析[J].电子与信息学报，2007，29(3):517-520.
[74] Zaimbashi A, Taban M R, Nayebi M M, Norouzi Y. Weighted order statistic and fuzzyrules CFAR detector for weibull clutter [J], Signal processing,2008,88(3):558-570.
[75]武楠，徐艳国，李宗武.海杂波背景下的双参数单元平均恒虚警检测器[J]，现代雷达，2010，32(2):52-56.
[76]陈建军，黄孟俊，邱伟，赵宏钟，付强.海杂波下的双门限恒虚警目标检测新方法[J].电子学报，2011，39(9):2135-2141.
[77] Haykin S, Puthusserypady S. Chaotic Dynamics of Sea Clutter [M]. John Wiley&Sons,Inc,1999.
[78] Leung H, Haykin S. Neural Network Modeling of Radar Backscatter from an OceanSurface Using Chaos Theory [C]. Adaptive Signal Processing, SPIE Proceedings,1991,1565:279-286.
[79] He N, Haykin S. Chaotic Modeling of Sea Clutter [J]. Electronics Letters,1992,28(22):2076-2077.
[80] Haykin S. Radar Clutter Attractor: Implications for Physics, Signal Processing andControl [J]. IEE Proc-Radar, Sonar Navig,1999,146(2):177-188.
[81] Haykin S, Bakker R, Currie B W. Uncovering Nonlinear Dynamics-the Case Study ofSea Clutter [J]. Proceedings of the IEEE,2002,90(5):860-881.
[82] Leung H, Lo T. Chaotic Radar Signal Processing over the Sea [J]. IEEE Journal ofOceanic Engineering,1993,18(3):287-295.
[83] Haykin S, Li X B. Detection of Signals in Chaos [J]. Proceeding of the IEEE,1995,183(1):94-122.
[84] Chakravarthi P R. Radar Target Detection in Chaotic Clutter [C]. IEEE National RadarConference,1997:367-371.
[85] Morrison A I, Srokosz M A. Estimating of the fractal dimension of the sea surface-a1stattempt [R]. Annales Geophysical-Atmospheres HY drosspheres and Space, Sep11,1993:648-658.
[86]杜干.目标检测的分形方法及应用[D].西安：西安电子科技大学博士学位论文，2000.
[87]石志广.基于统计与复杂性理论的杂波特性分析及信号处理方法研究[D].长沙：国防科学技术大学博士学位论文，2007.
[88] Xu X K. Low observable targets detection by Low Observable Targets Detection byJoint Fractal Properties of Sea Clutter: An Experimental Study of IPIX OHGR Datasets[J], IEEE Transactions on antennas and propagation.2010,58(4):1425-1429.
[89]关键，李秀友，黄勇，张建.基于海杂波分形特性的目标检测新方法[J].电光与控制，2008，15(12):5-9.
[90]刘宁波，李晓俊，李秀友，关键.基于分形自仿射预测的海杂波中目标检测[J].现代雷达，2009，31(4):43-50.
[91]关键，刘宁波，张建，宋杰.海杂波的多重分形关联特性与微弱目标检测[J].电子与信息学报，2010,32(1):54-61.
[92]关键，刘宁波，张建，宋杰.基于LGF的海杂波中微弱目标检测方法[J].信号处理.2010,26(1):69-73.
[93]刘宁波，关键，张建.基于分形可变步长LMS算法的海杂波中微弱目标检测[J].电子与信息学报，2010，32(2):371-376.
[94]张建，关键，刘宁波，宋杰.基于EMD和盒维数的固定微弱目标检测[J].信号处理，2010，26(4):492-496.
[95] Haykin S, Puthusserypady S, Yee P. Dynamic Reconstruction of Sea Clutter UsingRegularized RBF Networks [C]. The thirty-second asilomar conference on signals,systems&computers,1998:19-23.
[96] Leung H, Dubash N and Xie N. Detection of small objects in clutter using a GA-RBFneural network [J]. IEEE transactions on aerospace and electronic systems.2002,38(1):98-116.
