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
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摘要
现代战场环境复杂多变以及信息化战争对雷达信息处理能力要求的提高使得杂波下的信息处理问题尤为关键,而高分辨雷达技术的发展改变了雷达杂波特性,给杂波下的信息处理技术提出了新的挑战。本文结合背景需求,针对高分辨雷达杂波特性分析与建模以及杂波下的信号处理方法两大问题展开研究,主要内容包括以下四个方面:
第二章基于统计和随机过程理论对实测海杂波数据进行特性分析。首先总结了杂波统计特性分析的基本理论和方法,重点分析比较了K分布杂波模型不同参数估计方法的性能,提出了基于粒子滤波器的K分布参数估计新方法;其次,结合项目背景,对实测海杂波数据进行分布拟合检验、时空相关特性以及谱特性分析,并基于SIRP/ZMNL实现了时空二维相关K分布杂波的仿真。
第三章基于复杂性理论对高分辨海杂波数据的非线性和复杂性进行研究。首先简单回顾复杂性理论的发展历史与主要内容,定性分析了海杂波的复杂性特点;其次运用相空间重构技术、替代数据方法、递归图方法以及复杂性测度理论等对海杂波时间序列的非线性、可预测性、复杂性进行定量分析;再次,对海杂波的多重分形特性进行实验判定,分析了海杂波多重分形特性的的变化规律,提出了杂波的小波多重分形模型及其仿真方法;最后利用一类非线性时频分析方法-希尔波特-黄(HHT)变换对杂波数据进行分析,提出了基于HHT能量分布特征的杂波下目标鉴别新方法。
第四章研究非高斯杂波下的小目标检测问题,主要探讨了几类新的检测方法。①基于RBF神经网络非线性预测的检测方法,提出先通过小波分析降噪,提高数据的可预测性,再用目标单元数据进行神经网络训练的方法;②K分布杂波下基于随机共振的小目标检测方法,对K分布杂波下的最优检测处理器进行了推导,并与基于阈值处理的随机共振系统进行对比,提出基于阈值随机共振的次优检测方法;③提出了分数阶循环平稳信号的概念,将其应用于Alpha-Stable分布杂波下LFM信号的检测与估计。
第五章研究杂波下一维散射中心参数估计以及目标的运动参数估计问题。首先研究了强杂波背景下GTD散射中心模型的定阶与参数估计问题,提出基于贝叶斯原理和RJ-MCMC算法的GTD模型联合参数估计与定阶方法;其次,针对非高斯杂波中尖峰数据的存在,采用基于M估计的稳健算法进行散射中心参数估计,并利用协同PSO算法解决M估计中的优化问题;最后研究了非高斯杂波下目标运动参数估计问题,提出一种可应用于“静态参数”序贯估计的粒子滤波器改进算法。
最后对本文内容进行了系统的总结,对论文中需要进一步研究的工作进行了探讨。
The radar signal processing in clutter becomes a vital problem in the information based modern war because of the complex and variable battlefield and the increasing demand on the information processing ability of radar.Moreover,the characteristic of radar clutter has changed as the resolution of radar increases,and this puts forward a new challenge for modern radar signal processing methods.Evoked by the practical demand of several projects, we carry out research on the clutter characteristic of high range resolution radar and the signal processing method under clutter.The work contains four parts:
The second chapter analyzes the statistical characteristic of some measured sea clutter data.Firstly,the fundamental theory and method for analyzing the statistical characteristic of radar clutter is introduced.Different methods for estimating the model parameters of K-distributed clutter are compared and a new estimation method based on particle filter is proposed.Secondly,the statistical characteristic of the measured sea clutter data in two special projects is analyzed,including distribution fitting,time-space correlation analysis and spectrum analysis.The simulation method of spatial-temporal correlated clutter is realised based on SIRP and ZMNL.
The third chapter studies the complex characteristic of high range resolution sea clutter based on the complexity theory.Firstly,the evolution and principle of the complexity theory is briefly introduced,and the complexity of sea clutter is analyzed.Secondly,the nonlinearity of the sea clutter in time domain is testified by phase space reconstruction and surrogate data method;the predictability and deterministic of high resolution sea clutter is analyzed using recurrence plot;the complexity of clutter and target echo is analyzed quantificationally using complexity measuring theory.Thirdly,the multi-fractal of sea clutter is tested experimentally; the varying property of the multi-fractal is discussed;and the simulation method of sea clutter based on wavelet-multi-fractal model is proposed.Lastly,we use a new time-frequency analysis method,i.e.,the Hilbert-Huang Transform(HHT),to compare the decomposed characteristic of clutter and target echo.A new way for target-clutter discrimination is proposed based on the different HHT energy characteristic of the target and the clutter.
Chapter 4 focuses on the detection of small targets in non-Gaussian clutter.Several new detection methods are studied:①Detection method based on RBF neural network.We propose an improved algorithm which has a denoising step using wavelet decomposition and trains the network by target bins.②Small target detection method in K-distributed clutter based on stochastic resonance.The optimal detector under K-distributed clutter is deduced.A sub-optimal detector based on the quantizers is also proposed by comparing the optimal detector with the multilevel quantizers.③A detector based on the proposed principle of fractional lower order cyclostationary sigal is used to detect and estimate the LFM signals in alpha-stable clutter.
Chapter 5 studies the parameter estimation problem in clutter,i.e.,estimating scattering center parameters from high resolution range profiles and estimating the motion parameter of targets in clutter.Firstly,the joint model selection and parameter estimation problem of GTD model under heavy noise is studied and a new method based on Bayes principle and RJ-MCMC algorithm is proposed.Secondly,a robust M-estimator is proposed to suppress the spiky data in non-Gaussian clutter when estimating scattering center parameters and the CPSO(co-operative particle swarm optimization) algorithm is used in the optimization process of the M-estimator.Lastly,the motion parameter estimation problem of targets in non-Gaussian clutter is studied and an improved algorithm based on the particle filter for the sequential estimation of the "static parameters" is proposed.
A systematic conclusion together with some further discussion on future work is given at the end of this dissertation.
第二章基于统计和随机过程理论对实测海杂波数据进行特性分析。首先总结了杂波统计特性分析的基本理论和方法,重点分析比较了K分布杂波模型不同参数估计方法的性能,提出了基于粒子滤波器的K分布参数估计新方法;其次,结合项目背景,对实测海杂波数据进行分布拟合检验、时空相关特性以及谱特性分析,并基于SIRP/ZMNL实现了时空二维相关K分布杂波的仿真。
第三章基于复杂性理论对高分辨海杂波数据的非线性和复杂性进行研究。首先简单回顾复杂性理论的发展历史与主要内容,定性分析了海杂波的复杂性特点;其次运用相空间重构技术、替代数据方法、递归图方法以及复杂性测度理论等对海杂波时间序列的非线性、可预测性、复杂性进行定量分析;再次,对海杂波的多重分形特性进行实验判定,分析了海杂波多重分形特性的的变化规律,提出了杂波的小波多重分形模型及其仿真方法;最后利用一类非线性时频分析方法-希尔波特-黄(HHT)变换对杂波数据进行分析,提出了基于HHT能量分布特征的杂波下目标鉴别新方法。
第四章研究非高斯杂波下的小目标检测问题,主要探讨了几类新的检测方法。①基于RBF神经网络非线性预测的检测方法,提出先通过小波分析降噪,提高数据的可预测性,再用目标单元数据进行神经网络训练的方法;②K分布杂波下基于随机共振的小目标检测方法,对K分布杂波下的最优检测处理器进行了推导,并与基于阈值处理的随机共振系统进行对比,提出基于阈值随机共振的次优检测方法;③提出了分数阶循环平稳信号的概念,将其应用于Alpha-Stable分布杂波下LFM信号的检测与估计。
第五章研究杂波下一维散射中心参数估计以及目标的运动参数估计问题。首先研究了强杂波背景下GTD散射中心模型的定阶与参数估计问题,提出基于贝叶斯原理和RJ-MCMC算法的GTD模型联合参数估计与定阶方法;其次,针对非高斯杂波中尖峰数据的存在,采用基于M估计的稳健算法进行散射中心参数估计,并利用协同PSO算法解决M估计中的优化问题;最后研究了非高斯杂波下目标运动参数估计问题,提出一种可应用于“静态参数”序贯估计的粒子滤波器改进算法。
最后对本文内容进行了系统的总结,对论文中需要进一步研究的工作进行了探讨。
The radar signal processing in clutter becomes a vital problem in the information based modern war because of the complex and variable battlefield and the increasing demand on the information processing ability of radar.Moreover,the characteristic of radar clutter has changed as the resolution of radar increases,and this puts forward a new challenge for modern radar signal processing methods.Evoked by the practical demand of several projects, we carry out research on the clutter characteristic of high range resolution radar and the signal processing method under clutter.The work contains four parts:
The second chapter analyzes the statistical characteristic of some measured sea clutter data.Firstly,the fundamental theory and method for analyzing the statistical characteristic of radar clutter is introduced.Different methods for estimating the model parameters of K-distributed clutter are compared and a new estimation method based on particle filter is proposed.Secondly,the statistical characteristic of the measured sea clutter data in two special projects is analyzed,including distribution fitting,time-space correlation analysis and spectrum analysis.The simulation method of spatial-temporal correlated clutter is realised based on SIRP and ZMNL.
The third chapter studies the complex characteristic of high range resolution sea clutter based on the complexity theory.Firstly,the evolution and principle of the complexity theory is briefly introduced,and the complexity of sea clutter is analyzed.Secondly,the nonlinearity of the sea clutter in time domain is testified by phase space reconstruction and surrogate data method;the predictability and deterministic of high resolution sea clutter is analyzed using recurrence plot;the complexity of clutter and target echo is analyzed quantificationally using complexity measuring theory.Thirdly,the multi-fractal of sea clutter is tested experimentally; the varying property of the multi-fractal is discussed;and the simulation method of sea clutter based on wavelet-multi-fractal model is proposed.Lastly,we use a new time-frequency analysis method,i.e.,the Hilbert-Huang Transform(HHT),to compare the decomposed characteristic of clutter and target echo.A new way for target-clutter discrimination is proposed based on the different HHT energy characteristic of the target and the clutter.
Chapter 4 focuses on the detection of small targets in non-Gaussian clutter.Several new detection methods are studied:①Detection method based on RBF neural network.We propose an improved algorithm which has a denoising step using wavelet decomposition and trains the network by target bins.②Small target detection method in K-distributed clutter based on stochastic resonance.The optimal detector under K-distributed clutter is deduced.A sub-optimal detector based on the quantizers is also proposed by comparing the optimal detector with the multilevel quantizers.③A detector based on the proposed principle of fractional lower order cyclostationary sigal is used to detect and estimate the LFM signals in alpha-stable clutter.
