基于HRRP和JEM信号的雷达目标识别技术研究
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摘要
随着现代战争的不断信息化、智能化,雷达自动目标识别技术受到广泛关注。现代雷达工艺技术的发展为雷达目标识别提供了多种可用的信号形式,近年来雷达目标识别领域不断有新的成果面世,使其逐渐从理论研究向工程实现迈进。本文主要围绕着预研项目“目标识别技术”以及“重点实验室基金项目”等的研究任务,从宽带雷达一维高分辨距离像(HRRP)自动目标识别和窄带雷达自动目标分类两方面进行了研究,主要内容概括如下:
1.基于宽带雷达HRRP的目标识别研究包括四部分内容:
第一部分:较为详细地分析了HRRP的姿态敏感性,主要讨论了飞机类目标对偏航、俯仰、侧摆三维姿态角变化的敏感性、飞机类目标在正侧视附近的特点以及测试样本的“相干峰”现象。进而,为了合理划分HRRP方位角域,提出了一种自适应递归划分角域的建模方法,利用有概率含义的统计模型分类器,从雷达数据中提取连续HRRP序列中包含的非线性结构信息,递归地对雷达数据自适应划分角域。
第二部分:研究了HRRP强度和平移敏感性。现有HRRP雷达自动目标识别(RATR)方法一般对平移敏感性采用包络对齐及一些改进方法,对强度敏感性简单地能量归一化。显然,现有处理方式对强度敏感性与平移敏感性的优化是分离开来的,因此其匹配精度并不高。我们提出强度和平移联合优化匹配思想,并将其应用到独立高斯模型、PCA子空间统计模型和PPCA子空间统计模型,以提高平移和强度匹配精度。该思想可用于模板库建立和测试阶段。
第三部分:提出了一种稳健噪声的自适应统计识别方法。雷达目标识别希望能够在较远距离实现,因此识别算法对噪声的稳健性是HRRP目标识别工程化需要研究的一个问题。我们分别基于PPCA和FA统计模型提出了强噪声污染的测试样本如何匹配无噪声或弱噪声样本训练的模板。
第四部分:在线建库是HRRP目标识别工程化的一条途径。这一部分提出两种在线自适应HRRP识别方法:1).通过一种在线混合专家(OME)将HRRP数据在线地分割成若干个近似平稳的区域,在各个区域内使用平稳协方差函数的在线高斯过程分类器(OGPC)。针对迭代在线高斯过程分类器(IOGPC)的参数学习算法EP和EM,提出了一种双链高斯过程(Bi-OGP)来使OGPC的参数得以在单次数据扫掠的情况下实现在线更新。针对迭代在线混合专家门网络参数的学习算法EM,提出了基于初值选择的单次数据扫掠学习方法。2).在独立高斯模型假设下,推导了参数在线学习的公式,提出双门限法来剔除“坏值”样本并适当缓减假设模型与实时HRRP多模数据的失配。
2.基于窄带雷达的目标分类研究内容
由于窄带信号分类可以作为宽带HRRP识别的一种预处理,并且大量现役装备雷达是分辨率较低的窄带雷达,本论文研究了如何利用窄带信号喷气发动机调制(JEM)特征实现对喷气式飞机、螺旋桨飞机和直升机目标的分类。
大多数雷达,特别是地面警戒雷达,其脉冲重复频率相对较低,会导致JEM回波多普勒模糊,此外,对目标的观测时间(扫掠时间)相对较短,多普勒分辨率较低,会导致目标分类性能下降,这正是利用JEM特征进行目标分类的需要克服的困难。我们从模式分类的角度提出了利用JEM特征谱散布程度特征进行目标分类的方法。JEM回波在多普勒域近似看作是一系列线谱,采用谐波和的数学模型提取特征谱作为分类特征,分别给出脉间、脉内的特征谱提取方法及特征的降维方法。该特征不补偿机身回波,对机身多谱勒变化不敏感。
With the tendency of modern battle to become more and more information-based and intelligent, radar automatic target recognition(RATR) techniques have received intensive attentions. The development of modern radar technology supplies many usable signal forms for RATR, and many new productions in the RATR field have come forth in recent years. As a result, RATR is striding to practical realization from theoretical study. This dissertation provides our researches for RATR from two aspects, i.e. wideband radar High-resolution range profile (HRRP) automatic target recognition and narrow band radar automatic classification.
The main contents of this dissertation are summarized as follows.
1. The research of HRRP automatic recognition based on wideband radar includes four parts:
In the first part, we discuss the sensitivities of airplane targets with the variation of course, pitch and roll angle, the property of the HRRPs near the airplane target’s broadside, and coherent apex phenomena in test data. Furthermore, due to the target-aspect sensitivity, we propose a recursive algorithm for adaptively angular sector segmenting which exploit the nonlinear structure characteristic embedded in HRRP data through classifiers that have probability meaning.
The second part focuses on the sensitivities of amplitude-scale and time-shift in radar HRRP statistical recognition. Most approaches available just use the slide correlation processing or its modifications for time-shift sensitivity while simply normalizing the amplitude-scale. Obviously, approaches available solve the two sensitivity problems disjointedly, which leads to mismatch and limits recognition performance. In order to improve the matching precision, we propose two algorithms to jointly match the amplitude and time-shift, based on independent Gaussian model, PCA subspace and PPCA subspace statistical model. One of the algorithms is used in the training phase and the other one is used in the test phase.
The third part presents a noise-robust adaptive statistical recognition method. We hope to recognize target at long distance in RATR, and therefore, the robustness study of recognition algorithm is necessary. In this part, based on PPCA model and FA model, a robust method for adaptive statistical recognition is presented when test SNR is lower than training SNR.
The fourth part focuses on how to get data, learn and build the template database interactively and concurrently. We present two online methods. 1) An online mixture of experts (OME) is used to divide the HRRP data into several regions within which online Gaussian process classifier (OGPC) make predictions. Due to EM and EP, parameter learning methods for iterative online Gaussian process (IOGP), a Bi-online Gaussian process (Bi-OGP) is proposed to learn parameters by single pass of data. Due to EM algorithm for OME’s parameter learning, a single data pass method based on proper initial values is presented. 2) With the assumption that returned echoes in range cells is independent Gaussian distributed, online parameters is first given, and then a two thresholds method is proposed to pick the outliers and reduce the mismatch between the statistics model and the online real HRRP data.
2. Narrow band radar automatic classification
Because narrow bandwidth signal target classification is a pre-processing method of HRRP RATR and the bandwidth of a lot of radar equipments in use is narrow, we study how to use Jet Engine Modulation (JEM) characteristic of low-resolution signal to categorize aeroplanes into three kinds, i.e., turbojet aircraft, prop aircraft and helicopter.
Most low-resolution radar systems, especially ground surveillance radar systems, work at relatively low pulse repeat frequency (PRF) and with short time-on-target (TOT) (duration in scanning). Low PRF leads to Doppler ambiguity and short TOT results in low Doppler resolution, which poses a problem to target classification with low-resolution radar based on the JEM characteristic of radar echo. From the pattern classification viewpoint, using dispersion situations of JEM eigenvalue spectra, we propose a method for categorizing aeroplanes into three kinds, i.e., turbojet aircraft, prop aircraft and helicopter. We analyze the mathematical model of JEM echoes consisting of a series of line spectra and regard them as a sum of several series of harmonious waves. Classification features can be extracted based on the harmonious wave sum model. Some schemes for extracting features from echoes within or between pulses are proposed. Low-dimensional features are extracted to reduce computation burden. Our methods do not compensate for the fuselage echoes and are insensitive to the variation of fuselage Doppler.
1.基于宽带雷达HRRP的目标识别研究包括四部分内容:
第一部分:较为详细地分析了HRRP的姿态敏感性,主要讨论了飞机类目标对偏航、俯仰、侧摆三维姿态角变化的敏感性、飞机类目标在正侧视附近的特点以及测试样本的“相干峰”现象。进而,为了合理划分HRRP方位角域,提出了一种自适应递归划分角域的建模方法,利用有概率含义的统计模型分类器,从雷达数据中提取连续HRRP序列中包含的非线性结构信息,递归地对雷达数据自适应划分角域。
第二部分:研究了HRRP强度和平移敏感性。现有HRRP雷达自动目标识别(RATR)方法一般对平移敏感性采用包络对齐及一些改进方法,对强度敏感性简单地能量归一化。显然,现有处理方式对强度敏感性与平移敏感性的优化是分离开来的,因此其匹配精度并不高。我们提出强度和平移联合优化匹配思想,并将其应用到独立高斯模型、PCA子空间统计模型和PPCA子空间统计模型,以提高平移和强度匹配精度。该思想可用于模板库建立和测试阶段。
第三部分:提出了一种稳健噪声的自适应统计识别方法。雷达目标识别希望能够在较远距离实现,因此识别算法对噪声的稳健性是HRRP目标识别工程化需要研究的一个问题。我们分别基于PPCA和FA统计模型提出了强噪声污染的测试样本如何匹配无噪声或弱噪声样本训练的模板。
第四部分:在线建库是HRRP目标识别工程化的一条途径。这一部分提出两种在线自适应HRRP识别方法:1).通过一种在线混合专家(OME)将HRRP数据在线地分割成若干个近似平稳的区域,在各个区域内使用平稳协方差函数的在线高斯过程分类器(OGPC)。针对迭代在线高斯过程分类器(IOGPC)的参数学习算法EP和EM,提出了一种双链高斯过程(Bi-OGP)来使OGPC的参数得以在单次数据扫掠的情况下实现在线更新。针对迭代在线混合专家门网络参数的学习算法EM,提出了基于初值选择的单次数据扫掠学习方法。2).在独立高斯模型假设下,推导了参数在线学习的公式,提出双门限法来剔除“坏值”样本并适当缓减假设模型与实时HRRP多模数据的失配。
2.基于窄带雷达的目标分类研究内容
由于窄带信号分类可以作为宽带HRRP识别的一种预处理,并且大量现役装备雷达是分辨率较低的窄带雷达,本论文研究了如何利用窄带信号喷气发动机调制(JEM)特征实现对喷气式飞机、螺旋桨飞机和直升机目标的分类。
大多数雷达,特别是地面警戒雷达,其脉冲重复频率相对较低,会导致JEM回波多普勒模糊,此外,对目标的观测时间(扫掠时间)相对较短,多普勒分辨率较低,会导致目标分类性能下降,这正是利用JEM特征进行目标分类的需要克服的困难。我们从模式分类的角度提出了利用JEM特征谱散布程度特征进行目标分类的方法。JEM回波在多普勒域近似看作是一系列线谱,采用谐波和的数学模型提取特征谱作为分类特征,分别给出脉间、脉内的特征谱提取方法及特征的降维方法。该特征不补偿机身回波,对机身多谱勒变化不敏感。
With the tendency of modern battle to become more and more information-based and intelligent, radar automatic target recognition(RATR) techniques have received intensive attentions. The development of modern radar technology supplies many usable signal forms for RATR, and many new productions in the RATR field have come forth in recent years. As a result, RATR is striding to practical realization from theoretical study. This dissertation provides our researches for RATR from two aspects, i.e. wideband radar High-resolution range profile (HRRP) automatic target recognition and narrow band radar automatic classification.