[97] Lopez-Risueno G, Grajal J, Diaz-Oliver R. Target detection in sea clutter usingconvolutional neural networks [C]. Proceedings of IEEE radar conference,2003:321-328.
[98]谢红梅.基于混沌理论的信号处理方法研究[D].西安：西北工业大学博士学位论文.2003.
[99]陈瑛，罗鹏飞，曾勇虎.基于RBF神经网络的海杂波建模[J].系统仿真学报，2007，19(3):524-526.
[100] Thayaparan T, Kennedy S. Detection of a manoeuvring air target in sea-clutter usingjoint time–frequency analysis [C], IEE Proceedings-Radar Sonar&Navigation,151(1),2004,19-30.
[101] Davidson G, Griffiths H D. Wavelet detection scheme for small targets in sea clutter [J],Electronics letters,2002,38(19):1128-1130.
[102] Melino R, Tran H T. Application of the Fractional Fourier Transform in the Detection ofAccelerating Targets in Clutter [R], DSTO Defence Science and TechnologyOrganisation, PO Box1500Edinburgh, South Australia5111, Australia,2011.
[103]陈小龙，刘宁波，宋杰，关键，何友.海杂波FRFT域分形特征判别及动目标检测方法[J].电子与信息学报，2011，33(4):823-830.
[104]王福友，袁赣南，周卫东，白文娟.基于时间-Doppler分析的海杂波背景下小目标检测[J]，海洋测绘，2009，29(5):33-36.
[105] Phillips O M. Radar returns from the sea surface [J]. J. Phys. Oceanogr.,1988,18(4):1065-1074.
[106] Farina A, Gini F, Greco M V, Lee P H Y. Improvement factor for real sea-clutterdoppler frequency spectra [J]. IEE Proceedings Radar, Sonar, Navigation,1996,143(5):341-344.
[107] Farina A, Gini F, Greco M V, Verrazzani L. High resolution sea clutter data: statisticalanalysis of recorded live data [J]. IEE Proceedings Radar, Sonar, Navigation,1997,144(3):121-130.
[108] Posner F L. Spiky sea clutter at low grazing angles and high range resolutions [C]. IEEEProceedings of Radar Conference.1998:405-410.
[109] Kulemin G P. Spike characteristics of radar sea clutter for extremely small grazingangles (part1)[R]. Proc. SPIE, Radar Sensor Technology V,2000,4033-006:52-60.
[110] Gutnik, V G, Kulemin G P, Sharapov L I. Spike statistics features of the radar sea clutterin the millimeter wave band at extremely small grazing angles [J]. Physics andEngineering of Millimeter and Sub-Millimeter Waves,2001,1(4):426-428.
[111] Posner F. Spiky sea clutter at high range resolutions and very low grazing angles [J].IEEE Transactions on aerospace and electronic systems,2002,38(1):58-73.
[112] Keller M R, Gotwols B L, Chapman R D. Multiple sea spike definitions: reducing theclutter [C]. IEEE Geoscience and Remote Sensing Symposium,2002,2:940-942.
[113] Posner F, Gerlach K. Sea spike demographics at high range resolutions and very lowgrazing angles [C]. IEEE Proceedings of the Radar Conference,2003:38-45.
[114] Melief H W, H Greidanus, P Genderen, P Hoogeboom. Analysis of sea spikes in radarsea clutter data [J]. IEEE Transactions on geoscience and remote sensing,2006,44(4):985-993.
[115] Liu Y, Frasier S J, Mclntosh R E. Measurement and classification of low-grazing angleradar sea spikes [J]. IEEE Trans on Antennas Propagation,1998,46(1):27-40.
[116] Holliday D, Deraad L L, Stcyr G J. Sea spike backscatter from a steepening wave [J].IEEE Trans on Antennas Propagation,1998,46(1):108-113.
[117] Rino C L, Ngo H D. Numerical simulation of low-grazing angle ocean microwavebackscatter and its relation to sea spikes [J]. IEEE Trans on Antennas Propagation,1998,46(1):133-141.
[118] Sletten M A. Multipath scattering in ultrawide band radar sea spikes [J]. IEEE Trans onAntennas Propagation,1998,46(1):45-56.