Chapter 5 studies the parameter estimation problem in clutter,i.e.,estimating scattering center parameters from high resolution range profiles and estimating the motion parameter of targets in clutter.Firstly,the joint model selection and parameter estimation problem of GTD model under heavy noise is studied and a new method based on Bayes principle and RJ-MCMC algorithm is proposed.Secondly,a robust M-estimator is proposed to suppress the spiky data in non-Gaussian clutter when estimating scattering center parameters and the CPSO(co-operative particle swarm optimization) algorithm is used in the optimization process of the M-estimator.Lastly,the motion parameter estimation problem of targets in non-Gaussian clutter is studied and an improved algorithm based on the particle filter for the sequential estimation of the "static parameters" is proposed.
A systematic conclusion together with some further discussion on future work is given at the end of this dissertation.
引文
[1]黄培康 殷红成 许小剑.雷达目标特性.北京:电子工业出版社,2004
[2]Merrill I.Skolnik主编.雷达手册2~(nd).王军等译.北京:电子工业出版社,2003.
[3]丁鹭飞 耿富录 编著.雷达原理.3~(nd).西安电子科技人学出版社,2002
[4]Merrill I.Skolnik著.雷达系统导论.3~(nd).左群声等译.北京:电子工业出版社.2006
[5]S.Haykin,et al.Signal processing:where physics and mathematics meet.IEEE SP Mag,Jul,2001:6-7
[6]S.Haykin,et al.Chaotic Dynamics of sea clutter.Chaos,1997,4:777-802
[7]S.Haykin,et al.Detection of signals in chaos.Proc IEEE,1995,83:95-122
[8]Nathanson F.E.Radar design principles.2~(nd).New York:McGraw-Hill,1991
[9]Horst M.M,F.B.Dyer,M.T.Tuley.Radar sea clutter model.London:Inte Conf on Ant And Prop.IEE Pub.1978.No.169,Pts.1 and 2.
[10]Morchin W.Radar engineer's sourcebook.Norwood,MA:Artech House,1993.
[11]Watts S,Ward D.K.Spatial correlation in K-distribution sea clutter,IEE.Proc,1987.134(6):526-532
[12]Sheen D.R.,Johnston L.P.Statistical and spatial properties of forest clutter measured with polarimetric synthetic aperture radar.IEEE Trans.GPS,1992.30(3):578-585
[13]罗贤云,孙芳等.雷达地杂波的测试与分析.现代雷达,1994.16(4):10-22
[14]Ulaby F.T.,M.C.Dobson.Handbook of radar scattering statistics for terrain.Norwood:MA:Artech House,1989
[15]G.V.Trunck,S.F.George.Detection of targets in non-gaussian sea clutter.IEEE Trans.AES-6,Sep 1970:620-628
[16]M.Sekine,Y.Mao.Weibull radar clutter.London:Peter Peregrinus,1990
[17]Sekine,M.,et al.Weibull distributed ground clutter.IEEE Trans.AES,Jul 1981.17:596-598
[18]Sekine,M.,et al.Weibull distributed sea clutter,IEE Proc.130,Pt.F,1983:476
[19]Schleher,D.C.Radar detection in weibull clutter.IEEE Trans.AES,Nov 1976.12:736-743
[20]Jakeman E.,P.N.Pusey.A model for non-rayleigh sea echo.IEEE Trans.AES,Nov 1976.24:806-814
[21]M.D Bisceglie,C.Galdi.Random walk based characterisation of radar backscatter from the sea surface,lEE Proc RSN,Aug 1998.145(4):216-225
[22]R.J.A Tough,K.D.Ward.Diffusive and related models of some properties of sea clutter.J.Phys.:Condens Matter.14,2002:7737-7748
[23]C.J.Baker.K-distributed coherent sea clutter,IEE Proc.138,Pt.F,Apr 1991:82-92
[24]V.Anastassopoulos,et al.High resolution radar clutter statistics.IEEE Trans.AES,1999.35(1):43-60
[25]V.Anastassopoulos,et al.A generalized compound model for radar clutter.IEEE National Radar Conference,Atlanta,GA,Mar 1994:29-31
[26]Baker C.J.K-distributed coherent sea clutter,IEE Proc.138,Pt.F.Apr 1991:82-92
[27]Watts S.Radar detection prediction in k-distributed sea clutter and thermal noise.IEEE Trans.AES,Jan 1987.23:40-45
[28]B.C.Armstrong,H.D.Griffiths.CFAR detection of fluctuating targets in spatially correlated K-distributed clutter.IEE P ROC-F,APRIL 1991.138(2):139-152
[29]G.V.Trunk,S.F.George.Detection of targets in nonogaussian sea clutter.IEEE Trans.AES,Sep 1970.6:620-628
[30]W.David,J.Stein.Detection of Random Signals in Gaussian Mixture Noise.IEEE Trans IT,Nov 1995.41(6):1788-1801
[31]Trunk G.V.Radar properties of non-rayleigh sea clutter.IEEE Trans AES,1972.8:196-204
[32]Oliver C.J.Representation of radar sea clutter.IEE Proc.135,Pt.F,Dec 1988:497-500
[33]G.A.Tsihrintzis,C.L.Nikias.Evaluation of fractional,lower-order statistics-based detection algorithms on real radar sea-clutter data.IEE Proc RSN.Feb 1997.144(1):29-37
[34]邱天爽 张旭秀 等.统计信号处理.北京:电子工业出版社.2004
[35]Shlyakhin V.M.Probability models of non-rayleigh fluctuations of radar signals(a review).Soviet J.Communications Technology and Electronics.January 1988.33:1-16
[36]王盈等.相关K分布杂波的建模与仿真,信号处理,1997.13(2):141-146
[37]Irina antipov.Simulation of sea clutter returns.DSTO-TR-0679,1998
[38]吴翊 李永乐 胡庆军.应用数理统计.国防科技大学出版社,1995:70-101
[39]于寅.高等工程数学.第二版.华中理工大学出版社,1995:468-516
[40]A.Farina,et al.High resolution sea clutter data:statistical analysis of recorded live data.IEE Proc.RSN,June 1997.144(3):121-130
[41]M.V.Menon.Estimation of the shape and scale parameters of the Weibull distribution,Technometrics,1963,15:175-182.
[42]D.Blacknell,R.J.A.Tough.Parameter estimation for the K-distribution based on[z log(z)].IEE Proc.RSN,Dec 2001.148(6):309-312
[43]D.R.Iskander,A.M.Zoubir.Estimation of the Parameters of the K-Distribution Using Higher Order and Fractional Moments.IEEE Trans.AES,Oct 1999.35(4):1453-1457
[44]D.R.Iskander,A.M.Zoubir.Estimating the parameters of the k-distribution using the ml/mom approaoh.IEEE TENCON 1996:769-774
[45]M.Jahangir,D.Blacknell,R.G.White.Accurate approximation to the optimum parameter estimate for K-distributed clutter.IEE Proc RSN,Dec 1996.143(6):383-390
[46]Billingsley J.B.Radar ground clutter measurements and models Part I:Spatial amplitude statistics.In AGARD Conference Proceedings 501,Otawa,Canada,May 1991:1-15
[47]J.B.Billingsley,A.Farina,F.Gini,M.V.Greco,L.Verrazaani.Statistical analysis of measured radar ground clutter data.IEEE Trans.AES,1999.35(2):579-593
[48]H.C.Chan.Radar sea clutter at low grazing angles.IEE Proc.Pt.F,Apr 1990.137(2): 102-112
[49]A.Ozturk,et al.A New Statistical Goodness of fit Test Based on Graphical Representation.Technical Report.No.52.Department of Mathematics,Syracuse University,Syracuse,NY,1990
[50]Slaski,Murali R.L.K.A new efficient algorithm for pdf approximation.RL-TR-97-73(ADA329961) In-House Report,Aug 1997
[51]I.Antipov.Statistical Analysis of Northern Australian Coastline Sea Clutter Data.DSTO-TR-1236(ADA401323).2002.
[52]G.Davidson,H.D.Griffiths.Wavelet detection scheme for small targets in sea clutter.ELECTRONICS Let,12th Sep 2002.38(19):1128-1130
[53]S.Primak.Generation of compound non-Gaussian random processes with a given correlation function.Phys review E.Jan 2000.61(1):100-103
[54]Rangaswarmy M.,Weiner D.Computer generation of correlated non-guassian radar clutter.IEEE Trans.AES,1995.31(1):106-115
[55]Liu B.et al.Generation of a random sequence giving a jointly specified marginal distribution and auto covariance,IEEE Trans.ASSP,1982.30(2):973-983
[56]L.J.Marier.Correlated K-distributed clutter generation for radar detection and track.IEEE Trans.AES,1995.31(2):568-580
[57]姜卫东.光学区雷达目标结构成像的理论及其在雷达目标识别中的应用.国防科学技术大学博士学位论文,2000
[58]陈彦辉.雷达信号检测的分形方法研究.西安电子科技大学博士学位论文,2000
[59]姚纪欢.粗糙海面的电磁散射研究.西安电子科技大学博士学位论文,2000
[60]王俊.微弱目标信号积累检测的方法研究.西安电子科技大学博士学位论文,1999
[61]张嗣瀛.复杂系统与复杂性科学简介.青岛大学学报,2001.16(4):25-28
[62]金吾伦 郭元林.复杂性科学及其演变.复杂系统与复杂性科学,2004.1(1):1-5
[63]方锦清.令人关注的复杂性科学和复杂性研究.自然杂志,24(1):7-15
[64]刘秉正.非线性动力学.高等教育出版社,2004
[65]A.L.Lachinski.Land Warfare and Complexity,Part Ⅰ/Ⅱ(ADA362620;ADA362621).1999
[66]H.Leung,T.Lo.Chaotic radar signal-processing over the sea.IEEE J.OE,1993.18:287-295
[67]H.Leung.Experimental modeling of electromagnetic wave scattering for an ocean surface based on chaotic theory.Chaos,Solitons,Fractals.1992 2:25-43
[68]何建华等.基于混沌和神经网络的弱信号检测.电子学报,1998.26(10):33-37
[69]H.Heung,G.Hennessey,A.Drosopoulos.Signal Detection Using Radial Basis Function Coupled Map Lattice.IEEE Trans.NN,2000,11(5):1133-1137
[70]G.Hennessey,et al.Sea-Clutter Modeling Using a Radial Basis Function Neural Network.IEEE J.OE,Jul 2001.26(3):358-372
[71]M.McDonald,A.Damini.Limitations of nonlinear chaotic dynamics in predicting sea clutter returns.IEE Proc.RSN,Apr 2004.151(2):105-113
[72]S.Haykin,R.Bakker,B.W.Currie.Uncovering Nonlinear Dynamics-The Case Study of Sea Clutter.Proc of the IEEE,May 2002.90(5):860-881
[73]Nan Xie,H.Leung,Hing Chan.A Multiple-Model Prediction Approach for Sea Clutter Modeling.IEEE Trans.GRS,Jun 2003.41(6):1491-1502
[74]J.I.Butterfield.Fractal Interpolation of Radar Signatures for Detecting Stationary Targets in Ground Clutter.IEEE AES Systems Mag,Jul 1991:10-14
[75]T.LO,H.Heung,J.Litva,S.Haykin.Fractal characterization of sea scattered signals and detection of sea-surface targets.IEE Proc.-F,1993.140(4):243-250
[76]M.Martorella,F.Berizzi,E.D.Mese.On the fractal dimension of sea surface backscattered signal at low grazing angle.IEEE Trans.AP,2004.52:1193-1204
[77]P.Gambaoo,A.Garzelliooo,M.Martorellao,F.D.Acquaoo,F.Berizzio.Fractal and multifractal analysis of sea SAR clutter data.0-7803-7031-7/01.2001 IEEE:2430-2432
[78]薛笑荣等.基于分形理论的SAR图像分割.计算机工程与应用,2003.24:27-29
[79]张弓 曹俊纺.扩展分形在SAR图像特定尺寸目标检测中的应用.南京航空航天大学学报.2004.36(3):378-382
[80]M.Salmasil,et al.Performance Analysis of Fractal Detector for a General Model of HRR Signals.RADAR 2004-International Conference on Radar Systems
[81]Chih-ping LIN,M.Sano,S.Sayama,M.Sekine.Detection of Radar Targets Embedded in Sea Ice and Sea Clutter Using Fractals,Wavelets,and Neural Networks.IEICE TRANS.Commun,Sep 2000.E83-B(9):1916-1929
[82]Yi Zheng,Jianbo Gao,Kung Yao.Multiplicative multifractal modeling of sea clutter.0-7803-8882-8/05.IEEE 2005
[83]F.Berizzi,et al.Fractal approach for sea clutter generation.IEE Proc.RSN,Aug 2000.147(4):189-198.