The main contents of this dissertation are summarized as follows.
1. The research of HRRP automatic recognition based on wideband radar includes four parts:
In the first part, we discuss the sensitivities of airplane targets with the variation of course, pitch and roll angle, the property of the HRRPs near the airplane target’s broadside, and coherent apex phenomena in test data. Furthermore, due to the target-aspect sensitivity, we propose a recursive algorithm for adaptively angular sector segmenting which exploit the nonlinear structure characteristic embedded in HRRP data through classifiers that have probability meaning.
The second part focuses on the sensitivities of amplitude-scale and time-shift in radar HRRP statistical recognition. Most approaches available just use the slide correlation processing or its modifications for time-shift sensitivity while simply normalizing the amplitude-scale. Obviously, approaches available solve the two sensitivity problems disjointedly, which leads to mismatch and limits recognition performance. In order to improve the matching precision, we propose two algorithms to jointly match the amplitude and time-shift, based on independent Gaussian model, PCA subspace and PPCA subspace statistical model. One of the algorithms is used in the training phase and the other one is used in the test phase.
The third part presents a noise-robust adaptive statistical recognition method. We hope to recognize target at long distance in RATR, and therefore, the robustness study of recognition algorithm is necessary. In this part, based on PPCA model and FA model, a robust method for adaptive statistical recognition is presented when test SNR is lower than training SNR.
The fourth part focuses on how to get data, learn and build the template database interactively and concurrently. We present two online methods. 1) An online mixture of experts (OME) is used to divide the HRRP data into several regions within which online Gaussian process classifier (OGPC) make predictions. Due to EM and EP, parameter learning methods for iterative online Gaussian process (IOGP), a Bi-online Gaussian process (Bi-OGP) is proposed to learn parameters by single pass of data. Due to EM algorithm for OME’s parameter learning, a single data pass method based on proper initial values is presented. 2) With the assumption that returned echoes in range cells is independent Gaussian distributed, online parameters is first given, and then a two thresholds method is proposed to pick the outliers and reduce the mismatch between the statistics model and the online real HRRP data.
2. Narrow band radar automatic classification
Because narrow bandwidth signal target classification is a pre-processing method of HRRP RATR and the bandwidth of a lot of radar equipments in use is narrow, we study how to use Jet Engine Modulation (JEM) characteristic of low-resolution signal to categorize aeroplanes into three kinds, i.e., turbojet aircraft, prop aircraft and helicopter.
Most low-resolution radar systems, especially ground surveillance radar systems, work at relatively low pulse repeat frequency (PRF) and with short time-on-target (TOT) (duration in scanning). Low PRF leads to Doppler ambiguity and short TOT results in low Doppler resolution, which poses a problem to target classification with low-resolution radar based on the JEM characteristic of radar echo. From the pattern classification viewpoint, using dispersion situations of JEM eigenvalue spectra, we propose a method for categorizing aeroplanes into three kinds, i.e., turbojet aircraft, prop aircraft and helicopter. We analyze the mathematical model of JEM echoes consisting of a series of line spectra and regard them as a sum of several series of harmonious waves. Classification features can be extracted based on the harmonious wave sum model. Some schemes for extracting features from echoes within or between pulses are proposed. Low-dimensional features are extracted to reduce computation burden. Our methods do not compensate for the fuselage echoes and are insensitive to the variation of fuselage Doppler.
引文
[1] Skolnik M.. Introduction to Radar Systems.Second Edition, New York: McGraw-Hill. 1980.
[2] Duda R. O., Hart P. E., Stork D. G.. Pattern Classification. Second Edition. New York: John Wiley and Sons. 2001.
[3]叶炜.逆合成孔径雷达运动补偿与成像研究.博士研究生学位论文.西安电子科技大学. 1996.
[4]廖学军.基于高分辨距离像的雷达目标识别.博士研究生学位论文.西安电子科技大学. 1999.
[5]裴炳南.高分辨雷达自动目标识别方法研究.博士研究生学位论文.西安电子科技大学. 2002.
[6]邢孟道.基于实测数据的雷达成像方法研究.博士研究生学位论文.西安电子科技大学. 2002.
[7]杜兰.雷达高分辨距离像目标识别方法研究.博士研究生学位论文.西安电子科技大学. 2007.
[8]袁莉.基于高分辨距离像的雷达目标识别方法研究.博士研究生学位论文.西安电子科技大学.2007.
[9]陈渤.基于核方法的雷达高分辨目标识别技术研究.博士研究生学位论文.西安电子科技大学.2008.
[10]刘敬.雷达一维距离像特征提取与识别方法研究.博士研究生学位论文.西安电子科技大学.2008
[11]刘宏伟,杜兰,袁莉,保铮.雷达高分辨距离像目标识别研究进展.电子与信息学报. 2005, 27(8): 1328-1334.
[12]陈凤,杜兰,刘宏伟等.一种强度和平移联合优化的雷达HRRP目标识别方法.电子学报,2009,37(3):3459-3463。
[13]陈凤,杜兰,保铮,刘宏伟.雷达高分辨距离像自动目标识别方法的改进.电子与信息学报,2007,29(6):1450-1454。
[14]陈凤,杜兰,保铮,刘宏伟.一种优化K近邻准则及在雷达HRRP目标识别中的应用.西电学报,2007,30(1):1-5.
[15] Jacobs S. P.. Automatic target recognition using high-resolution radar range profiles. Ph. D. Dissertation. Washington University. 1999.
[16] Li H.-J., Wang Y. D., Wang L.-H. Matching score properties between range profile of high-resolution radar targets. IEEE trans. A. P.,1996, 44(4): 444-452.
[17] Li H.-J., Yang S.-H.. Using range profiles as feature vectors to identify aerospace objects. IEEE Trans. A.P., 1993, 41(3):261-268.
[18] Williams R., Westerkamp J., Gross D. et al.. Automatic Target Recognition of Time Critical Moving Targets 1D High Range Resolution (HRR) Radar. IEEE AES System Magazine, 2000, 15(4):37-43.
[19] Mitchell R. A., Dewall R.. Overview of high range resolution radar target identification. Proceedings of Automayic Target Recognition Working Group,1994.
[20] Barton D. K.. Sputnik II as observed by C band radar. IRE National Conference, 1959, 7(5): 67-73.
[21] Eesterkamp J.. A Framework for 1D HRR ATR evaluation. AFRL Technical Report. Aug.1998.
[22] Zhang X., Shi Y., Bao Z.. A New Feature Vector Using Selected Bispectra for Signal Classification with Application in Radar Target Recognition. IEEE Trans. Signal Processing. 2001, 49(9):1875-1885.
[23] Kim K. T., Seo D. K., Kim H. T.. Efficient radar target recognition using the MUSIC algorithm and invariant feature. IEEE Trans A.P., 2002, 50(3): 325-337
[24] J.Portegies Zwart, R.van der Heiden, S.Gelsema and F.Groen. Fast translation invariant classification of HRR range profiles in a zero phase representation. IEE Proc.-Radar Sonar Navig., 2003,150(6), 411-418.
[25] Lan Du, Hongwei Liu, Zheng Bao and Mengdao Xing. Radar HRRP target recognition based on higher order spectra. IEEE transactions on Signal Processing, 2005,53(7):2359-2368
[26]杜兰,刘宏伟,保铮,张军英.一种利用雷达高分辨距离像幅度起伏特性的特征提取新方法.电子学报, 2005, 33 (3):411-415.
[27] C.A.Wiley. Synthetic aperture radar. IEEE Trans. on AES, 1985, 21(3): 440-443.
[28] Bryant M., Garber F.. SVM classifier applied to the MSTAR public data set. Algorithms for Synthetic Aperture Radar Imagery VI. Proceedings of the SPIE, 2001, 3721: 355-360.
[29] Anthony Zywechm and Robert E. Bogner. Coherent averaging of range profiles. Proceedings of IEEE International Radar Conference,1995, 456-461
[30] Du Lan, Liu Hongwei, Bao Zheng, Zhang Junying. A two-distribution compounded statistical model for radar HRRP target recognition. IEEE Trans. on Signal Processing. 54 (6): 2226-2238.