[119] Greco M, Stinco P, Gini F. Identification and analysis of sea radar clutter spikes [J]. IETRadar Sonar Navig.,2010,4(2):239-250.
[120] Jaggard D L, Sun X. Scattering from fractally corrugated surfaces[J]. J. Opt. Soc. Amer.A,1990,1131-1139.
[121] Berizzi F. Fractal analysis of the signal scattered from the sea surface [J]. IEEE Trans onAntennas Propagation,1999,47(2):324-338.
[122] Mandelbrot B B. The fractal geometry of nature [M]. San Francisco: Freeman,1982.
[123] Jaggard D L, Kim Y. Diffraction by band-limited fractal screens [J]. J. Opt. Soc. Am. A.,1987,4(6):1055-1062.
[124] Jaggard D L, Sum X. Scattaiag from fractal corrugated surface [J]. J. Opt. Soc. Am. A.,1990,7(6):1131-1139.
[125] Frenceschotti G. Scattering from natural rough surfaces modeled by fractional Brownianmotion two-dimensional processes [J]．IEEE Trans. Antennas Propag.,1999,47(9):1405-1415.
[126] Jakemen E. Scattering by a corrugated random surface with fractal slope [J]. J. Phys. A:Math. Gen.,1982,15(2):55-59.
[127]郭立新，吴振森．二维导体粗糙面电磁散射的分形特征研究[J]．物理学报．2000，49(6):1064-1069.
[128] Guo L X, Jiao L C, Wu Z S．Electromagnetic scattering from two-dimeosional roughsurface using the Kirchhoff approximation [J]. Chin. Phys. Lett,2001,18(1):214-216.
[129]郭立新，吴振森．fBm随机粗糙面电磁散射的微扰法近似[J]．微波学报，2001，17(2):60-66.
[130]郭立新，徐燕，吴振森．分形粗糙海面高斯波束散射特性模拟[J]．电子学报，2005，33(3):534-537.
[131]郭立新，金彩英．脉冲波入射时分形粗糙海面的双频散射截面和脉冲展宽[J]．红外与毫米波学报，2003，22(2):132-136.
[132]杨超，郭立新.粗糙海面电磁散射的小斜率近似方法[J].系统工程与电子技术，2009，31(8):1814-1818.
[133]王运华，郭立新，吴振森.改进的一维分形模型在海面电磁散射中的应用[J].电子学报，2007，35(3):478-483.
[134] Lemaire D, Sobieski P, Guissard A. Scattering by the ocean surface under nonfullydeveloped state: Two scale model validation and inversion of radar measurements [J]. InProc.3rd ERS Symp., Florence, Italy, Mar.1997:6.
[135] Donelan M A, Pierson W. Radar scattering and equilibrium ranges in wind-generatedwaves with application to scatterometry [J]. J. Geophys. Res.,1987,92(5):4971-5029.
[136] Hasselmann K, Ross D B, Muller P, Sell W. A parametric wave prediction model [J]. J.Phys. Oceanogr.,1976,2:200-228.
[137] Guissard A, Baufays C, Sobieski P. Fully and nonfully developed sea models formicrowave remote sensing applications [J], Remote Sens. Environ.,1994,48:25-38.
[138] Toba Y. Local balance in the air-sea boundary processes: On the spectrum of windwaves [J]. J. Oceanogr. Soc. Jpn.,1973,29:209-220.
[139] Mitsuyasu H. Measurement of the high-frequency spectrum of ocean surface waves [J]. J.Phys. Oceanogr.,1977,7:882-891.
[140] Hara T, Bock E J, Donelan M. Frequency-wavenumber spectrum of wind-generatedgravity-capillary waves [J]. J. Geophys. Res.,1997,102:1061-1072.
[141] Hwang P A, Trizna D B, Wu J. Spatial measurements of short wind waves using ascanning slope sensor [J]. Dyn. Atmosph. Oceans,1993,20:1-23.
[142] Jahne B, Riemer K S. Two-dimensional wavenumber spectra of small-scale watersurface waves [J]. J. Geophys. Res.,1990,95(C7):11531-11546.