[84]杜干.目标检测的分形方法及应用.西安电子科技大学博士学位论文,2000
[85]鲜明等.基于混沌、多重分形理论的雷达信号分析和目标识别.电子科学学刊,1998.20(4):433-439
[86]黎湘等.基于分形IFS的运动目标RCS序列分解.电子学报,1999.27(9):12-15
[87]李晓辉等.扩展目标角闪烁非线性检验及其抑制技术.系统工程与电子技术,2004.126(15):592-693
[88]Farina A.,A.Russo,F.A.Studer.Coherent radar detection in log-normal clutter.IEE Proc.133,Pt.F.Feb 1986:39-54
[89]Farina A.,et al.Theory of radar detection in coherent weibull clutter.IEE Proc.134,Pt.F.Apr 1987:174-190
[90]Sangston K.J.Coherent detection of radar targets in k-distributed,correlated clutter.NRL,Washington,D.C.Report 9130,Aug 1988
[91]何友等.雷达自动检测与恒虚警处理.清华大学出版社,1999
[92]F.Gini,et al.Estimation of Chirp Radar Signals in Compound-Gaussian Clutter:A Cyclostationary Approach.IEEE Trans SP,Apr 2000.48(4):1029-1039
[93]A.A.Saha,G.V.Anand.Design of detectors based on stochastic resonance.Signal Processing,2003.83:1193-1212.
[94]H.Heung,N.Dubash,Nan XIE.Detection of Small Objects in Clutter Using a GA-RBF Neural Network.IEEE Trans.AES,Jan 2002.38(1):98-118
[95]Hu Weidong,Yu Wenxian,Luo Aimin.Fractal detection of radar weak targets.NAECON 1995.ISSN:0-7803-2666-0/95:140-144
[96]马晓岩等.海杂波中基于混沌预测的目标检测方法改进.电子学报,2003.6:907-910
[97]刘剑等.基于海杂波混沌特性的目标检测.现代雷达,2004.26(2):56-60
[98]杜鹏飞等.混沌海杂波背景下的弱信号检测.系统工程与电子技术,2002.24(7):65-98
[99]李月.混沌振子检测引论.电子工业出版社,2004
[100]F.Gini,M.Montanari,L.Verrazzani.Maximum likelihood,ESPRIT,and periodogram frequency estimation of radar signals in K-distributed clutter.SP,2000.80:1115-1126
[101]F.Gini.A Radar Application of a Modified Cramer-Rao Bound:Parameter Estimation in Non-Gaussian Clutter.IEEE Trans SP,Jul 1998.46(7):1945-1953
[102]A.Dogandzic,Jian Wang,A.Nehorai.Maximum.Likelihood Estimation of Compound-Gaussian Clutter and Target Parameters.ADA433621
[103]R.J.Kozick,B.M.Sadler.Maximum-Likelihood Array Processing in Non-Gaussian Noise with Gaussian Mixtures.IEEE Trans.SP,Dec 2000.48(12):3520-353
[104]T.W.Hilands,et al.Nonlinear Filtering Methods for Harmonic Retrieval and Model Order Selection in Gaussian and Non-Gaussian Noise.IEEE Trans.SP,Apr 1997.45(4):982-995
[105]D.S.Lee,N.K.K.Chia.A Particle Algorithm for Sequential Bayesian Parameter Estimation and Model Selection.IEEE Trans.SP,Feb 2002.50(2):326-336
[106]张贤达.现代信号处理.清华大学出版社,1995:411-416
[107]梁应敞等.非高斯有色噪声中的正弦信号频率估计.电子学报,1995.4:111-134
[108]B.Kannanl,et al.Joint doa,frequency and model order estimation in additive α-stable noise.0-7803-6293-4/00.2000 IEEE:3798-3801
[109]S.C.A.Thomopoulos,T.W.Hilands.Joint Detection/Estimation for Model Order Selection in Gaussian and non-Gaussian Noise.0-7803-0585-X/92.1992 IEEE:613-617
[110]H.Belkacemi,et al.Subspace technique for joint angle/doppler estimation in alpha-stable clutter using phased fractional lower-order moments.0-7803-9314-7/05.2005IEEE:153-158
[111]R.J.Kozickl,B.M.Sadle.Robust subspace estimation in non-gaussian noise.0-7803-6293-4/00.2000 IEEE:3818-3821
[112]Yuehua Wu,Kwok-Wai Tam.M-Estimation in Exponential Signal Models.IEEE Trans.SP,Feb 2001.49(2):373-380
[113]温显斌等.指数信号模型中参数的稳健估计.航空学报,2003.24(5):439-442
[114]A.Swami,B.M.Sadler.On some detection and estimation problems in heavy-tailed noise.Signal Processing,2002.82:1471-1488
[115]G.A.Tsihrintzis,P.Ysakalides,C.L.Nikias.Signal Detection in Severely Heavy-Tailed Radar Clutter.IEEE Proc of ASILOMAR-29,1996:865-869
[116]M.A.Altinkaya,H.Delic,B.Sankur,E.Anarim.Subspace-based frequency estimation of sinusoidal signals in alpha-stable noise.Signal Processing,2002.82:1807-1827
[117]T.Liu,J.M.Mendel.A subspace-based direction finding algorithm using fractional lower order statistics.IEEE Trans.SP,Aug 2001.49(8):1605-1613
[118]P.Tsakalides,et al.Angle/doppler estimation in heavy tailed clutter backgrounds.IEEE Trans AES,Apr 1999.35(2):419-436
[119]S.M.Kay.罗鹏飞等 译.统计信号处理基础.北京:电子工业出版社,2003
[120]赵树杰 赵建勋 编著.信号检测与估计理论.清华大学出版社,2005
[121]B.Efron.Computers and the theory of statistics.SIAM Rev,1979.21:460-480
[122]曹晨 王小谟.关于雷达杂波性质研究的若干问题.现代雷达,2001.23(5):1-5
[123]S.Faruk,S.Nursen,M.Lamine.Modelling of Sea Clutter with Gaussian Mixtures and Estimation of the Clutter Parameter.0-7803-8318-4104.2004 IEEE:53-56
[124]M.Rangaswamy,D.WEINER.Computer Generation of Correlated Non-Gaussian Radar Clutter.IEEE Trans AES,Jan 1995.31(1):106-116
[125]I.Antipov.Simulation of Sea Clutter Returns.DSTO-TR-0679(ADA352675).1998
[126]Ward K.D.,S.Watts.Radar sea clutter.Microwave J.28,June 1985:109-121
[127]茅予海.频率捷变雷达.北京:国防工业出版社,1981:135-146
[128]宋海娜等.低入射余角下雷达海杂波的建模与仿真.国防科大学报,2000.3:29-33
[129]I.Antipov.Analysis of Sea Clutter Data.DSTO-TR-0647(ADA348339).1998
[130]E.Jakeman.K-distributed noise.J.Opt.A:Pure Appl.Opt.1,1999:784-789.
[131]现代应用数学手册-概率统计与随机过程卷.清华大学出版社.2000
[132]皇甫堪 陈建文 楼生强.现代数字信号处理.北京:电子工业出版社,2003
[133]B.C.Armstrong,H.D.Griffiths.Modelling spatially correlated K-distributed clutter.Electronic Let.1991.27(15):1355-1356
[134]陈平.文明分岔、经济混沌、和演化经济学.北京:经济科学出版社,2000:587-619
[135]佟春生.复杂性理论在河川径流时间序列分析中的应用研究.西安理工大学博士学位论文,2005
[136]黄欣荣等 复杂性科学兴起的语境分析.清华大学学报(社科版),2004.3:38-45
[137]成思危.复杂性科学探索-复杂科学与管理.北京:民主与建设出版社,1999
[138]李景平 刘军海.复杂科学的研究对象:非线性复杂系统.西安:全国复杂性与系统科学理论、方法及其应用学术研讨会,2004.10
[139]尼科里斯 普利高津.探索复杂性.四川教育出版社,1986
[140]欧阳莹之 著.田宝国等 译.复杂系统理论基础.上海科技教育出版社,2002
[141]马军海.复杂非线性系统的重构技术.天津大学出版社,2005
[142]周守仁.复杂性研究与混沌控制及其哲学阐析.四川教育出版社,2001
[143]王海燕.多变量非线性时间序列的复杂性分析研究.东南大学博士学位论文.2001
[144]刘文财.中国股票市场价格行为复杂性研究.天津大学管理学院博士论文.2002
[145]雷敏.非线性时间序列分析及其在动作表面肌电信号中的应用研究.上海交通大学博士学位论文.2000
[146]谭善文.多分辨希尔伯特.黄变换方法的研究.重庆大学博士学位论文,2001
[147]吕勇.非线性时间序列分析在设备故障诊断中的应用研究.北京科技大学博士学位论文,2004
[148]Metin Akay.Nonlinear biomedical signal processing.Dynamic analysis and modeling,Volume Ⅱ.New York:IEEE PRESS,2001
[149]吕金虎.混沌时间序列分析及其应用.武汉:武汉大学出版社,2002
[150]黄润生.混沌及其应用.武汉:武汉大学出版社,2000
[151]杨绍清,贾传荧.两种实用的相空间重构方法.物理学报,2002.51(11):2452-2458
[152]曹建福,韩崇昭等.非线性系统理论及应用.西安:西安交通大学出版社,2001
[153]Svarer C.,Hansen L.K.,Larsen J.On Design and Evaluation of Tapped-Delay Neural Network Architectures.San Francisco,IEEE Int Conf on NN,1993:45-51
[154]Kanty H.,Schreiber T.Nonlinear Time Series Analysis.Cambridge:Cambridge University Press,1997
[155]虞和济.基于神经网络的智能诊断.北京:冶金工业出版社,2000
[156]Cao Lianyue.Practical method for determining the minimum embedding dimension of a scalar time series.Physica D,1997.110:43-50
[157]T.Schreiber,A.Schmitz.Surrogate time series.Phys.D,2000.142:346-382
[158]M.Small,C.K.Tse.Detecting Determinism in Time Series:The Method of Surrogate Data.IEEE Trans.CS,May 2003.50(5):663-672
[159]Temujin A.,Mandic D.P.,Van H.M.M.The Delay Vector Variance Method for Determinism and Nonlinearity in Time Series.Physica D,2004.190:167-176
[160]J.P.Eckmann,S.O.Kamphorst,D.Ruelle.Recurrence plots of dynamical systems.Euro phys.Let.,1987.4(9):973-977
[161]E.Kononov.Visual Recurrence Analysis v4.2.http://pwl.netcom.com/～eugenek.