[31] Webb A. R.. Gamma Mixture Models for Target Recognition. Pattern Recognition, 2000, 33:2045-2054.
[32] M. Tipping and C.M. Bishop. Probabilistic Principal Component Analysis. J. Royal Statistical Soc. B, 1999,21(3):611-622
[33] Rubin D., Thayer D.. EM algorithms for ML factor analysis. Psychometrika, 1982,47(1): 69-76
[34] Du Lan, Liu Hongwei, Bao Zheng. Radar HRRP statistical recognition: parametric model and model selection . IEEE Trans. on Signal Processing, 2008,56(5):1931–1944.
[35] C. Cortes and V. Vapnik, Support-vector networks. Machine Learning, 1995,20: 273-297
[36] V. Vapnik. The nature of statistical learning theory. Springer, N. Y. 1995.
[37] C. J. C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 1998, 2:121–167
[38] Xuejun Liao, Paul Runkle and Lawrence Carin. Identification of Ground Targets From Sequential High-Range-Resolution Radar Signatures. IEEE Trans. Aerospace and Electronic Systems, 2002, 38(4): 1230-1242.
[39] Xing M.-D., Bao Z., Pei B.. The properties of high-resolution range profiles. Optical Engineering. 2002, 41(2):493-504
[40] Rong Hu and Zhaoda Zhu, Research on Radar Classification based on High Resolution Range Profiles. Proceedings of Aerospace and Electronics Conference, 1997, 2, 951-955.
[41] Bo Chen, Hongwei Liu, Zheng Bao. A Kernel Optimization Method Based on the Localized Kernel Fisher. Pattern Recognition, 2008,41(3):1098-1109
[42] B. Sch?lkopf. Support vector learning. Oldenbourg Verlag, Munich. 1997.
[43] Bo Chen, Hongwei Liu, Zheng Bao. Kernel Subclass Discriminant Analysis. Neurocomputing, 2007, 71: 455-458
[44] Bo Chen, Hongwei Liu, Zheng Bao. PCA and Kernel PCA for Radar High Range Resolution Profiles Recognition. IEEE International Radar Conference in Arlington, Virginia USA, 528-533, 2005.
[45] C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. The MIT Press, Cambridge, MA, 2006.
[46] L. Csató. Gaussian Processes - Iterative Sparse Approximations. PhD thesis. Neural Computing Research Group, Aston University. 2002.
[47] Hyun-Chul Kim,Zoubin Ghahramani.Bayesian Gaussian Process Classification with the EM-EP Algorithm .IEEE Trans. on PAMI,2006,28(12):1948-1959
[48] Viswanath Ramamurti,Joydeep Ghosh. Structurally Adaptive Modular Networks for Nonstationary Environments. IEEE Tran. on neural networks 1999,10(1):152-160
[49] Ghadaki H, Dizaji R. Target track classification for airport surveillance radar (ASR). Radar 2006.New York:IEEE.2006,24-27
[50]朱张帆,丁建江.基于回波幅相分解的JEM特征提取方法.空军雷达学院学报,2006, 20(1):21-24
[51] Leung H, Wu J. Bayesian and Dempster–Shafer Target Identification for Radar Surveillance. IEEE Trans. on AES, 2000, 36 (2):432-447
[52] Pouliguen P, Lucas L, Muller F, Quete S et al. Calculation and analysis of electromagnetic scattering by helicopter rotating blades. IEEE Trans. on antennas and propagation,2002,50(10): 1193-1408
[53] Bell M R, Grubbs R A. JEM modeling and measurement for radar target identification. IEEE Trans on AES, 1993, 29(1):73-87
[54] Piazza E. Radar Signals Analysis and modellization presence of JEM application in the civilian ATC Radars. IEEE AES Systems Magazine,1999, 14(1):35-40.
[55] Martin J, Mulgrew B. Analysis of the theoretical radar return signal form aircraft propeller blades. The record of the IEEE International Conference Radar-90. New York:IEEE,1990. 569-572.
[56] Yang S, Yeh S.. Electromagnetic backscattering from aircraft propeller blades . IEEE Trans on Magnetic,1997,33(3):1432-1435
[57] Martint J, Mulgrew B. Analysis of the effects of blade pitch on the radar return signal from rotating aircraft blades. IEE Conference Publication 365. NewYork: IEEE,1992. 446-449.
[58] Elshafei M, Akhtar S, Ahmed M S. Parametric models for helicopter identification. IEEE Trans on AES,2000, 36(4):1242-1252
[59] Melendez G J, Kesler S B. Spectrum estimation by neural networks and their use for target classification by radar. ICASSP-95. New York:IEEE,1995, 3615-3618
[60] Moses R L, Carl J W. Autoregressive modeling of radar data with application to target identification. Radar Conference 1988.NewYork:IEEE,1988,220-224
[61] Pellegrini-S P F, Pardini C S. Radar Signals Analysis oriented to target characterization applied to civilian ATC radar. Radar 1992.NewYork:IEEE,1992,438-445
[62] Chen Feng, Liu Hongwei, Bao Zheng. Target classification with low-resolution radar based on dispersion situations of eigenvalue spectra. Science in China SeriesF-Information Sciences. Accepted
[63]陈凤,刘宏伟,保铮.基于特征谱散布特征的低分辨雷达目标分类方法.中国科学F辑录用
[64]丁建江,张贤达.低分辨雷达螺旋桨飞机回波调制特性的研究.电子与信息学报,2003,25(4): 460-465
[65] Gorshkov S A,Leschenko S P,Orlenko V M ,Sedyshev S Y and Shirman Y D.Radar Target Backscattering Simulation Software and User’s Manual. Artech House Publishers,Boston. London.2002.
[1] Duda R. O., Hart P. E., Stork D. G.. Pattern Classification. Second Edition. New York: John Wiley and Sons. 2001.
[2]杜兰.雷达高分辨距离像目标识别方法研究.博士研究生学位论文.西安电子科技大学. 2007.
[3]袁莉.基于高分辨距离像的雷达目标识别方法研究.博士研究生学位论文.西安电子科技大学.2007.
[4]陈渤.基于核方法的雷达高分辨目标识别技术研究.博士研究生学位论文.西安电子科技大学.2008.
[5] Du Lan, Liu Hongwei, Zheng Bao, and Mengdao Xing. Radar HRRP Target Recognition Based on Higher Order Spectra, IEEE Transactions on Signal Processing,2005,53 (7):2359-2368
[6] Hastie T., Tibshirani R.. Discriminant adaptive nearest neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18 (1):607-615.
[7] Domeniconi C., Peng J., Gunopulos D.. Locally adaptive metric nearest-neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002,24(1):1281-1285.
[8] Wang J., Neskovic Predrag, Cooper Leon. An daptive nearest neighbor algorithm for classification.Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, Guangzhou,2005,3069-3074
[9] Du Lan, Liu Hongwei, Bao Zheng, Zhang Junying. A two-distribution compounded statistical model for radar HRRP target recognition. IEEE Trans. on Signal Processing. Vol.54 (6): 2226-2238.
[10] M. Tipping and C.M. Bishop. Probabilistic Principal Component Analysis. J. Royal Statistical Soc. B, 1999,21(3), 611-622.
[11] Rubin D., Thayer D.. EM algorithms for ML factor analysis. Psychometrika. 1982,47(1): 69-76
[12] Du Lan, Liu Hngwei, Bao Zheng. Radar HRRP statistical recognition: parametric model and model selection.IEEE Trans. on Signal Processing,2008, 56(5):1931-1944.
[13] C. Cortes and V. Vapnik. Support-vector networks. Machine Learning, 1995, 20:273-297.
[14] V. Vapnik, The nature of statistical learning theory. Springer, N. Y. 1995.
[15] C. J. C. Burges, A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 1998, 2: 121-167
[16] Hongwei Liu, Zheng Bao, Radar HRR profiles recognition based on SVM with power-transformed-correlation kernel, LNCS 3174(I)(2004),531-536.
[17] D. J. C. MacKay. Information Theory, Inference and Learning Algorithms. Cambridge University Press, Cambridge, UK, 2003.
[18] D. J. C. MacKay. Gaussian processes - a replacement for supervised neural networks? Tutorial lecture notes for NIPS 1997, 1997.
[19] D. J. C. MacKay. Introduction to Gaussian processes. C. M. Bishop, editor, Neural Networks and Machine Learning, volume 168 of NATO ASI Series, pages 133-165. Springer, Berlin, 1998.
[20] C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. The MIT Press, Cambridge, MA, 2006.
[21] C. E. Rasmussen and Z. Ghahramani. Infinite mixtures of Gaussian process experts. T. G. Diettrich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14. The MIT Press, 2002
[22] L. Csató. Gaussian Processes - Iterative Sparse Approximations. PhD thesis. Neural Computing Research Group, Aston University.2002.
[23] Hyun-Chul Kim ,Zoubin Ghahramani.Bayesian Gaussian Process Classification with the EM-EP Algorithm .IEEE Trans. on PAMI,2006,28(12):1948-1959
[24] Ghadaki H, Dizaji R. Target track classification for airport surveillance radar (ASR). Radar 2006.New York:IEEE,2006,24-27
[25]朱张帆,丁建江.基于回波幅相分解的JEM特征提取方法.空军雷达学院学报,2006, 20(1):21-24
[26] Leung H, Wu J. Bayesian and Dempster–Shafer Target Identification for Radar Surveillance. IEEE Trans. on AES, 2000, 36 (2):432-447
[27] Pouliguen P, Lucas L, Muller F, Quete S et al. Calculation and analysis of electromagnetic scattering by helicopter rotating blades. IEEE Trans. on antennas and propagation,2002,50(10): 1193-1408
[28] Bell M R, Grubbs R A. JEM modeling and measurement for radar target identification. IEEE Trans on AES, 1993, 29(1):73-87
[29] Piazza E. Radar Signals Analysis and modellization presence of JEM application inthe to civilian ATC Radars. IEEE AES Systems Magazine,1999, 14(1):35-40.