[143] Klinke J, Jahne B.2D wavenumber spectra of short wind waves-Results from wind wavefacilities and extrapolation to the ocean [J]. Opt. Air-Sea Interface,1992,1749:245-257.
[144] Pierson W J, Stacy R A. The elevation, slope and curvature spectra of a wind roughenedsea surface [R]. Final Rep. NASA-CR-2247, CUNY Inst. Marine Atmosph. Sci., CityUniv., New York,1973:129.
[145] Cox C S. Measurements of slopes of high frequency wind waves [J]. J. Marine Res.,1958,16(3):199-229.
[146] Long M W. Radar reflectivity of land and sea [M].2nd ed. Artech House,1983.
[147] Chen K S, Fung A K, Weissman D E. A backscattering model for ocean surface [J].IEEE Trans. Geosci. Remote Sens.,1992,30(4):811-817．
[148] Guissard A. Multispectra for ocean-like random rough surface scattering [J]. J.Electromagn. Waves Appl.,1996,10:1413-1443.
[149] George S. The detection of monfluctuating targets in log-normal clutter [R]. NRL Report,1968, No.6796.
[150] Fay F A, Clarke J, Peters R S. Weibull distribution applied to sea-clutter [C]. Proc. IEEConf. Radar, London,1977:101-104.
[151] Rangaswamy M, Weiner D. Computer generation of correlated non-gaussian radarclutter [J]. IEEE Trans. Aerosp. Electron. Syst.,1995,3l(1):106-115.
[152] Yanhui H, Feng L, Baobao Zhang. Simulation of coherent correlation K-distribution seaclutter based on SIRP [C]. Radar CIE Conference2006:1-4.
[153]周万幸，基于SIRP的时空相关K分布海杂波仿真研究，电子学报，2009，37(12):2672-2676.
[154] Armstrong B C, Griffiths H D. Modeling Spatially Correlated K-Distributed RandomClutter[J]. Electron. Letters,1991,27(15):1355-1356.
[155] Freitas C C, Frery A C, Correia A H. The polarimetric G distribution for SAR dataanalysis. Environmetrics,2005,16(1):13-31.
[156]石志广，周剑雄，付强. K分布海杂波参数估计方法研究.信号处理，2007，3:420-424.
[157] Blacknell D, Tough R J A. Parameter estimation for the K-distribution based on [z log(z)][C]. IEE Proc.-Radar.Sonar Naving,2001, No.6.
[158] Iskander D R, Zoubir A M. Estimation of the Parameters of the K-Distribution UsingHigher Order and Fractional Moments [C]. IEEE Trans. on AES,1999, No.4.
[159] DeVore M D. Analytical performance evaluation of SAR ATR with inaccurate orestimated models [J]. Proceedings of SPIE,2004,5427:407-417.
[160] DeVore M D, O’Sullivan J A. Quantitative statistical assessment of conditional modelsfor synthetic aperture radar [J]. IEEE Trans. on IP,2004,13(2):113-124.
[161] Fung A K. Microwave scattering and emission models and their application[M]. ArtechHouse,1994.
[162]张剑.基于插件技术的雷达信号仿真软件的设计与实现[D]，长沙：国防科学技术大学硕士学位论文，2009.
[163]丁鹭飞，耿富录.雷达原理[M]，西安:西安电子科技大学出版社,2002:128-129.
[164]于宵辉.基于平台/插件软件结构的雷达视频回波模拟器的研究与实现[D]，长沙：国防科学技术大学硕士学位论文，2009.
[165] Irina Antipov. Simulation of sea clutter returns[R]. Defence science and technologyorganisation, Australia, ADA352675,1998.
[166] Billingsley J B, Larrabee J F. Multifrequency measurements of radar ground clutter at42Sites[R]. MIT Lincoln Laboratory, ADA312399,1996.
[167]刘剑，赵艳丽，罗鹏飞.基于海杂波混沌特性的目标检测[J].现代雷达，2004，26(2):56-60.
[168]陈瑛，罗鹏飞，海杂波背景下基于RBF神经网络的目标检测[J].雷达科学与技术，2005，3(5):271-275.