[162]A.Lempel,J.Ziv.On the complexity of finite sequences.IEEE Trans.IT,1976.22(1):75-81
[163]Kasper F.,Schuster H.G.Easily Calculable Measure for the Complexity of Spatiotemporal Patterns.Physical review A,1987.36(2):842-848
[164]Richman J.S.,Moorman J.R.Physiological time series analysis using approximate entropy and sample entropy.Am J Physiol Heart Circ Physiol,2000.278:H2039-H2049.
[165]B.B.Mandelbrot.Fractal:Form,Chance and Dimension.San Francisco:Freeman,1975
[166]T.Lo,H.Leung,J.Litva.Fractal Characterization of Sea-scattered Signal and Detection of Sea-surface Targets.IEE Procs-F,Aug 1993.140(4):243-250
[167]D.C.Halsey,M.H.Jensen,L.P.Kadanoff,I.Procaccia,B.I.Shraiman.Fractal measures and their singularities:The characterization of strange sets.Phys Rev A,1986.33(2):1141-1151
[168]J.Feder.Fractals.New York:Pergamon,1988
[169]A.Arneodo,E.Bacry,J.F.Muzy.The thermodynamics of fractals revisited with wavelets.Phys A,1995.213:232-275
[170]J.L.Vehel,R.Riedi.Fractional Brownian motion and data traffic modeling:The other end of the spectrum.Fractals in Engineering,Eds:J.L.Vehel,et al,1997
[171]R.Riedi.An Improved Multifractal Formalism and Self-Similar Measures.Journal of math analysis and applications,1995.189:462-490
[172]孙霞 吴自勤 黄昀.分形原理及其应用.中国科学技术大学出版社,2003
[173]Y.Shimizu,S.Thurner,K.Ehrenberger.Multifractal spectra as a measure of complexity in human posture.Fractals,2002.10:103-116
[174]R.Riedi,M.Crouse,V.Ribeiro,R.Baraniuk.A Multifractal Wavelet Model with Application to Network Traffic.IEEE Trans.IT,Apr 1999.45(3):992-1018
[175]Huang N.E.,Shen Z.,Long S.R.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis.Proc.of the Royal Society of London,1998.A(454):903-995
[176]王宏禹.信号处理相关理论综合与统一法.国防工业出版社.2005
[177]丁浩.新型信息处理技术在气液两相流流型辨识中的应用研究.浙江大学博士学位论文,2005
[178]M.R.Cowper.B.Mulgrew.Nonlinear processing of high resolution radar sea clutter.0-7803-5529-6/99.1999,IEEE:2633-2638
[179]S.Haykin,R.Bakker,B.W.Currie.Uncovering Nonlinear Dynamics-The Case Study of Sea Clutter.Proc of the IEEE,May 2002.90(5):860-881
[180]雷明等.非线性时间序列的小波分频预测.物理学报,2005.54(5):1988-1993
[181]小波分析理论与Matlab 7实现.北京:电子工业出版社,2005:321-361
[182]S.Mallat著.信号处理的小波导引2~(nd).杨力华等译.机械工业出版社,2002:122-163
[183]Broomhead D.,Lowe D.Multivariable function interpolation and adaptive networks.Complex Systems,1988.2:321-355
[184]S.Haykin著.神经网络原理2~(nd).叶世伟 史忠植 译.机械工业出版社,2004:230-280
[185]R.O.Duda,P.E.Hart,D.G.Stork著.模式分类2~(nd).李宏东 姚天翔等 译.机械工 业出版社,2004:183-224
[186]阎平凡 张长水.人工神经网络与模拟进化计算2~(nd).北京:清华大学出版社,2005
[187]叶青华.随机共振理论及其在信号处理中的应用研究.中国科学院声学研究所博士学位论文.2005
[188]M.I.Dykman.What can stochastic resonance do? Nature,1998.391:344
[189]D.Petracchi.What is the role of stochastic resonance? Chaos,Solitons & Fractrals,11,2000:1827-1834
[190]J.J.Collins,C.C.Chow,T.T.Imhoff.Stochastic resonance without turning.Nature,1995.376:236-239
[191]M.Nafie,A.H.Tewfik.Low power detection using stochastic resonance.The Thirty-Second Asilomar.Conf On Signal,System and Computer,1998
[192]A.S.Asdi,A.H.Tewfik.Detection of weak signals using adaptive stochastic resonance.Proc.IEEE Int Conf On ASSP,Detroit,MI.1995:1332-1335
[193]V.Galdi,V.Pierro,I.M.Pinto.Evaluation of stochastic-resonance-based detectors of weak harmonic signals in additive white Gaussian noise.Phys.Rev.1998.E57(6):6470-6479
[194]Valembois A.Fast-soft-decision decoding of linear codes,stochastic resonance in algorithms.IEEE Int Symposium on Information Theory,Sorrento,Italy.2000:91
[195]L.B.Kish.Information transfer rate of neurons:stochastic resonance of shannon's information channel capacity.Fluctuation & Noise Lett.,2001.1(1):L13-L19
[196]M.E.Inchiosa,A.R.Bulsara.Signal detection statistics of stochastic resonators.Phys.Rev.1996.E,53(3):R2021-R2024
[197]R.Benzi,A.Sutera,A.Vulpiani.The mechanism of stochastic resonance.J.Phys,1981.A14(11):L453-L457
[198]R.Benzi,et al.Stochastic resonance in climatic Change.Tellus,1982.34:10-16
[199]L.Gammaitoni,et al.Stochastic resonance.Rev.Mod.Phys.,1998.70(1):223-285
[200]S.Mitaim,B.Kosko.Adaptive stochastic resonance.Pro.IEEE,1998.86(11):2152-2183
[201]S.Fauve,et al.Stochastic resonance in a bistable system.Phys.Lett.,1983.97 A(1-2):5-7
[202]H.Risken.The Fokker-Planck Equation.New York:Springer,1983
[203]G.A.Tsihrintzis,C.L.Nikias.High-resolution autofocus techniques for SAR imaging based on fractional lower-order statistics.IEE Proc.RSN,Oct 2001.148(5):267-276
[204]A.Yasotharan.The performance of the fourier method in detecting an accelerating target and estimating its median velocity.IEEE Int radar conference,2000:1-7
[205]Xiang-Gen Xia.Discrete chirp-fourier transform and its application to chirp rate estimation.IEEE Trans.SP,Nov 2000.48(11):3122-3133
[206]S.Barbarossa.Analysis of multicomponent LFM signals by a combined Wigner-Hough transform.IEEE Trans.SP,1995.43(6):1511-1515
[207]S.Barbarossa,A.Scaglione.Product high-order ambiguity function for multicomponent polynomial -phase signal modeling.IEEE Trans.SP,Mar 1998.46(3):691-707
[208]G.T.Zhou.Harmonics in multiplicative and addtive noise:performance analysis of cyclic estimators.IEEE Trans SP,Jun 1995.43(6):1445-1459
[209]Min Shao,C.L.Nikias.Signal Processing with Fractional Lower Order Moments:Stable Processes and Their Applications.IEEE Proc,81(7),Jul 1993:986-1010
[210]张贤达 保铮.非平稳信号分析与处理.国防工业出版社,1998
[211]P.Tsakalides,et al.The robust covariation-based MUSIC(ROC-MUSIC) algorithm for bearing estimation in impulsive noise environments.IEEE Trans.SP,Jul 1996.44:1623-1633
[212]沈凤鳞等.信号统计分析与处理.合肥:中国科学技术大学出版,2002
[213]刘福声 罗鹏飞.统计信号处理.长沙:国防科技大学出版社,1999
[214]孙真真.基于光学区雷达目标二维像的目标散射特征提取的理论及方法研究.国防科技大学博士学位论文,2001.