[30] Martin J, Mulgrew B. Analysis of the theoretical radar return signal form aircraft propeller blades. The record of the IEEE International Conference Radar-90. New York:IEEE,1990, 569-572.
[31] Yang S, Yeh S. Electromagnetic backscattering from aircraft propeller blades. IEEE Trans on Magnetic,1997,33(3):1432-1435
[32] Martint J, Mulgrew B. Analysis of the effects of blade pitch on the radar return signal from rotating aircraft blades. IEE Conference Publication 365. NewYork: IEEE,1992, 446-449.
[33] Elshafei M, Akhtar S, Ahmed M S. Parametric models for helicopter identification, IEEE Trans on AES,2000, 36(4):1242-1252
[34] Melendez G J,Kesler S B. Spectrum estimation by neural networks and their use for target classification by radar. ICASSP-95. New York:IEEE,1995,3615-3618
[35] Moses R L, Carl J W. Autoregressive modeling of radar data with application to target identification. Radar Conference 1988.NewYork:IEEE,1988,220-224
[36] Pellegrini-S P F, Pardini C S. Radar Signals Analysis oriented to target characterization applied to civilian ATC radar. Radar 1992.NewYork:IEEE,1992,438-445
[37]丁建江,张贤达,低分辨雷达螺旋桨飞机回波调制特性的研究.电子与信息学报,2003,25(4): 460-465
[38]丁建江,张贤达,防空雷达螺旋桨飞机回波JEM特征的模型与分析.清华大学学报(自然科学版),2003,43(3):418-421
[39]陈凤,杜兰,保铮,刘宏伟.一种优化K近邻准则及在雷达HRRP目标识别中的应用.西电学报,2007,30(1):1-5.
[40] Chen Feng, Du Lan, Yuan Li, Bao Zheng. Radar HRRP target recognition using influence region of samples. Proceedings of 2006 International Conference on Radar. Oct.2006,1428-1431.
[1]马建华,刘宏伟,保铮.利用核匹配追踪算法进行雷达HRRP识别.西安电子科技大学学报,2005,32(1):84-88
[2]刘宏伟,杜兰,袁莉,保铮.雷达高分辨距离像识别研究进展.电子与信息学报, 2005,27(8):1328-1334
[3] Du Lan, Liu Hongwei, Bao Zheng, Radar HRRP target recognition based on higher order spectra. IEEE Trans. on S.P., 2005,53 (7):2359-2368.
[4]杜兰,保铮,邢孟道.飞机目标的雷达一维距离像特性研究.西安电子科技大学学报,2002,28(Sup.):14-19
[5]杜兰,刘宏伟,保铮.利用目标方位信息改善雷达距离像识别性能.系统工程与电子技术,2004,26(8):1040-1043
[6]边肇祺,张学工.模式识别(第二版).北京:清华大学出版社.2000: 30-31,136-140
[7] Li Hsueh-Jyh, Wang Yung-Deh, and Wang Long-huai. Matching score properties between rang profiles of high-resolution radar targets. IEEE Trans.on Antennas and Propagation,1996,44(4):444-452
[8] Li Hsueh-Jyh, Yang Sheng-Hui. Using rang profile as feature vector to identify aerospace objects. IEEE Trans, on Antennas and Propagation,1993, 41(3):261-268
[9] Gorshkov S A,Leschenko S P,Orlenko V M ,Sedyshev S Y and Shirman Y D.Radar Target Backscattering Simulation Software and User’s Manual. Artech House Publishers,Boston. London.2002.
[10]廖学军.基于高分辨距离的雷达目标识别.博士论文.西安电子科技大学, 1999.
[11]袁莉,刘宏伟,保铮.雷达高分辨距离像分类器的参数自适应学习算法.电子与信息学报,2007,30(1):1010-1015
[12] Chen Bo, Yuan Li, Liu Hongwei, Bao Zheng. Adaptively segmenting Angular sector for radar HRRP automatic target recognition. EURASIP Journal on Applied Signal Processing. Accepted.
[13]杜兰.雷达高分辨距离像目标识别方法研究.博士论文.西安电子科技大学,2007
[14]陈凤,杜兰,保铮,刘宏伟,雷达高分辨距离像自动目标识别方法的改进.电子与信息学报. 2007,29(6):1450-1454
[15] Csato Lehel. Gaussian processes - iterative sparse approximation. Ph.d Dissertation. Birmingham: Aston University. 2002.
[16] Williams C K. I., Rasmussen C. E. Gaussian processes for regression. NIPS-8.NewYork: MIT Press,1995,514-520
[17] Gibbs M, MacKay D. J. C. Variational Gaussian process classiffers. IEEE Transcations on NN, 2000,11(6): 1458-1464
[18] Csato Lehel, Manfred Opper. Sparse on-line Gaussian Processes. Neural Computation,2002,14(3):641-669.
[19]陈凤,杜兰,保铮,刘宏伟.雷达高分辨距离像自动目标识别方法的改进.电子与信息学报,2007,29(6):1450-1454
[1] Jacobs S. P.. Automatic target recognition using high-resolution radar range profiles. Ph. D. Dissertation. Washington University. 1999.
[2] Zwart, J., Heiden, R., Gelsema, S., and Groen, F. Fast translation invariant classification of HRR range profiles in a zero phase representation. IEE Proc.-Radar Sonar Navig., 2003, 150 (6): 411-418
[3] Du, L., Liu, H., Bao, Z., Zhang. J. Radar automatic target recognition using complex high-resolution range profiles. IET Radar, Sonar &Navig., 2007, 1(1):18-26.
[4] Bi, Zi., Wu, R., Li, J., and Williams, R. Joint super-resolution moving target feature extraction and stationary clutter suppression. IEE Proc.-Radar Sonar Navig., 2000, 147(1): 23–29
[5] Zhu, F., Zhang, X., Hu, Y., and Xie, D. Nonstationary Hidden Markov models for multiaspect discriminative feature extraction from radar targets.Trans. Signal Process., 2007, 55(5):2203-2214
[6] Haspert, K., Heagy, J., Sullivan, R. Template-based target identification andconfusion matrices. IEEE AES Sys. Mag.,2007, 22, (9):53-67
[7] Vespe, M., Baker, C.J., Griffiths, H. D. Automatic target recognition using multi-diversity radar. IET Radar, Sonar &Navig.,2007, 1(6): 470–478
[8] O'Sullivan, J. A., DeVore, M. D., Kedia, V., and Miller, M. I. SAR ATR performance using a conditionally Gaussian model. IEEE Trans. AES, 2001, 37, (1) ,91-108
[9] Kim, K. T. ,Seo, D. K, Kim, H. T. One-dimentional scattering centre extraction for efficient radar target classification. IEE Proc.-Radar Sonar Navig. 2001,148(5):285-296.
[10] Zyweck, A., Bogner, R.E.. Radar target classification of commercial aircraft.IEEE Trans. AES.1996, 32 (2):598-606
[11] Du, L., Liu, H., and Bao, Z.. Radar HRRP target recognition: parametric model and model selection. IEEE Trans. Signal Process., 2008, 56 (5): 1931-1944
[12] Webb A. statistical pattern recognition(second ed.). John wiley and Sons.2002.
[13] Du, L., Liu, H., Bao, Z., and Zhang, J..A Two-Distribution Compounded Statistical Model for Radar HRRP Target Recognition. IEEE Trans. Signal Process., 2006, 54(6): 2226–2238
[14] Li, H., Wang, Y., Wang L..Matching score properties between range profile of high-resolution radar targets.IEEE Trnas. Antennas. Propag.,1996,44(4):444-452
[15] Hudson, S., Psaltis, D..Correlation filters for aircraft identification form radar range profiles. IEEE Trans. AES,1993, 29(3):741-748
[16] Tipping, M. E., Bishop, C. M. Mixtures of principal component analysers. Neural Computation. 1999, 11(2):443-482
[17] Duda, R. O., Hart, P. E., and Stork, D. G.. Pattern classification(second ed.) .John Wiley and Sons.2001.
[18] Gorshkov, S. A., Leschenko, S. P., Orlenko, V. M., Sedyshev,S. Y., and Shirman, Y. D. Radar Target Backscattering Simulation Software and User’s Manual. Boston, MA: Artech House.2002.
[19] Shirman,Y. D.. Computer Simulation of Aerial Target Radar Scattering, Recognition, Detection, and Tracking. Boston, MA: Artech House.2002
[20]杜兰.雷达高分辨距离像目标识别方法研究.博士研究生学位论文.西安电子科技大学. 2007.
[21]陈凤,杜兰,保铮,刘宏伟.雷达高分辨距离像自动目标识别方法的改进.电子与信息学报,2007,29(6):1450-1454 .
[1]杜兰.雷达高分辨距离像目标识别方法研究.博士研究生学位论文.西安电子科技大学. 2007.
[2]袁莉.基于高分辨距离像的雷达目标识别方法研究.博士研究生学位论文.西安电子科技大学.2007.
[3] Du Lan, Liu Hongwei, Bao Zheng, Zhang Junying. A two-distribution compounded statistical model for radar HRRP target recognition. IEEE Trans. on Signal Processing, Jun. 2006, 54(6): 2226-2238.