[169]陈晓楠，许小可，索继东，基于径向基函数的海杂波时间序列预测方法[J].大连海事大学学报，2007，33(1):115-118.
[170] Hu J, Gao J B, Posner F L, Zheng Y, Tung W W. Target detection within sea clutter: acomparative study by fractal scaling analyses [J], Fractals,2006,14(3):187-204.
[171] Gao J B, Cao Y H, Tung W W, Hu J. Multiscale analysis of complex time series [M].New York: Wiley,2007.
[172] Hu J, Tung W W, Gao J B. Detection of Low Observable Targets Within Sea Clutter byStructure Function Based Multifractal Analysis [J]. IEEE Transactions on antennas andpropagation,2006,54(1):136-143.
[173] Talkner P, Weber R O. Power spectrum and detrended fluctuation analysis: applicationto daily temperatures [J]. Phys. Rev. E,2000,62:150-160.
[174]王福友，卢志忠，袁赣南，周卫东.基于时空混沌的海杂波背景下小目标检测[J].仪器仪表学报，2009，30(6):1180-1185.
[175] Jaeger H. The echo state approach to analysing and training recurrent neural networks[R]. Tech. Rep. Fraunhofer Institute for Autonomous Intelligent Systens: GermanNational Research Center for Information Technology (GMD Report148),2001.
[176] Jarger H, Haas H. Harnessing nonlinearity: Predicting chaotic systems and saving energyin wireless communication [J]. Science,2004,304(5667):78-80.
[177]袁长清，李俊峰，邓志东，宝音贺西.基于ESN网络的航天器姿态跟踪鲁棒控制[J].清华大学学报，2008,48(8):1362-1367.
[178]史志伟，韩敏. ESN岭回归学习算法及混沌时间序列预测[J].控制与决策，2007，22(3):258-267.
[179]彭宇，王建民，彭喜元.基于回声状态网络的时间序列预测方法研究[J].电子学报，2010，38(2A):148-154.
[180]韩敏，穆大芸.回声状态网络LM算法及混沌时间序列预测，控制与决策，2011，26(10):1469-1472.
[181] Gunturkun U. Toward the Development of radar scene analyzer for cognitive radar [J].IEEE journal of oceanic engineering,2010,35(2):303-313.
[182] Xue Y B, Yang L, Haykin S. Decoupled echo state networks with lateral inhibition [J].Neural Networks,2007,20:365-376.
[183] Jaeger H. Adaptive nonlinear system identification with echo state networks [C]. In S. T.S. Becher,&K. Obermayer (Eds.), Advances in neural information processing systems,Cambrige, MA: MIT Press,2003:593-600.
[184]时飞飞.基于改进ESN的高炉煤气系统预测方法的研究[D]，大连：大连理工大学硕士学位论文，2009.
[185] Ishii K, Zant T van der, Becanovic V, Ploger P. Identification of motion with echo statenetwork [C]. In Proc. OCEANS’04,2004:1205-1210.
[186] Ploger P G, Arghir A, Gunther T, Hosseiny R. Echo state networks for mobile robotmodeling and control [C]. In Proc. RoboCup,2003:157-168.
[187] Schmidhuber J, Gagliolo M, Wierstra D, Gomez F. Evolino for recurrent support vectormachines [R]. Technical report no. IDSIA-19-15, IDSIA/USI-SUPSI, Manno,Switzerland,2005.
[188] Fischler M A, Firschein O. Intelligence: The eye, the brain and the computer, Reading[M], MA: Addison-Wesley,1987.
[189] Diamantaras K I, Kung S K. Principal component neural network: theory and application
[M]. New York: Wiley,1996.
[190] Cernansky M, Tino P. Predictive modeling with echo state networks [EB/OL].http://www2.fiit.stuba.sk/~cernans/main/publications/icann2008_paper_cernansky.pdf,2008.
[191] Posner F, Gerlach K. Sea spike demographics at high range resolutions and very lowgrazing angles [C]. IEEE Radar Conference,2003:38-45.
[192] Greco M, Stinco P, Gini F. Identification and analysis of sea radar clutter spikes [J]. IETRadar Sonar Navig.,2010,4(2):239-250.