[215]M.Wax,T.Kailath.Detection of Signals by Information Theoretic Criteria.IEEE Trans.ASSP,Apr 1985.33(2):387-392
[216]V.U.Reddy,L.S.Biradar.SVD-Based Information Theoretic Criteria for Detection of the Number of Damped/Undamped Sinusoids and Their Performance Analysis.IEEE Trans.SP,Sep 1993.41(9):2872-2881
[217]G.Schwartz.Estimating the dimension of a model.Ann.Stat.,1978.6(2):461-464
[218]J.Rissanen.Modelling by shortest data description.Automatica,1978.14:465-471
[219]H.Akaike.A new look at the statistical model identification.IEEE Trans.AC,Dec 1974.19:716-723
[220]P.M.Djuric.A Model Selection Rule for Sinusoids in White Gaussian Noise.IEEE Trans.SP,Jul I996.44(7):1744-1751
[221]P.Stoica,Y.Selen.Model-order selection:a review of information criterion rules.IEEE SP Mag,Jul 2004:36-47
[222]L.C.Potter,D.M.Chiang.A GTD-Based Parametric Model for Radar Scattering.IEEE Trans AP,Oct 1995.43(10):1058-1067
[223]张恂.雷达目标的高分辨参数建模及其在目标识别中的应用.国防科技大学学位博士论文,1997
[224]周剑雄 石志广 付强.雷达目标散射中心参数估计的极限性能分析.电子学报,2006.3:726-731
[225]G.L.Bretthorst.Bayesian Spectrum Analysis and Parameter Estimation.New York:Springer-Verlag,1988.Lecture Notes in Statistics.Vol.48
[226]G.L.Bretthorst,Bayesian analysis-Ⅰ/Ⅱ.J.Magn.Reson.,1990.88:533-570
[227]P.M.Djuric,H.Li.Bayesian spectrum estimation of harmonic signals.IEEE SP Let.,Nov 1995.2:213-215
[228]L.Dou,R.J.W.Hodgson.Bayesian inference and Gibbs sampling in spectral analysis and parameter estimation-Ⅰ/Ⅱ.Inv.Probl.,1995.1069-1085.Inv.Probl.,1996:121-137
[229]M.Kliger,J.M.Francos.MAP Model Order Selection Rule for 2-D Sinusoids in White Noise.IEEE Trans.SP,Jul 2005.53(7):2563-2575
[230]S.Kotz 吴喜之.现代贝叶斯统计学.北京:中国统计出版社,2000
[231]W.B.Bishop,P.M.Djuric.Model Order Selection of Damped Sinusoids in Noise by Predictive Densities.IEEE Trans SP,44(3):611-619
[232]龚光鲁 钱敏平.应用随机过程教程.北京:清华大学出版社,2004:203-228
[233]P.J.Green.Reversible jump MCMC computation and Bayesian model determination.Biometrika,1995.82:711-732
[234]C.Andrieu.A.Doucet.Joint Bayesian Model Selection and Estimation of Noisy Sinusoids via Reversible Jump MCMC.IEEE Trans.SP,Oct 1999.47(10):2667-2676
[235]S.Richardson,P.J.Green.On Bayesian analysis of mixtures with an unknown number of components.J.R.Statist.Soc.1997.B59:731-792
[236]J.Besag,et al.Bayesian computation and stochastic systems,Stat.Sci.,1995.10:3-66
[237]L.Tierney,Markov chains for exploring posterior distributions,Ann.Stat.,1994,with discussion:1701-1762
[238]N.William,J.P.Reilly.Wideband Array Signal Processing Using MCMC Methods.IEEE Trans.SP,Feb 2005.53(2):411-426
[239]张贤达.现代信号处理.北京:清华大学出版社,2002
[240]Xian-Da Zhang,et al.Prefiltering-Based ESPRIT for Estimating Sinusoidal Parameters in Non-Gaussian ARMA Noise.IEEE Trans.SP,Jan 1995.43(1):349-353
[241]张健 李国英.稳健估计和检验的若干进展.数学进展,1998.27(5):403-415
[242]Huber P.J.Robust Staristics.Wiley,New York,1981
[243]Hampel F.R.,Ronchetti E.M.,Rpusseeuw P.J.,Stahel W.A.Robust Statistics:The Approach Based on Influence Functions.Wiley,New York,1986
[244]G.E.P.Box.Non-normality and tests On variances.Biometritica,1953.40:318-335
[245]J.W.Tukey.A survey of sampling from contaminated distributions.In "Contributions to Probability and Statistics" edited by I.Olkin.Standford CA:Standford University Press,1960
[246]Hampel F.R.Rejection rules and robust estimates of location:An analysis of Some Monte Carlo results.In Transactions of the Seventh Pargue Conference and of the European Meeting of Statisticians,1977:187-194
[247]段凤增.信号检测理论2~(nd).哈尔滨:哈尔滨工业大学出版社,2002:344-356
[248]Sang G.O.,Rangasami L.K.A Robust Approach for High Resolution Frequency Estimation.IEEE Trans.SP,Mar 1991.39(3):627-643
[249]R.R.Hansen,R.Chellappa.Two-Dimensional Robust Spectrum Estimation.IEEE Trans.ASSP,Jul 1988.16(7):1051-1066
[250]Lagarias J.C.,et al.Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions.SIAM Journal of Optimization,1998.9(1)
[251]P.W.Holland,R.E.Welsch.Robust regression using iteratively reweighted least squares.Commun.Stat.Theory Methods A6,1977
[252]Y.Berthoumieua,et al.A 2-D robust high-resolution frequency estimation approach. Signal Processing 85. 2005: 1165-1188
[253] J. Kennedy, R.C. Eberhart. Particle Swarm optimization. Proc: IEEE international Conference On Neural Networks. USA: IEEE Press, 1995: 1942-1948
[254] Y Shi, R.C. Eberhart. A modified Swarm optimizer. IEEE Int Conference of Evolutionary Computation. Anchorage, Alaska: IEEE Press, May, 1998
[255] Clerc M. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. Proc of the Cong on EC. 1999: 1951-1957
[256] Frans van den Bergh, Engelbrecht A.P. A cooperative approach to particles warm optimization. IEEE Trans. EC, 2004. 8(3): 225-239
[257] J. Robinson, Yahya Rahmat-Samii. Particle Swarm Optimization in Electromagnetics.IEEE Trans. AP, Feb 2004. 52(2): 397-407
[258] N.J. Gordon., et al. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc-F, Apr 1993. 140(2): 107-113
[259] B. Ristic, S. Arulampalm, N.J. Gordon. Beyond the Kalman filter: particle filters for tracking applications. Boston, MA: Artech House, 2004
[260] A. Doucet, N. de Freitas, N.J. Gordon, editors. Sequential Monte Carlo methods in practice. New York: Springer-Verlag, Jan 2001
[261] M.S. Arulampalam, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. SP, Feb 2002. 50(2): 174-188
[262] C. Andrieu, et al. Sequential Monte Carlo methods for optimal filtering. In Sequential Monte Carlo Methods in Practice. New York: Springer, 2001: 79-95
[263] M. West. Mixture models, Monte Carlo, Bayesian updating and dynamic models. In Proc. 24th Symp. Interface, Comput. Sci. Statist., J.H. Newton, Ed. Fairfax Station, VA:Interface Foundation of North America, 1993: 325-333
[264] D.S. Lee, K.K. Chia. Recursive estimation for noise-free state models by Monte Carlo filtering. In Proc. Section Bayesian Statist. Sci., 1999. Joint Statist. Meet, Baltimore, MD,Aug. 8-12, 1999:21-26
[265] J. Liu, M. West. Combined parameter and state estimation in simulation-based filtering.In Sequential Monte Carlo Methods in Practice, A. Doucet, N. de Freitas, N. Gordon, Eds.New York: Springer, 2001: 197-223
[266] P.M. Djuric, et al. Density assisted particle filters for state and parameter estimation.0-7803-8484-9/04. IEEE ICASSP, 2004: 701-704
[2]Merrill I.Skolnik主编.雷达手册2~(nd).王军等译.北京:电子工业出版社,2003.
[3]丁鹭飞 耿富录 编著.雷达原理.3~(nd).西安电子科技人学出版社,2002
[4]Merrill I.Skolnik著.雷达系统导论.3~(nd).左群声等译.北京:电子工业出版社.2006
[5]S.Haykin,et al.Signal processing:where physics and mathematics meet.IEEE SP Mag,Jul,2001:6-7
[6]S.Haykin,et al.Chaotic Dynamics of sea clutter.Chaos,1997,4:777-802
[7]S.Haykin,et al.Detection of signals in chaos.Proc IEEE,1995,83:95-122
[8]Nathanson F.E.Radar design principles.2~(nd).New York:McGraw-Hill,1991
[9]Horst M.M,F.B.Dyer,M.T.Tuley.Radar sea clutter model.London:Inte Conf on Ant And Prop.IEE Pub.1978.No.169,Pts.1 and 2.
[10]Morchin W.Radar engineer's sourcebook.Norwood,MA:Artech House,1993.
[11]Watts S,Ward D.K.Spatial correlation in K-distribution sea clutter,IEE.Proc,1987.134(6):526-532
[12]Sheen D.R.,Johnston L.P.Statistical and spatial properties of forest clutter measured with polarimetric synthetic aperture radar.IEEE Trans.GPS,1992.30(3):578-585
[13]罗贤云,孙芳等.雷达地杂波的测试与分析.现代雷达,1994.16(4):10-22
[14]Ulaby F.T.,M.C.Dobson.Handbook of radar scattering statistics for terrain.Norwood:MA:Artech House,1989
[15]G.V.Trunck,S.F.George.Detection of targets in non-gaussian sea clutter.IEEE Trans.AES-6,Sep 1970:620-628
[16]M.Sekine,Y.Mao.Weibull radar clutter.London:Peter Peregrinus,1990
[17]Sekine,M.,et al.Weibull distributed ground clutter.IEEE Trans.AES,Jul 1981.17:596-598
[18]Sekine,M.,et al.Weibull distributed sea clutter,IEE Proc.130,Pt.F,1983:476
[19]Schleher,D.C.Radar detection in weibull clutter.IEEE Trans.AES,Nov 1976.12:736-743
[20]Jakeman E.,P.N.Pusey.A model for non-rayleigh sea echo.IEEE Trans.AES,Nov 1976.24:806-814
[21]M.D Bisceglie,C.Galdi.Random walk based characterisation of radar backscatter from the sea surface,lEE Proc RSN,Aug 1998.145(4):216-225
[22]R.J.A Tough,K.D.Ward.Diffusive and related models of some properties of sea clutter.J.Phys.:Condens Matter.14,2002:7737-7748
[23]C.J.Baker.K-distributed coherent sea clutter,IEE Proc.138,Pt.F,Apr 1991:82-92
[24]V.Anastassopoulos,et al.High resolution radar clutter statistics.IEEE Trans.AES,1999.35(1):43-60
[25]V.Anastassopoulos,et al.A generalized compound model for radar clutter.IEEE National Radar Conference,Atlanta,GA,Mar 1994:29-31
[26]Baker C.J.K-distributed coherent sea clutter,IEE Proc.138,Pt.F.Apr 1991:82-92
[27]Watts S.Radar detection prediction in k-distributed sea clutter and thermal noise.IEEE Trans.AES,Jan 1987.23:40-45
[28]B.C.Armstrong,H.D.Griffiths.CFAR detection of fluctuating targets in spatially correlated K-distributed clutter.IEE P ROC-F,APRIL 1991.138(2):139-152
[29]G.V.Trunk,S.F.George.Detection of targets in nonogaussian sea clutter.IEEE Trans.AES,Sep 1970.6:620-628
[30]W.David,J.Stein.Detection of Random Signals in Gaussian Mixture Noise.IEEE Trans IT,Nov 1995.41(6):1788-1801
[31]Trunk G.V.Radar properties of non-rayleigh sea clutter.IEEE Trans AES,1972.8:196-204
[32]Oliver C.J.Representation of radar sea clutter.IEE Proc.135,Pt.F,Dec 1988:497-500
[33]G.A.Tsihrintzis,C.L.Nikias.Evaluation of fractional,lower-order statistics-based detection algorithms on real radar sea-clutter data.IEE Proc RSN.Feb 1997.144(1):29-37
[34]邱天爽 张旭秀 等.统计信号处理.北京:电子工业出版社.2004
[35]Shlyakhin V.M.Probability models of non-rayleigh fluctuations of radar signals(a review).Soviet J.Communications Technology and Electronics.January 1988.33:1-16
[36]王盈等.相关K分布杂波的建模与仿真,信号处理,1997.13(2):141-146
[37]Irina antipov.Simulation of sea clutter returns.DSTO-TR-0679,1998
[38]吴翊 李永乐 胡庆军.应用数理统计.国防科技大学出版社,1995:70-101
[39]于寅.高等工程数学.第二版.华中理工大学出版社,1995:468-516
[40]A.Farina,et al.High resolution sea clutter data:statistical analysis of recorded live data.IEE Proc.RSN,June 1997.144(3):121-130
[41]M.V.Menon.Estimation of the shape and scale parameters of the Weibull distribution,Technometrics,1963,15:175-182.