[4] L. Du, H.W. Liu, Z. Bao. Radar HRRP statistical recognition: parametric model and model selection . IEEE Trans. on Signal Processing, 2008, vol. 56(5):1931–1944.
[5] Du Lan, Liu Hongwei, Bao Zheng. Radar HRRP statistical recognition based on hypersphere model. Submitted to Signal Processing.
[6] S. P. Jacobs, Automatic target recognition using high-resolution radar range profiles. Ph.D., Washington Univ. St. Louis, MO.1999.
[7] K. Copsey and A. R.Webb. Bayesian gamma mixture model approach to radar target recognition.IEEE Trans. Aerosp. Electron. Syst., 2003,39(4):1201–1217
[8] A. R. Webb.Gamma mixture models for target recognition. Pattern Recog., 2000, 33: 2045–2054
[9] M. Evans, N. Hastings, and B. Peacock.Statistical Distributions, Second ed. New York: Wiley, 1993.
[10] K. Fukunaga. Introduction to Statistical Pattern Recognition (2nd ed). Boston, MA: Academic. 1990.
[11] M. Skolnik, Introduction to Radar Systems(2nd ed.) New York: Mc- Graw-Hill. 1980.
[12] M. E. Tipping and C. M. Bishop.Mixtures of principal component analysers. Neural Comput.,1999,11(2): 443–482.
[13] Van der Heiden R., Groen F. C. A.. The Box-Cox Metric for Nearest Neighbour Classification Improvement. Pattern Recognition,1997, 30(2): 273-279.
[14] H. W. Liu Z. Bao. Radar HRR Profiles Recognition Based on SVM With Power-Transformed Correlation Kernel . Lecture Notes in Computer Science, 2004, 531–536.
[15]李世雄.代数方程与置换群.上海教育出版社.1981.
[16] R. A. Horn, C. R. Johnson. Matrix Analysis. Cambridge: Cambridge University Press.1985.
[17] Cardoso J F, Souloumiac A. Blind beamforming for non-Gaussian signals. Proc IEE, F,1993,140(6):362-370
[18] Belochrani A, Abed-Merain K, Cardoso J F,Moulines E. A blind sourse separation technique using seconde-order statistics. IEEE trans SignalProcessing,1997,45(2):434-444
[19] Golub G H, Van Loan C F. Matrix Computation(2nd ed). Baltimore: The John Hopkins University Press.1989
[20]张贤达.矩阵分析与应用.清华大学出版社.2004.
[1]杜兰.雷达高分辨距离像目标识别方法研究.博士论文.西安电子科技大学.2007.
[2] Chen Bo,Liu Hongwei,Chai Jing, et al.Large Margin Feature Weighting via Linear Programming. IEEE Tran. on Knowledage and data Enginneering, Accepted.
[3]陈凤,杜兰,保铮,刘宏伟.雷达高分辨距离像自动目标识别方法的改进.电子与信息学报, 2007,29(6):1450-1454
[4] Lehel Csato,Gaussian Processes Iterative Sparse Approximations.Ph.D thesis. Aston University.2002.
[5] Hyun-Chul Kim, Zoubin Ghahramani. Bayesian Gaussian Process Classification with the EM-EP Algorithm. IEEE Trans. on PAMI.2006,28(12):1948-1959
[6] L. Xu, M. I. Jordan, and G. E. Hinton. An alternative model for mixtures of experts, In Advances in Neural Information Processing Systems 7, MIT press,1995.
[7] Viswanath Ramamurti,Joydeep Ghosh. Structurally Adaptive Modular Networks for Nonstationary Environments.IEEE Tran. on neural networks 1999,10(1):152-160
[8] V. Tresp. Mixture of Gaussian processes. In Advances in Neural Information Processing Systems 13. MIT press, 2001.
[9] C.E. Rasmussen and Z. Ghahramani. Infinite mixtures of Gaussian process experts,In Advances in Neural Information Processing Systems 14, MIT press,2002.
[10] E.Meeds and S. Osindero. An Alternative Infinite Mixture Of Gaussian Process Experts,In Advances in Neural Information Processing Systems 13. MIT press, 2001.
[11] Carl Edward Rasmusen, Chris Williams. Gaussian process for machine learning.New York: MIT Press,2006
[12] Jacobs S.P.. Automatic target recognition using high-resolution radar range profiles. Ph.D. Dissertation. Washington University.1999
[13]裴炳南.高分辨雷达自动目标识别方法研究.博士论文.西安电子科技大学.2002.
[14] Berger J.Statistical Decision and Bayesian Analysis, New York,Springer,1985:1-617
[15] Jain A. K., Robert P.W., Duin ,et al. Statistical Pattern Recognition: A Review, IEEE Trains. PAMI,2000,22 (1): 4-34
[16] Declercq Arnaud, Piater J. H. online learning of gaussian mixture models: a two-level approach, VISAPP (1) 2008: 605-611
[17] Hall, P. and Hicks, Y. A. A method to add gaussian mixture models,Tech. Report,University of Bath,2005.
[18] Csato Lehel, Manfred Opper. Sparse on-line Gaussian Processes,Neural Computation,2002,14(3):641-669.
[1] Ghadaki H, Dizaji R. Target track classification for airport surveillance radar (ASR). Radar 2006.New York:IEEE,2006:24-27
[2]朱张帆,丁建江.基于回波幅相分解的JEM特征提取方法.空军雷达学院学报,2006, 20(1):21-24
[3] Leung H, Wu J. Bayesian and Dempster–Shafer Target Identification for Radar Surveillance. IEEE Trans. on AES, 2000, 36 (2):432-447
[4] Pouliguen P, Lucas L, Muller F, Quete S et al. Calculation and analysis of electromagnetic scattering by helicopter rotating blades. IEEE Trans. on antennas and propagation,2002,50(10): 1193-1408
[5] Bell M R, Grubbs R A. JEM modeling and measurement for radar target identification. IEEE Trans on AES, 1993, 29(1):73-87
[6] Piazza E. Radar Signals Analysis and modellization presence of JEM application in the civilian ATC Radars. IEEE AES Systems Magazine,1999, 14(1):35-40.
[7] Martin J, Mulgrew B. Analysis of the theoretical radar return signal form aircraft propeller blades. The record of the IEEE International Conference Radar-90, New York:IEEE, 1990:569-572.
[8] Yang S, Yeh S. Electromagnetic backscattering from aircraft propeller blades. IEEE Trans on Magnetic,1997,33(3):1432-1435
[9] Martint J, Mulgrew B. Analysis of the effects of blade pitch on the radar return signal from rotating aircraft blades. IEE Conference Publication 365,NewYork: IEEE,1992:446-449.
[10] Elshafei M, Akhtar S, Ahmed M S. Parametric models for helicopter identification. IEEE Trans on AES,2000, 36(4):1242-1252
[11] Melendez G J,Kesler S B. Spectrum estimation by neural networks and their use for target classification by radar. ICASSP-95. New York:IEEE,1995:3615-3618
[12] Moses R L, Carl J W. Autoregressive modeling of radar data with application to target identification. Radar Conference 1988,NewYork:IEEE,1988:220-224
[13] Pellegrini-S P F, Pardini C S. Radar Signals Analysis oriented to target characterization applied to civilian ATC radar. Radar 1992.NewYork:IEEE,1992:438-445
[14]丁建江,张贤达,低分辨雷达螺旋桨飞机回波调制特性的研究.电子与信息学报,2003,25(4): 460-465
[15]丁建江,张贤达,防空雷达螺旋桨飞机回波JEM特征的模型与分析.清华大学学报(自然科学版),2003,43(3):418-421
[16]徐仲,张凯院,陆金.Toeplitz矩阵类矩阵的快速算法.西安:西北工业大学出版社,2006:212-255
[17]王贵平,鲍长春,张鹏.基于奇异值分解的低速率波形内插语音编码算法.电子学报,2006,34(1):135-139
[18] Duda R O, Hart P E, and Stork DG. Pattern classification,2nd ed. New York: John Wiley and Sons, 2001:259-264
[19] Stove A. A doppler-based target classifier using linear discriminants and principal components.High Resolution Imaging and Target Classification.London:IET,2006:171-176
[20] Jahangir M, Pointing K M,O'Loghlen J W. A robust doppler classification technique based on Hidden Markov Models. Processing Radar 2002.London: IEE, 2002:162-166.
[21] Jahangir M, Ponting K M, O'Loghlen J W. Correction to "Robust Doppler classification technique based on Hidden Markov models. IEE Proc.-Radar Sonar Navig,150(5),2003,387.
[22] Ji H, Li J, Xie W. Bispectrum Based Radar Target Classification. ICSP’98.NewYorK:IEEE,1992:419-422
[23] Andric M, Durovic Z, Zrnic B. Ground surveillance radar target classification based on fuzzy logic approach. EROCON 2005. NewYork:IEEE,2005:1390-1392
[24] Bullard BD,Dowdy P C . Pulse doppler signature of a rotary wing aircraft. Aerospace and Electronic Systerms Magaziene,1991, 36 (5):28-30
[25] Yoon S, Kim B, Kim Y, Helicopter classification using time-frequency analysis, Electronics Letters,2000,36(22):1871-1872
[26] Linde G. Use of wideband waveforms for target recognition with surveillanceradars.IEEE international radar conference 2000, NewYork:IEEE, 2000:128-133
[2] Duda R. O., Hart P. E., Stork D. G.. Pattern Classification. Second Edition. New York: John Wiley and Sons. 2001.
[3]叶炜.逆合成孔径雷达运动补偿与成像研究.博士研究生学位论文.西安电子科技大学. 1996.
[4]廖学军.基于高分辨距离像的雷达目标识别.博士研究生学位论文.西安电子科技大学. 1999.