[42]D.Blacknell,R.J.A.Tough.Parameter estimation for the K-distribution based on[z log(z)].IEE Proc.RSN,Dec 2001.148(6):309-312
[43]D.R.Iskander,A.M.Zoubir.Estimation of the Parameters of the K-Distribution Using Higher Order and Fractional Moments.IEEE Trans.AES,Oct 1999.35(4):1453-1457
[44]D.R.Iskander,A.M.Zoubir.Estimating the parameters of the k-distribution using the ml/mom approaoh.IEEE TENCON 1996:769-774
[45]M.Jahangir,D.Blacknell,R.G.White.Accurate approximation to the optimum parameter estimate for K-distributed clutter.IEE Proc RSN,Dec 1996.143(6):383-390
[46]Billingsley J.B.Radar ground clutter measurements and models Part I:Spatial amplitude statistics.In AGARD Conference Proceedings 501,Otawa,Canada,May 1991:1-15
[47]J.B.Billingsley,A.Farina,F.Gini,M.V.Greco,L.Verrazaani.Statistical analysis of measured radar ground clutter data.IEEE Trans.AES,1999.35(2):579-593
[48]H.C.Chan.Radar sea clutter at low grazing angles.IEE Proc.Pt.F,Apr 1990.137(2): 102-112
[49]A.Ozturk,et al.A New Statistical Goodness of fit Test Based on Graphical Representation.Technical Report.No.52.Department of Mathematics,Syracuse University,Syracuse,NY,1990
[50]Slaski,Murali R.L.K.A new efficient algorithm for pdf approximation.RL-TR-97-73(ADA329961) In-House Report,Aug 1997
[51]I.Antipov.Statistical Analysis of Northern Australian Coastline Sea Clutter Data.DSTO-TR-1236(ADA401323).2002.
[52]G.Davidson,H.D.Griffiths.Wavelet detection scheme for small targets in sea clutter.ELECTRONICS Let,12th Sep 2002.38(19):1128-1130
[53]S.Primak.Generation of compound non-Gaussian random processes with a given correlation function.Phys review E.Jan 2000.61(1):100-103
[54]Rangaswarmy M.,Weiner D.Computer generation of correlated non-guassian radar clutter.IEEE Trans.AES,1995.31(1):106-115
[55]Liu B.et al.Generation of a random sequence giving a jointly specified marginal distribution and auto covariance,IEEE Trans.ASSP,1982.30(2):973-983
[56]L.J.Marier.Correlated K-distributed clutter generation for radar detection and track.IEEE Trans.AES,1995.31(2):568-580
[57]姜卫东.光学区雷达目标结构成像的理论及其在雷达目标识别中的应用.国防科学技术大学博士学位论文,2000
[58]陈彦辉.雷达信号检测的分形方法研究.西安电子科技大学博士学位论文,2000
[59]姚纪欢.粗糙海面的电磁散射研究.西安电子科技大学博士学位论文,2000
[60]王俊.微弱目标信号积累检测的方法研究.西安电子科技大学博士学位论文,1999
[61]张嗣瀛.复杂系统与复杂性科学简介.青岛大学学报,2001.16(4):25-28
[62]金吾伦 郭元林.复杂性科学及其演变.复杂系统与复杂性科学,2004.1(1):1-5
[63]方锦清.令人关注的复杂性科学和复杂性研究.自然杂志,24(1):7-15
[64]刘秉正.非线性动力学.高等教育出版社,2004
[65]A.L.Lachinski.Land Warfare and Complexity,Part Ⅰ/Ⅱ(ADA362620;ADA362621).1999
[66]H.Leung,T.Lo.Chaotic radar signal-processing over the sea.IEEE J.OE,1993.18:287-295
[67]H.Leung.Experimental modeling of electromagnetic wave scattering for an ocean surface based on chaotic theory.Chaos,Solitons,Fractals.1992 2:25-43
[68]何建华等.基于混沌和神经网络的弱信号检测.电子学报,1998.26(10):33-37
[69]H.Heung,G.Hennessey,A.Drosopoulos.Signal Detection Using Radial Basis Function Coupled Map Lattice.IEEE Trans.NN,2000,11(5):1133-1137
[70]G.Hennessey,et al.Sea-Clutter Modeling Using a Radial Basis Function Neural Network.IEEE J.OE,Jul 2001.26(3):358-372
[71]M.McDonald,A.Damini.Limitations of nonlinear chaotic dynamics in predicting sea clutter returns.IEE Proc.RSN,Apr 2004.151(2):105-113
[72]S.Haykin,R.Bakker,B.W.Currie.Uncovering Nonlinear Dynamics-The Case Study of Sea Clutter.Proc of the IEEE,May 2002.90(5):860-881
[73]Nan Xie,H.Leung,Hing Chan.A Multiple-Model Prediction Approach for Sea Clutter Modeling.IEEE Trans.GRS,Jun 2003.41(6):1491-1502
[74]J.I.Butterfield.Fractal Interpolation of Radar Signatures for Detecting Stationary Targets in Ground Clutter.IEEE AES Systems Mag,Jul 1991:10-14
[75]T.LO,H.Heung,J.Litva,S.Haykin.Fractal characterization of sea scattered signals and detection of sea-surface targets.IEE Proc.-F,1993.140(4):243-250
[76]M.Martorella,F.Berizzi,E.D.Mese.On the fractal dimension of sea surface backscattered signal at low grazing angle.IEEE Trans.AP,2004.52:1193-1204
[77]P.Gambaoo,A.Garzelliooo,M.Martorellao,F.D.Acquaoo,F.Berizzio.Fractal and multifractal analysis of sea SAR clutter data.0-7803-7031-7/01.2001 IEEE:2430-2432
[78]薛笑荣等.基于分形理论的SAR图像分割.计算机工程与应用,2003.24:27-29
[79]张弓 曹俊纺.扩展分形在SAR图像特定尺寸目标检测中的应用.南京航空航天大学学报.2004.36(3):378-382
[80]M.Salmasil,et al.Performance Analysis of Fractal Detector for a General Model of HRR Signals.RADAR 2004-International Conference on Radar Systems
[81]Chih-ping LIN,M.Sano,S.Sayama,M.Sekine.Detection of Radar Targets Embedded in Sea Ice and Sea Clutter Using Fractals,Wavelets,and Neural Networks.IEICE TRANS.Commun,Sep 2000.E83-B(9):1916-1929
[82]Yi Zheng,Jianbo Gao,Kung Yao.Multiplicative multifractal modeling of sea clutter.0-7803-8882-8/05.IEEE 2005
[83]F.Berizzi,et al.Fractal approach for sea clutter generation.IEE Proc.RSN,Aug 2000.147(4):189-198.
[84]杜干.目标检测的分形方法及应用.西安电子科技大学博士学位论文,2000
[85]鲜明等.基于混沌、多重分形理论的雷达信号分析和目标识别.电子科学学刊,1998.20(4):433-439
[86]黎湘等.基于分形IFS的运动目标RCS序列分解.电子学报,1999.27(9):12-15
[87]李晓辉等.扩展目标角闪烁非线性检验及其抑制技术.系统工程与电子技术,2004.126(15):592-693
[88]Farina A.,A.Russo,F.A.Studer.Coherent radar detection in log-normal clutter.IEE Proc.133,Pt.F.Feb 1986:39-54
[89]Farina A.,et al.Theory of radar detection in coherent weibull clutter.IEE Proc.134,Pt.F.Apr 1987:174-190
[90]Sangston K.J.Coherent detection of radar targets in k-distributed,correlated clutter.NRL,Washington,D.C.Report 9130,Aug 1988
[91]何友等.雷达自动检测与恒虚警处理.清华大学出版社,1999
[92]F.Gini,et al.Estimation of Chirp Radar Signals in Compound-Gaussian Clutter:A Cyclostationary Approach.IEEE Trans SP,Apr 2000.48(4):1029-1039
[93]A.A.Saha,G.V.Anand.Design of detectors based on stochastic resonance.Signal Processing,2003.83:1193-1212.
[94]H.Heung,N.Dubash,Nan XIE.Detection of Small Objects in Clutter Using a GA-RBF Neural Network.IEEE Trans.AES,Jan 2002.38(1):98-118
[95]Hu Weidong,Yu Wenxian,Luo Aimin.Fractal detection of radar weak targets.NAECON 1995.ISSN:0-7803-2666-0/95:140-144
[96]马晓岩等.海杂波中基于混沌预测的目标检测方法改进.电子学报,2003.6:907-910
[97]刘剑等.基于海杂波混沌特性的目标检测.现代雷达,2004.26(2):56-60
[98]杜鹏飞等.混沌海杂波背景下的弱信号检测.系统工程与电子技术,2002.24(7):65-98
[99]李月.混沌振子检测引论.电子工业出版社,2004
[100]F.Gini,M.Montanari,L.Verrazzani.Maximum likelihood,ESPRIT,and periodogram frequency estimation of radar signals in K-distributed clutter.SP,2000.80:1115-1126
[101]F.Gini.A Radar Application of a Modified Cramer-Rao Bound:Parameter Estimation in Non-Gaussian Clutter.IEEE Trans SP,Jul 1998.46(7):1945-1953
[102]A.Dogandzic,Jian Wang,A.Nehorai.Maximum.Likelihood Estimation of Compound-Gaussian Clutter and Target Parameters.ADA433621
[103]R.J.Kozick,B.M.Sadler.Maximum-Likelihood Array Processing in Non-Gaussian Noise with Gaussian Mixtures.IEEE Trans.SP,Dec 2000.48(12):3520-353
[104]T.W.Hilands,et al.Nonlinear Filtering Methods for Harmonic Retrieval and Model Order Selection in Gaussian and Non-Gaussian Noise.IEEE Trans.SP,Apr 1997.45(4):982-995
[105]D.S.Lee,N.K.K.Chia.A Particle Algorithm for Sequential Bayesian Parameter Estimation and Model Selection.IEEE Trans.SP,Feb 2002.50(2):326-336
[106]张贤达.现代信号处理.清华大学出版社,1995:411-416
[107]梁应敞等.非高斯有色噪声中的正弦信号频率估计.电子学报,1995.4:111-134
[108]B.Kannanl,et al.Joint doa,frequency and model order estimation in additive α-stable noise.0-7803-6293-4/00.2000 IEEE:3798-3801
[109]S.C.A.Thomopoulos,T.W.Hilands.Joint Detection/Estimation for Model Order Selection in Gaussian and non-Gaussian Noise.0-7803-0585-X/92.1992 IEEE:613-617
[110]H.Belkacemi,et al.Subspace technique for joint angle/doppler estimation in alpha-stable clutter using phased fractional lower-order moments.0-7803-9314-7/05.2005IEEE:153-158
[111]R.J.Kozickl,B.M.Sadle.Robust subspace estimation in non-gaussian noise.0-7803-6293-4/00.2000 IEEE:3818-3821
[112]Yuehua Wu,Kwok-Wai Tam.M-Estimation in Exponential Signal Models.IEEE Trans.SP,Feb 2001.49(2):373-380
[113]温显斌等.指数信号模型中参数的稳健估计.航空学报,2003.24(5):439-442
[114]A.Swami,B.M.Sadler.On some detection and estimation problems in heavy-tailed noise.Signal Processing,2002.82:1471-1488
[115]G.A.Tsihrintzis,P.Ysakalides,C.L.Nikias.Signal Detection in Severely Heavy-Tailed Radar Clutter.IEEE Proc of ASILOMAR-29,1996:865-869
[116]M.A.Altinkaya,H.Delic,B.Sankur,E.Anarim.Subspace-based frequency estimation of sinusoidal signals in alpha-stable noise.Signal Processing,2002.82:1807-1827
[117]T.Liu,J.M.Mendel.A subspace-based direction finding algorithm using fractional lower order statistics.IEEE Trans.SP,Aug 2001.49(8):1605-1613
[118]P.Tsakalides,et al.Angle/doppler estimation in heavy tailed clutter backgrounds.IEEE Trans AES,Apr 1999.35(2):419-436
[119]S.M.Kay.罗鹏飞等 译.统计信号处理基础.北京:电子工业出版社,2003
[120]赵树杰 赵建勋 编著.信号检测与估计理论.清华大学出版社,2005
[121]B.Efron.Computers and the theory of statistics.SIAM Rev,1979.21:460-480
[122]曹晨 王小谟.关于雷达杂波性质研究的若干问题.现代雷达,2001.23(5):1-5
[123]S.Faruk,S.Nursen,M.Lamine.Modelling of Sea Clutter with Gaussian Mixtures and Estimation of the Clutter Parameter.0-7803-8318-4104.2004 IEEE:53-56
[124]M.Rangaswamy,D.WEINER.Computer Generation of Correlated Non-Gaussian Radar Clutter.IEEE Trans AES,Jan 1995.31(1):106-116
[125]I.Antipov.Simulation of Sea Clutter Returns.DSTO-TR-0679(ADA352675).1998
[126]Ward K.D.,S.Watts.Radar sea clutter.Microwave J.28,June 1985:109-121
[127]茅予海.频率捷变雷达.北京:国防工业出版社,1981:135-146
[128]宋海娜等.低入射余角下雷达海杂波的建模与仿真.国防科大学报,2000.3:29-33
[129]I.Antipov.Analysis of Sea Clutter Data.DSTO-TR-0647(ADA348339).1998
[130]E.Jakeman.K-distributed noise.J.Opt.A:Pure Appl.Opt.1,1999:784-789.
[131]现代应用数学手册-概率统计与随机过程卷.清华大学出版社.2000
[132]皇甫堪 陈建文 楼生强.现代数字信号处理.北京:电子工业出版社,2003
[133]B.C.Armstrong,H.D.Griffiths.Modelling spatially correlated K-distributed clutter.Electronic Let.1991.27(15):1355-1356
[134]陈平.文明分岔、经济混沌、和演化经济学.北京:经济科学出版社,2000:587-619
[135]佟春生.复杂性理论在河川径流时间序列分析中的应用研究.西安理工大学博士学位论文,2005
[136]黄欣荣等 复杂性科学兴起的语境分析.清华大学学报(社科版),2004.3:38-45
[137]成思危.复杂性科学探索-复杂科学与管理.北京:民主与建设出版社,1999
[138]李景平 刘军海.复杂科学的研究对象:非线性复杂系统.西安:全国复杂性与系统科学理论、方法及其应用学术研讨会,2004.10
[139]尼科里斯 普利高津.探索复杂性.四川教育出版社,1986
[140]欧阳莹之 著.田宝国等 译.复杂系统理论基础.上海科技教育出版社,2002
[141]马军海.复杂非线性系统的重构技术.天津大学出版社,2005
[142]周守仁.复杂性研究与混沌控制及其哲学阐析.四川教育出版社,2001
[143]王海燕.多变量非线性时间序列的复杂性分析研究.东南大学博士学位论文.2001
[144]刘文财.中国股票市场价格行为复杂性研究.天津大学管理学院博士论文.2002
[145]雷敏.非线性时间序列分析及其在动作表面肌电信号中的应用研究.上海交通大学博士学位论文.2000
[146]谭善文.多分辨希尔伯特.黄变换方法的研究.重庆大学博士学位论文,2001
[147]吕勇.非线性时间序列分析在设备故障诊断中的应用研究.北京科技大学博士学位论文,2004
[148]Metin Akay.Nonlinear biomedical signal processing.Dynamic analysis and modeling,Volume Ⅱ.New York:IEEE PRESS,2001
[149]吕金虎.混沌时间序列分析及其应用.武汉:武汉大学出版社,2002
[150]黄润生.混沌及其应用.武汉:武汉大学出版社,2000
[151]杨绍清,贾传荧.两种实用的相空间重构方法.物理学报,2002.51(11):2452-2458
[152]曹建福,韩崇昭等.非线性系统理论及应用.西安:西安交通大学出版社,2001
[153]Svarer C.,Hansen L.K.,Larsen J.On Design and Evaluation of Tapped-Delay Neural Network Architectures.San Francisco,IEEE Int Conf on NN,1993:45-51
[154]Kanty H.,Schreiber T.Nonlinear Time Series Analysis.Cambridge:Cambridge University Press,1997
[155]虞和济.基于神经网络的智能诊断.北京:冶金工业出版社,2000
[156]Cao Lianyue.Practical method for determining the minimum embedding dimension of a scalar time series.Physica D,1997.110:43-50
[157]T.Schreiber,A.Schmitz.Surrogate time series.Phys.D,2000.142:346-382
[158]M.Small,C.K.Tse.Detecting Determinism in Time Series:The Method of Surrogate Data.IEEE Trans.CS,May 2003.50(5):663-672
[159]Temujin A.,Mandic D.P.,Van H.M.M.The Delay Vector Variance Method for Determinism and Nonlinearity in Time Series.Physica D,2004.190:167-176
[160]J.P.Eckmann,S.O.Kamphorst,D.Ruelle.Recurrence plots of dynamical systems.Euro phys.Let.,1987.4(9):973-977
[161]E.Kononov.Visual Recurrence Analysis v4.2.http://pwl.netcom.com/～eugenek.
[162]A.Lempel,J.Ziv.On the complexity of finite sequences.IEEE Trans.IT,1976.22(1):75-81
[163]Kasper F.,Schuster H.G.Easily Calculable Measure for the Complexity of Spatiotemporal Patterns.Physical review A,1987.36(2):842-848
[164]Richman J.S.,Moorman J.R.Physiological time series analysis using approximate entropy and sample entropy.Am J Physiol Heart Circ Physiol,2000.278:H2039-H2049.
[165]B.B.Mandelbrot.Fractal:Form,Chance and Dimension.San Francisco:Freeman,1975
[166]T.Lo,H.Leung,J.Litva.Fractal Characterization of Sea-scattered Signal and Detection of Sea-surface Targets.IEE Procs-F,Aug 1993.140(4):243-250
[167]D.C.Halsey,M.H.Jensen,L.P.Kadanoff,I.Procaccia,B.I.Shraiman.Fractal measures and their singularities:The characterization of strange sets.Phys Rev A,1986.33(2):1141-1151
[168]J.Feder.Fractals.New York:Pergamon,1988
[169]A.Arneodo,E.Bacry,J.F.Muzy.The thermodynamics of fractals revisited with wavelets.Phys A,1995.213:232-275
[170]J.L.Vehel,R.Riedi.Fractional Brownian motion and data traffic modeling:The other end of the spectrum.Fractals in Engineering,Eds:J.L.Vehel,et al,1997
[171]R.Riedi.An Improved Multifractal Formalism and Self-Similar Measures.Journal of math analysis and applications,1995.189:462-490
[172]孙霞 吴自勤 黄昀.分形原理及其应用.中国科学技术大学出版社,2003
[173]Y.Shimizu,S.Thurner,K.Ehrenberger.Multifractal spectra as a measure of complexity in human posture.Fractals,2002.10:103-116
[174]R.Riedi,M.Crouse,V.Ribeiro,R.Baraniuk.A Multifractal Wavelet Model with Application to Network Traffic.IEEE Trans.IT,Apr 1999.45(3):992-1018
[175]Huang N.E.,Shen Z.,Long S.R.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis.Proc.of the Royal Society of London,1998.A(454):903-995
[176]王宏禹.信号处理相关理论综合与统一法.国防工业出版社.2005
[177]丁浩.新型信息处理技术在气液两相流流型辨识中的应用研究.浙江大学博士学位论文,2005
[178]M.R.Cowper.B.Mulgrew.Nonlinear processing of high resolution radar sea clutter.0-7803-5529-6/99.1999,IEEE:2633-2638
[179]S.Haykin,R.Bakker,B.W.Currie.Uncovering Nonlinear Dynamics-The Case Study of Sea Clutter.Proc of the IEEE,May 2002.90(5):860-881
[180]雷明等.非线性时间序列的小波分频预测.物理学报,2005.54(5):1988-1993
[181]小波分析理论与Matlab 7实现.北京:电子工业出版社,2005:321-361
[182]S.Mallat著.信号处理的小波导引2~(nd).杨力华等译.机械工业出版社,2002:122-163
[183]Broomhead D.,Lowe D.Multivariable function interpolation and adaptive networks.Complex Systems,1988.2:321-355
[184]S.Haykin著.神经网络原理2~(nd).叶世伟 史忠植 译.机械工业出版社,2004:230-280
[185]R.O.Duda,P.E.Hart,D.G.Stork著.模式分类2~(nd).李宏东 姚天翔等 译.机械工 业出版社,2004:183-224
[186]阎平凡 张长水.人工神经网络与模拟进化计算2~(nd).北京:清华大学出版社,2005
[187]叶青华.随机共振理论及其在信号处理中的应用研究.中国科学院声学研究所博士学位论文.2005
[188]M.I.Dykman.What can stochastic resonance do? Nature,1998.391:344
[189]D.Petracchi.What is the role of stochastic resonance? Chaos,Solitons & Fractrals,11,2000:1827-1834
[190]J.J.Collins,C.C.Chow,T.T.Imhoff.Stochastic resonance without turning.Nature,1995.376:236-239
[191]M.Nafie,A.H.Tewfik.Low power detection using stochastic resonance.The Thirty-Second Asilomar.Conf On Signal,System and Computer,1998
[192]A.S.Asdi,A.H.Tewfik.Detection of weak signals using adaptive stochastic resonance.Proc.IEEE Int Conf On ASSP,Detroit,MI.1995:1332-1335
[193]V.Galdi,V.Pierro,I.M.Pinto.Evaluation of stochastic-resonance-based detectors of weak harmonic signals in additive white Gaussian noise.Phys.Rev.1998.E57(6):6470-6479
[194]Valembois A.Fast-soft-decision decoding of linear codes,stochastic resonance in algorithms.IEEE Int Symposium on Information Theory,Sorrento,Italy.2000:91
[195]L.B.Kish.Information transfer rate of neurons:stochastic resonance of shannon's information channel capacity.Fluctuation & Noise Lett.,2001.1(1):L13-L19
[196]M.E.Inchiosa,A.R.Bulsara.Signal detection statistics of stochastic resonators.Phys.Rev.1996.E,53(3):R2021-R2024
[197]R.Benzi,A.Sutera,A.Vulpiani.The mechanism of stochastic resonance.J.Phys,1981.A14(11):L453-L457
[198]R.Benzi,et al.Stochastic resonance in climatic Change.Tellus,1982.34:10-16
[199]L.Gammaitoni,et al.Stochastic resonance.Rev.Mod.Phys.,1998.70(1):223-285
[200]S.Mitaim,B.Kosko.Adaptive stochastic resonance.Pro.IEEE,1998.86(11):2152-2183
[201]S.Fauve,et al.Stochastic resonance in a bistable system.Phys.Lett.,1983.97 A(1-2):5-7
[202]H.Risken.The Fokker-Planck Equation.New York:Springer,1983
[203]G.A.Tsihrintzis,C.L.Nikias.High-resolution autofocus techniques for SAR imaging based on fractional lower-order statistics.IEE Proc.RSN,Oct 2001.148(5):267-276
[204]A.Yasotharan.The performance of the fourier method in detecting an accelerating target and estimating its median velocity.IEEE Int radar conference,2000:1-7
[205]Xiang-Gen Xia.Discrete chirp-fourier transform and its application to chirp rate estimation.IEEE Trans.SP,Nov 2000.48(11):3122-3133
[206]S.Barbarossa.Analysis of multicomponent LFM signals by a combined Wigner-Hough transform.IEEE Trans.SP,1995.43(6):1511-1515
[207]S.Barbarossa,A.Scaglione.Product high-order ambiguity function for multicomponent polynomial -phase signal modeling.IEEE Trans.SP,Mar 1998.46(3):691-707
[208]G.T.Zhou.Harmonics in multiplicative and addtive noise:performance analysis of cyclic estimators.IEEE Trans SP,Jun 1995.43(6):1445-1459
[209]Min Shao,C.L.Nikias.Signal Processing with Fractional Lower Order Moments:Stable Processes and Their Applications.IEEE Proc,81(7),Jul 1993:986-1010
[210]张贤达 保铮.非平稳信号分析与处理.国防工业出版社,1998
[211]P.Tsakalides,et al.The robust covariation-based MUSIC(ROC-MUSIC) algorithm for bearing estimation in impulsive noise environments.IEEE Trans.SP,Jul 1996.44:1623-1633
[212]沈凤鳞等.信号统计分析与处理.合肥:中国科学技术大学出版,2002
[213]刘福声 罗鹏飞.统计信号处理.长沙:国防科技大学出版社,1999
[214]孙真真.基于光学区雷达目标二维像的目标散射特征提取的理论及方法研究.国防科技大学博士学位论文,2001.