[5]裴炳南.高分辨雷达自动目标识别方法研究.博士研究生学位论文.西安电子科技大学. 2002.
[6]邢孟道.基于实测数据的雷达成像方法研究.博士研究生学位论文.西安电子科技大学. 2002.
[7]杜兰.雷达高分辨距离像目标识别方法研究.博士研究生学位论文.西安电子科技大学. 2007.
[8]袁莉.基于高分辨距离像的雷达目标识别方法研究.博士研究生学位论文.西安电子科技大学.2007.
[9]陈渤.基于核方法的雷达高分辨目标识别技术研究.博士研究生学位论文.西安电子科技大学.2008.
[10]刘敬.雷达一维距离像特征提取与识别方法研究.博士研究生学位论文.西安电子科技大学.2008
[11]刘宏伟,杜兰,袁莉,保铮.雷达高分辨距离像目标识别研究进展.电子与信息学报. 2005, 27(8): 1328-1334.
[12]陈凤,杜兰,刘宏伟等.一种强度和平移联合优化的雷达HRRP目标识别方法.电子学报,2009,37(3):3459-3463。
[13]陈凤,杜兰,保铮,刘宏伟.雷达高分辨距离像自动目标识别方法的改进.电子与信息学报,2007,29(6):1450-1454。
[14]陈凤,杜兰,保铮,刘宏伟.一种优化K近邻准则及在雷达HRRP目标识别中的应用.西电学报,2007,30(1):1-5.
[15] Jacobs S. P.. Automatic target recognition using high-resolution radar range profiles. Ph. D. Dissertation. Washington University. 1999.
[16] Li H.-J., Wang Y. D., Wang L.-H. Matching score properties between range profile of high-resolution radar targets. IEEE trans. A. P.,1996, 44(4): 444-452.
[17] Li H.-J., Yang S.-H.. Using range profiles as feature vectors to identify aerospace objects. IEEE Trans. A.P., 1993, 41(3):261-268.
[18] Williams R., Westerkamp J., Gross D. et al.. Automatic Target Recognition of Time Critical Moving Targets 1D High Range Resolution (HRR) Radar. IEEE AES System Magazine, 2000, 15(4):37-43.
[19] Mitchell R. A., Dewall R.. Overview of high range resolution radar target identification. Proceedings of Automayic Target Recognition Working Group,1994.
[20] Barton D. K.. Sputnik II as observed by C band radar. IRE National Conference, 1959, 7(5): 67-73.
[21] Eesterkamp J.. A Framework for 1D HRR ATR evaluation. AFRL Technical Report. Aug.1998.
[22] Zhang X., Shi Y., Bao Z.. A New Feature Vector Using Selected Bispectra for Signal Classification with Application in Radar Target Recognition. IEEE Trans. Signal Processing. 2001, 49(9):1875-1885.
[23] Kim K. T., Seo D. K., Kim H. T.. Efficient radar target recognition using the MUSIC algorithm and invariant feature. IEEE Trans A.P., 2002, 50(3): 325-337
[24] J.Portegies Zwart, R.van der Heiden, S.Gelsema and F.Groen. Fast translation invariant classification of HRR range profiles in a zero phase representation. IEE Proc.-Radar Sonar Navig., 2003,150(6), 411-418.
[25] Lan Du, Hongwei Liu, Zheng Bao and Mengdao Xing. Radar HRRP target recognition based on higher order spectra. IEEE transactions on Signal Processing, 2005,53(7):2359-2368
[26]杜兰,刘宏伟,保铮,张军英.一种利用雷达高分辨距离像幅度起伏特性的特征提取新方法.电子学报, 2005, 33 (3):411-415.
[27] C.A.Wiley. Synthetic aperture radar. IEEE Trans. on AES, 1985, 21(3): 440-443.
[28] Bryant M., Garber F.. SVM classifier applied to the MSTAR public data set. Algorithms for Synthetic Aperture Radar Imagery VI. Proceedings of the SPIE, 2001, 3721: 355-360.
[29] Anthony Zywechm and Robert E. Bogner. Coherent averaging of range profiles. Proceedings of IEEE International Radar Conference,1995, 456-461
[30] Du Lan, Liu Hongwei, Bao Zheng, Zhang Junying. A two-distribution compounded statistical model for radar HRRP target recognition. IEEE Trans. on Signal Processing. 54 (6): 2226-2238.
[31] Webb A. R.. Gamma Mixture Models for Target Recognition. Pattern Recognition, 2000, 33:2045-2054.
[32] M. Tipping and C.M. Bishop. Probabilistic Principal Component Analysis. J. Royal Statistical Soc. B, 1999,21(3):611-622
[33] Rubin D., Thayer D.. EM algorithms for ML factor analysis. Psychometrika, 1982,47(1): 69-76
[34] Du Lan, Liu Hongwei, Bao Zheng. Radar HRRP statistical recognition: parametric model and model selection . IEEE Trans. on Signal Processing, 2008,56(5):1931–1944.
[35] C. Cortes and V. Vapnik, Support-vector networks. Machine Learning, 1995,20: 273-297
[36] V. Vapnik. The nature of statistical learning theory. Springer, N. Y. 1995.
[37] C. J. C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 1998, 2:121–167
[38] Xuejun Liao, Paul Runkle and Lawrence Carin. Identification of Ground Targets From Sequential High-Range-Resolution Radar Signatures. IEEE Trans. Aerospace and Electronic Systems, 2002, 38(4): 1230-1242.
[39] Xing M.-D., Bao Z., Pei B.. The properties of high-resolution range profiles. Optical Engineering. 2002, 41(2):493-504
[40] Rong Hu and Zhaoda Zhu, Research on Radar Classification based on High Resolution Range Profiles. Proceedings of Aerospace and Electronics Conference, 1997, 2, 951-955.
[41] Bo Chen, Hongwei Liu, Zheng Bao. A Kernel Optimization Method Based on the Localized Kernel Fisher. Pattern Recognition, 2008,41(3):1098-1109
[42] B. Sch?lkopf. Support vector learning. Oldenbourg Verlag, Munich. 1997.
[43] Bo Chen, Hongwei Liu, Zheng Bao. Kernel Subclass Discriminant Analysis. Neurocomputing, 2007, 71: 455-458
[44] Bo Chen, Hongwei Liu, Zheng Bao. PCA and Kernel PCA for Radar High Range Resolution Profiles Recognition. IEEE International Radar Conference in Arlington, Virginia USA, 528-533, 2005.
[45] C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. The MIT Press, Cambridge, MA, 2006.
[46] L. Csató. Gaussian Processes - Iterative Sparse Approximations. PhD thesis. Neural Computing Research Group, Aston University. 2002.
[47] Hyun-Chul Kim,Zoubin Ghahramani.Bayesian Gaussian Process Classification with the EM-EP Algorithm .IEEE Trans. on PAMI,2006,28(12):1948-1959
[48] Viswanath Ramamurti,Joydeep Ghosh. Structurally Adaptive Modular Networks for Nonstationary Environments. IEEE Tran. on neural networks 1999,10(1):152-160
[49] Ghadaki H, Dizaji R. Target track classification for airport surveillance radar (ASR). Radar 2006.New York:IEEE.2006,24-27
[50]朱张帆,丁建江.基于回波幅相分解的JEM特征提取方法.空军雷达学院学报,2006, 20(1):21-24
[51] Leung H, Wu J. Bayesian and Dempster–Shafer Target Identification for Radar Surveillance. IEEE Trans. on AES, 2000, 36 (2):432-447
[52] Pouliguen P, Lucas L, Muller F, Quete S et al. Calculation and analysis of electromagnetic scattering by helicopter rotating blades. IEEE Trans. on antennas and propagation,2002,50(10): 1193-1408
[53] Bell M R, Grubbs R A. JEM modeling and measurement for radar target identification. IEEE Trans on AES, 1993, 29(1):73-87
[54] Piazza E. Radar Signals Analysis and modellization presence of JEM application in the civilian ATC Radars. IEEE AES Systems Magazine,1999, 14(1):35-40.
[55] Martin J, Mulgrew B. Analysis of the theoretical radar return signal form aircraft propeller blades. The record of the IEEE International Conference Radar-90. New York:IEEE,1990. 569-572.
[56] Yang S, Yeh S.. Electromagnetic backscattering from aircraft propeller blades . IEEE Trans on Magnetic,1997,33(3):1432-1435
[57] Martint J, Mulgrew B. Analysis of the effects of blade pitch on the radar return signal from rotating aircraft blades. IEE Conference Publication 365. NewYork: IEEE,1992. 446-449.
[58] Elshafei M, Akhtar S, Ahmed M S. Parametric models for helicopter identification. IEEE Trans on AES,2000, 36(4):1242-1252
[59] Melendez G J, Kesler S B. Spectrum estimation by neural networks and their use for target classification by radar. ICASSP-95. New York:IEEE,1995, 3615-3618
[60] Moses R L, Carl J W. Autoregressive modeling of radar data with application to target identification. Radar Conference 1988.NewYork:IEEE,1988,220-224
[61] Pellegrini-S P F, Pardini C S. Radar Signals Analysis oriented to target characterization applied to civilian ATC radar. Radar 1992.NewYork:IEEE,1992,438-445
[62] Chen Feng, Liu Hongwei, Bao Zheng. Target classification with low-resolution radar based on dispersion situations of eigenvalue spectra. Science in China SeriesF-Information Sciences. Accepted
[63]陈凤,刘宏伟,保铮.基于特征谱散布特征的低分辨雷达目标分类方法.中国科学F辑录用
[64]丁建江,张贤达.低分辨雷达螺旋桨飞机回波调制特性的研究.电子与信息学报,2003,25(4): 460-465
[65] Gorshkov S A,Leschenko S P,Orlenko V M ,Sedyshev S Y and Shirman Y D.Radar Target Backscattering Simulation Software and User’s Manual. Artech House Publishers,Boston. London.2002.
[1] Duda R. O., Hart P. E., Stork D. G.. Pattern Classification. Second Edition. New York: John Wiley and Sons. 2001.
[2]杜兰.雷达高分辨距离像目标识别方法研究.博士研究生学位论文.西安电子科技大学. 2007.
[3]袁莉.基于高分辨距离像的雷达目标识别方法研究.博士研究生学位论文.西安电子科技大学.2007.
[4]陈渤.基于核方法的雷达高分辨目标识别技术研究.博士研究生学位论文.西安电子科技大学.2008.
[5] Du Lan, Liu Hongwei, Zheng Bao, and Mengdao Xing. Radar HRRP Target Recognition Based on Higher Order Spectra, IEEE Transactions on Signal Processing,2005,53 (7):2359-2368
[6] Hastie T., Tibshirani R.. Discriminant adaptive nearest neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18 (1):607-615.
[7] Domeniconi C., Peng J., Gunopulos D.. Locally adaptive metric nearest-neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002,24(1):1281-1285.
[8] Wang J., Neskovic Predrag, Cooper Leon. An daptive nearest neighbor algorithm for classification.Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, Guangzhou,2005,3069-3074
[9] Du Lan, Liu Hongwei, Bao Zheng, Zhang Junying. A two-distribution compounded statistical model for radar HRRP target recognition. IEEE Trans. on Signal Processing. Vol.54 (6): 2226-2238.
[10] M. Tipping and C.M. Bishop. Probabilistic Principal Component Analysis. J. Royal Statistical Soc. B, 1999,21(3), 611-622.
[11] Rubin D., Thayer D.. EM algorithms for ML factor analysis. Psychometrika. 1982,47(1): 69-76
[12] Du Lan, Liu Hngwei, Bao Zheng. Radar HRRP statistical recognition: parametric model and model selection.IEEE Trans. on Signal Processing,2008, 56(5):1931-1944.
[13] C. Cortes and V. Vapnik. Support-vector networks. Machine Learning, 1995, 20:273-297.
[14] V. Vapnik, The nature of statistical learning theory. Springer, N. Y. 1995.
[15] C. J. C. Burges, A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 1998, 2: 121-167
[16] Hongwei Liu, Zheng Bao, Radar HRR profiles recognition based on SVM with power-transformed-correlation kernel, LNCS 3174(I)(2004),531-536.
[17] D. J. C. MacKay. Information Theory, Inference and Learning Algorithms. Cambridge University Press, Cambridge, UK, 2003.
[18] D. J. C. MacKay. Gaussian processes - a replacement for supervised neural networks? Tutorial lecture notes for NIPS 1997, 1997.
[19] D. J. C. MacKay. Introduction to Gaussian processes. C. M. Bishop, editor, Neural Networks and Machine Learning, volume 168 of NATO ASI Series, pages 133-165. Springer, Berlin, 1998.
[20] C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. The MIT Press, Cambridge, MA, 2006.
[21] C. E. Rasmussen and Z. Ghahramani. Infinite mixtures of Gaussian process experts. T. G. Diettrich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14. The MIT Press, 2002
[22] L. Csató. Gaussian Processes - Iterative Sparse Approximations. PhD thesis. Neural Computing Research Group, Aston University.2002.
[23] Hyun-Chul Kim ,Zoubin Ghahramani.Bayesian Gaussian Process Classification with the EM-EP Algorithm .IEEE Trans. on PAMI,2006,28(12):1948-1959
[24] Ghadaki H, Dizaji R. Target track classification for airport surveillance radar (ASR). Radar 2006.New York:IEEE,2006,24-27
[25]朱张帆,丁建江.基于回波幅相分解的JEM特征提取方法.空军雷达学院学报,2006, 20(1):21-24
[26] Leung H, Wu J. Bayesian and Dempster–Shafer Target Identification for Radar Surveillance. IEEE Trans. on AES, 2000, 36 (2):432-447
[27] Pouliguen P, Lucas L, Muller F, Quete S et al. Calculation and analysis of electromagnetic scattering by helicopter rotating blades. IEEE Trans. on antennas and propagation,2002,50(10): 1193-1408
[28] Bell M R, Grubbs R A. JEM modeling and measurement for radar target identification. IEEE Trans on AES, 1993, 29(1):73-87
[29] Piazza E. Radar Signals Analysis and modellization presence of JEM application inthe to civilian ATC Radars. IEEE AES Systems Magazine,1999, 14(1):35-40.
[30] Martin J, Mulgrew B. Analysis of the theoretical radar return signal form aircraft propeller blades. The record of the IEEE International Conference Radar-90. New York:IEEE,1990, 569-572.
[31] Yang S, Yeh S. Electromagnetic backscattering from aircraft propeller blades. IEEE Trans on Magnetic,1997,33(3):1432-1435
[32] Martint J, Mulgrew B. Analysis of the effects of blade pitch on the radar return signal from rotating aircraft blades. IEE Conference Publication 365. NewYork: IEEE,1992, 446-449.
[33] Elshafei M, Akhtar S, Ahmed M S. Parametric models for helicopter identification, IEEE Trans on AES,2000, 36(4):1242-1252
[34] Melendez G J,Kesler S B. Spectrum estimation by neural networks and their use for target classification by radar. ICASSP-95. New York:IEEE,1995,3615-3618
[35] Moses R L, Carl J W. Autoregressive modeling of radar data with application to target identification. Radar Conference 1988.NewYork:IEEE,1988,220-224
[36] Pellegrini-S P F, Pardini C S. Radar Signals Analysis oriented to target characterization applied to civilian ATC radar. Radar 1992.NewYork:IEEE,1992,438-445
[37]丁建江,张贤达,低分辨雷达螺旋桨飞机回波调制特性的研究.电子与信息学报,2003,25(4): 460-465
[38]丁建江,张贤达,防空雷达螺旋桨飞机回波JEM特征的模型与分析.清华大学学报(自然科学版),2003,43(3):418-421
[39]陈凤,杜兰,保铮,刘宏伟.一种优化K近邻准则及在雷达HRRP目标识别中的应用.西电学报,2007,30(1):1-5.
[40] Chen Feng, Du Lan, Yuan Li, Bao Zheng. Radar HRRP target recognition using influence region of samples. Proceedings of 2006 International Conference on Radar. Oct.2006,1428-1431.
[1]马建华,刘宏伟,保铮.利用核匹配追踪算法进行雷达HRRP识别.西安电子科技大学学报,2005,32(1):84-88
[2]刘宏伟,杜兰,袁莉,保铮.雷达高分辨距离像识别研究进展.电子与信息学报, 2005,27(8):1328-1334
[3] Du Lan, Liu Hongwei, Bao Zheng, Radar HRRP target recognition based on higher order spectra. IEEE Trans. on S.P., 2005,53 (7):2359-2368.
[4]杜兰,保铮,邢孟道.飞机目标的雷达一维距离像特性研究.西安电子科技大学学报,2002,28(Sup.):14-19
[5]杜兰,刘宏伟,保铮.利用目标方位信息改善雷达距离像识别性能.系统工程与电子技术,2004,26(8):1040-1043
[6]边肇祺,张学工.模式识别(第二版).北京:清华大学出版社.2000: 30-31,136-140
[7] Li Hsueh-Jyh, Wang Yung-Deh, and Wang Long-huai. Matching score properties between rang profiles of high-resolution radar targets. IEEE Trans.on Antennas and Propagation,1996,44(4):444-452
[8] Li Hsueh-Jyh, Yang Sheng-Hui. Using rang profile as feature vector to identify aerospace objects. IEEE Trans, on Antennas and Propagation,1993, 41(3):261-268
[9] Gorshkov S A,Leschenko S P,Orlenko V M ,Sedyshev S Y and Shirman Y D.Radar Target Backscattering Simulation Software and User’s Manual. Artech House Publishers,Boston. London.2002.
[10]廖学军.基于高分辨距离的雷达目标识别.博士论文.西安电子科技大学, 1999.
[11]袁莉,刘宏伟,保铮.雷达高分辨距离像分类器的参数自适应学习算法.电子与信息学报,2007,30(1):1010-1015
[12] Chen Bo, Yuan Li, Liu Hongwei, Bao Zheng. Adaptively segmenting Angular sector for radar HRRP automatic target recognition. EURASIP Journal on Applied Signal Processing. Accepted.
[13]杜兰.雷达高分辨距离像目标识别方法研究.博士论文.西安电子科技大学,2007
[14]陈凤,杜兰,保铮,刘宏伟,雷达高分辨距离像自动目标识别方法的改进.电子与信息学报. 2007,29(6):1450-1454
[15] Csato Lehel. Gaussian processes - iterative sparse approximation. Ph.d Dissertation. Birmingham: Aston University. 2002.
[16] Williams C K. I., Rasmussen C. E. Gaussian processes for regression. NIPS-8.NewYork: MIT Press,1995,514-520
[17] Gibbs M, MacKay D. J. C. Variational Gaussian process classiffers. IEEE Transcations on NN, 2000,11(6): 1458-1464
[18] Csato Lehel, Manfred Opper. Sparse on-line Gaussian Processes. Neural Computation,2002,14(3):641-669.
[19]陈凤,杜兰,保铮,刘宏伟.雷达高分辨距离像自动目标识别方法的改进.电子与信息学报,2007,29(6):1450-1454
[1] Jacobs S. P.. Automatic target recognition using high-resolution radar range profiles. Ph. D. Dissertation. Washington University. 1999.