[215]M.Wax,T.Kailath.Detection of Signals by Information Theoretic Criteria.IEEE Trans.ASSP,Apr 1985.33(2):387-392
[216]V.U.Reddy,L.S.Biradar.SVD-Based Information Theoretic Criteria for Detection of the Number of Damped/Undamped Sinusoids and Their Performance Analysis.IEEE Trans.SP,Sep 1993.41(9):2872-2881
[217]G.Schwartz.Estimating the dimension of a model.Ann.Stat.,1978.6(2):461-464
[218]J.Rissanen.Modelling by shortest data description.Automatica,1978.14:465-471
[219]H.Akaike.A new look at the statistical model identification.IEEE Trans.AC,Dec 1974.19:716-723
[220]P.M.Djuric.A Model Selection Rule for Sinusoids in White Gaussian Noise.IEEE Trans.SP,Jul I996.44(7):1744-1751
[221]P.Stoica,Y.Selen.Model-order selection:a review of information criterion rules.IEEE SP Mag,Jul 2004:36-47
[222]L.C.Potter,D.M.Chiang.A GTD-Based Parametric Model for Radar Scattering.IEEE Trans AP,Oct 1995.43(10):1058-1067
[223]张恂.雷达目标的高分辨参数建模及其在目标识别中的应用.国防科技大学学位博士论文,1997
[224]周剑雄 石志广 付强.雷达目标散射中心参数估计的极限性能分析.电子学报,2006.3:726-731
[225]G.L.Bretthorst.Bayesian Spectrum Analysis and Parameter Estimation.New York:Springer-Verlag,1988.Lecture Notes in Statistics.Vol.48
[226]G.L.Bretthorst,Bayesian analysis-Ⅰ/Ⅱ.J.Magn.Reson.,1990.88:533-570
[227]P.M.Djuric,H.Li.Bayesian spectrum estimation of harmonic signals.IEEE SP Let.,Nov 1995.2:213-215
[228]L.Dou,R.J.W.Hodgson.Bayesian inference and Gibbs sampling in spectral analysis and parameter estimation-Ⅰ/Ⅱ.Inv.Probl.,1995.1069-1085.Inv.Probl.,1996:121-137
[229]M.Kliger,J.M.Francos.MAP Model Order Selection Rule for 2-D Sinusoids in White Noise.IEEE Trans.SP,Jul 2005.53(7):2563-2575
[230]S.Kotz 吴喜之.现代贝叶斯统计学.北京:中国统计出版社,2000
[231]W.B.Bishop,P.M.Djuric.Model Order Selection of Damped Sinusoids in Noise by Predictive Densities.IEEE Trans SP,44(3):611-619
[232]龚光鲁 钱敏平.应用随机过程教程.北京:清华大学出版社,2004:203-228
[233]P.J.Green.Reversible jump MCMC computation and Bayesian model determination.Biometrika,1995.82:711-732
[234]C.Andrieu.A.Doucet.Joint Bayesian Model Selection and Estimation of Noisy Sinusoids via Reversible Jump MCMC.IEEE Trans.SP,Oct 1999.47(10):2667-2676
[235]S.Richardson,P.J.Green.On Bayesian analysis of mixtures with an unknown number of components.J.R.Statist.Soc.1997.B59:731-792
[236]J.Besag,et al.Bayesian computation and stochastic systems,Stat.Sci.,1995.10:3-66
[237]L.Tierney,Markov chains for exploring posterior distributions,Ann.Stat.,1994,with discussion:1701-1762
[238]N.William,J.P.Reilly.Wideband Array Signal Processing Using MCMC Methods.IEEE Trans.SP,Feb 2005.53(2):411-426
[239]张贤达.现代信号处理.北京:清华大学出版社,2002
[240]Xian-Da Zhang,et al.Prefiltering-Based ESPRIT for Estimating Sinusoidal Parameters in Non-Gaussian ARMA Noise.IEEE Trans.SP,Jan 1995.43(1):349-353
[241]张健 李国英.稳健估计和检验的若干进展.数学进展,1998.27(5):403-415
[242]Huber P.J.Robust Staristics.Wiley,New York,1981
[243]Hampel F.R.,Ronchetti E.M.,Rpusseeuw P.J.,Stahel W.A.Robust Statistics:The Approach Based on Influence Functions.Wiley,New York,1986
[244]G.E.P.Box.Non-normality and tests On variances.Biometritica,1953.40:318-335
[245]J.W.Tukey.A survey of sampling from contaminated distributions.In "Contributions to Probability and Statistics" edited by I.Olkin.Standford CA:Standford University Press,1960
[246]Hampel F.R.Rejection rules and robust estimates of location:An analysis of Some Monte Carlo results.In Transactions of the Seventh Pargue Conference and of the European Meeting of Statisticians,1977:187-194
[247]段凤增.信号检测理论2~(nd).哈尔滨:哈尔滨工业大学出版社,2002:344-356
[248]Sang G.O.,Rangasami L.K.A Robust Approach for High Resolution Frequency Estimation.IEEE Trans.SP,Mar 1991.39(3):627-643
[249]R.R.Hansen,R.Chellappa.Two-Dimensional Robust Spectrum Estimation.IEEE Trans.ASSP,Jul 1988.16(7):1051-1066
[250]Lagarias J.C.,et al.Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions.SIAM Journal of Optimization,1998.9(1)
[251]P.W.Holland,R.E.Welsch.Robust regression using iteratively reweighted least squares.Commun.Stat.Theory Methods A6,1977
[252]Y.Berthoumieua,et al.A 2-D robust high-resolution frequency estimation approach. Signal Processing 85. 2005: 1165-1188
[253] J. Kennedy, R.C. Eberhart. Particle Swarm optimization. Proc: IEEE international Conference On Neural Networks. USA: IEEE Press, 1995: 1942-1948
[254] Y Shi, R.C. Eberhart. A modified Swarm optimizer. IEEE Int Conference of Evolutionary Computation. Anchorage, Alaska: IEEE Press, May, 1998
[255] Clerc M. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. Proc of the Cong on EC. 1999: 1951-1957
[256] Frans van den Bergh, Engelbrecht A.P. A cooperative approach to particles warm optimization. IEEE Trans. EC, 2004. 8(3): 225-239
[257] J. Robinson, Yahya Rahmat-Samii. Particle Swarm Optimization in Electromagnetics.IEEE Trans. AP, Feb 2004. 52(2): 397-407
[258] N.J. Gordon., et al. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc-F, Apr 1993. 140(2): 107-113
[259] B. Ristic, S. Arulampalm, N.J. Gordon. Beyond the Kalman filter: particle filters for tracking applications. Boston, MA: Artech House, 2004
[260] A. Doucet, N. de Freitas, N.J. Gordon, editors. Sequential Monte Carlo methods in practice. New York: Springer-Verlag, Jan 2001
[261] M.S. Arulampalam, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. SP, Feb 2002. 50(2): 174-188
[262] C. Andrieu, et al. Sequential Monte Carlo methods for optimal filtering. In Sequential Monte Carlo Methods in Practice. New York: Springer, 2001: 79-95
[263] M. West. Mixture models, Monte Carlo, Bayesian updating and dynamic models. In Proc. 24th Symp. Interface, Comput. Sci. Statist., J.H. Newton, Ed. Fairfax Station, VA:Interface Foundation of North America, 1993: 325-333
[264] D.S. Lee, K.K. Chia. Recursive estimation for noise-free state models by Monte Carlo filtering. In Proc. Section Bayesian Statist. Sci., 1999. Joint Statist. Meet, Baltimore, MD,Aug. 8-12, 1999:21-26
[265] J. Liu, M. West. Combined parameter and state estimation in simulation-based filtering.In Sequential Monte Carlo Methods in Practice, A. Doucet, N. de Freitas, N. Gordon, Eds.New York: Springer, 2001: 197-223
[266] P.M. Djuric, et al. Density assisted particle filters for state and parameter estimation.0-7803-8484-9/04. IEEE ICASSP, 2004: 701-704
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