[2] Zwart, J., Heiden, R., Gelsema, S., and Groen, F. Fast translation invariant classification of HRR range profiles in a zero phase representation. IEE Proc.-Radar Sonar Navig., 2003, 150 (6): 411-418
[3] Du, L., Liu, H., Bao, Z., Zhang. J. Radar automatic target recognition using complex high-resolution range profiles. IET Radar, Sonar &Navig., 2007, 1(1):18-26.
[4] Bi, Zi., Wu, R., Li, J., and Williams, R. Joint super-resolution moving target feature extraction and stationary clutter suppression. IEE Proc.-Radar Sonar Navig., 2000, 147(1): 23–29
[5] Zhu, F., Zhang, X., Hu, Y., and Xie, D. Nonstationary Hidden Markov models for multiaspect discriminative feature extraction from radar targets.Trans. Signal Process., 2007, 55(5):2203-2214
[6] Haspert, K., Heagy, J., Sullivan, R. Template-based target identification andconfusion matrices. IEEE AES Sys. Mag.,2007, 22, (9):53-67
[7] Vespe, M., Baker, C.J., Griffiths, H. D. Automatic target recognition using multi-diversity radar. IET Radar, Sonar &Navig.,2007, 1(6): 470–478
[8] O'Sullivan, J. A., DeVore, M. D., Kedia, V., and Miller, M. I. SAR ATR performance using a conditionally Gaussian model. IEEE Trans. AES, 2001, 37, (1) ,91-108
[9] Kim, K. T. ,Seo, D. K, Kim, H. T. One-dimentional scattering centre extraction for efficient radar target classification. IEE Proc.-Radar Sonar Navig. 2001,148(5):285-296.
[10] Zyweck, A., Bogner, R.E.. Radar target classification of commercial aircraft.IEEE Trans. AES.1996, 32 (2):598-606
[11] Du, L., Liu, H., and Bao, Z.. Radar HRRP target recognition: parametric model and model selection. IEEE Trans. Signal Process., 2008, 56 (5): 1931-1944
[12] Webb A. statistical pattern recognition(second ed.). John wiley and Sons.2002.
[13] Du, L., Liu, H., Bao, Z., and Zhang, J..A Two-Distribution Compounded Statistical Model for Radar HRRP Target Recognition. IEEE Trans. Signal Process., 2006, 54(6): 2226–2238
[14] Li, H., Wang, Y., Wang L..Matching score properties between range profile of high-resolution radar targets.IEEE Trnas. Antennas. Propag.,1996,44(4):444-452
[15] Hudson, S., Psaltis, D..Correlation filters for aircraft identification form radar range profiles. IEEE Trans. AES,1993, 29(3):741-748
[16] Tipping, M. E., Bishop, C. M. Mixtures of principal component analysers. Neural Computation. 1999, 11(2):443-482
[17] Duda, R. O., Hart, P. E., and Stork, D. G.. Pattern classification(second ed.) .John Wiley and Sons.2001.
[18] Gorshkov, S. A., Leschenko, S. P., Orlenko, V. M., Sedyshev,S. Y., and Shirman, Y. D. Radar Target Backscattering Simulation Software and User’s Manual. Boston, MA: Artech House.2002.
[19] Shirman,Y. D.. Computer Simulation of Aerial Target Radar Scattering, Recognition, Detection, and Tracking. Boston, MA: Artech House.2002
[20]杜兰.雷达高分辨距离像目标识别方法研究.博士研究生学位论文.西安电子科技大学. 2007.
[21]陈凤,杜兰,保铮,刘宏伟.雷达高分辨距离像自动目标识别方法的改进.电子与信息学报,2007,29(6):1450-1454 .
[1]杜兰.雷达高分辨距离像目标识别方法研究.博士研究生学位论文.西安电子科技大学. 2007.
[2]袁莉.基于高分辨距离像的雷达目标识别方法研究.博士研究生学位论文.西安电子科技大学.2007.
[3] Du Lan, Liu Hongwei, Bao Zheng, Zhang Junying. A two-distribution compounded statistical model for radar HRRP target recognition. IEEE Trans. on Signal Processing, Jun. 2006, 54(6): 2226-2238.
[4] L. Du, H.W. Liu, Z. Bao. Radar HRRP statistical recognition: parametric model and model selection . IEEE Trans. on Signal Processing, 2008, vol. 56(5):1931–1944.
[5] Du Lan, Liu Hongwei, Bao Zheng. Radar HRRP statistical recognition based on hypersphere model. Submitted to Signal Processing.
[6] S. P. Jacobs, Automatic target recognition using high-resolution radar range profiles. Ph.D., Washington Univ. St. Louis, MO.1999.
[7] K. Copsey and A. R.Webb. Bayesian gamma mixture model approach to radar target recognition.IEEE Trans. Aerosp. Electron. Syst., 2003,39(4):1201–1217
[8] A. R. Webb.Gamma mixture models for target recognition. Pattern Recog., 2000, 33: 2045–2054
[9] M. Evans, N. Hastings, and B. Peacock.Statistical Distributions, Second ed. New York: Wiley, 1993.
[10] K. Fukunaga. Introduction to Statistical Pattern Recognition (2nd ed). Boston, MA: Academic. 1990.
[11] M. Skolnik, Introduction to Radar Systems(2nd ed.) New York: Mc- Graw-Hill. 1980.
[12] M. E. Tipping and C. M. Bishop.Mixtures of principal component analysers. Neural Comput.,1999,11(2): 443–482.
[13] Van der Heiden R., Groen F. C. A.. The Box-Cox Metric for Nearest Neighbour Classification Improvement. Pattern Recognition,1997, 30(2): 273-279.
[14] H. W. Liu Z. Bao. Radar HRR Profiles Recognition Based on SVM With Power-Transformed Correlation Kernel . Lecture Notes in Computer Science, 2004, 531–536.
[15]李世雄.代数方程与置换群.上海教育出版社.1981.
[16] R. A. Horn, C. R. Johnson. Matrix Analysis. Cambridge: Cambridge University Press.1985.
[17] Cardoso J F, Souloumiac A. Blind beamforming for non-Gaussian signals. Proc IEE, F,1993,140(6):362-370
[18] Belochrani A, Abed-Merain K, Cardoso J F,Moulines E. A blind sourse separation technique using seconde-order statistics. IEEE trans SignalProcessing,1997,45(2):434-444
[19] Golub G H, Van Loan C F. Matrix Computation(2nd ed). Baltimore: The John Hopkins University Press.1989
[20]张贤达.矩阵分析与应用.清华大学出版社.2004.
[1]杜兰.雷达高分辨距离像目标识别方法研究.博士论文.西安电子科技大学.2007.
[2] Chen Bo,Liu Hongwei,Chai Jing, et al.Large Margin Feature Weighting via Linear Programming. IEEE Tran. on Knowledage and data Enginneering, Accepted.
[3]陈凤,杜兰,保铮,刘宏伟.雷达高分辨距离像自动目标识别方法的改进.电子与信息学报, 2007,29(6):1450-1454
[4] Lehel Csato,Gaussian Processes Iterative Sparse Approximations.Ph.D thesis. Aston University.2002.
[5] Hyun-Chul Kim, Zoubin Ghahramani. Bayesian Gaussian Process Classification with the EM-EP Algorithm. IEEE Trans. on PAMI.2006,28(12):1948-1959
[6] L. Xu, M. I. Jordan, and G. E. Hinton. An alternative model for mixtures of experts, In Advances in Neural Information Processing Systems 7, MIT press,1995.
[7] Viswanath Ramamurti,Joydeep Ghosh. Structurally Adaptive Modular Networks for Nonstationary Environments.IEEE Tran. on neural networks 1999,10(1):152-160
[8] V. Tresp. Mixture of Gaussian processes. In Advances in Neural Information Processing Systems 13. MIT press, 2001.
[9] C.E. Rasmussen and Z. Ghahramani. Infinite mixtures of Gaussian process experts,In Advances in Neural Information Processing Systems 14, MIT press,2002.
[10] E.Meeds and S. Osindero. An Alternative Infinite Mixture Of Gaussian Process Experts,In Advances in Neural Information Processing Systems 13. MIT press, 2001.
[11] Carl Edward Rasmusen, Chris Williams. Gaussian process for machine learning.New York: MIT Press,2006
[12] Jacobs S.P.. Automatic target recognition using high-resolution radar range profiles. Ph.D. Dissertation. Washington University.1999
[13]裴炳南.高分辨雷达自动目标识别方法研究.博士论文.西安电子科技大学.2002.
[14] Berger J.Statistical Decision and Bayesian Analysis, New York,Springer,1985:1-617
[15] Jain A. K., Robert P.W., Duin ,et al. Statistical Pattern Recognition: A Review, IEEE Trains. PAMI,2000,22 (1): 4-34
[16] Declercq Arnaud, Piater J. H. online learning of gaussian mixture models: a two-level approach, VISAPP (1) 2008: 605-611
[17] Hall, P. and Hicks, Y. A. A method to add gaussian mixture models,Tech. Report,University of Bath,2005.
[18] Csato Lehel, Manfred Opper. Sparse on-line Gaussian Processes,Neural Computation,2002,14(3):641-669.
[1] Ghadaki H, Dizaji R. Target track classification for airport surveillance radar (ASR). Radar 2006.New York:IEEE,2006:24-27
[2]朱张帆,丁建江.基于回波幅相分解的JEM特征提取方法.空军雷达学院学报,2006, 20(1):21-24
[3] Leung H, Wu J. Bayesian and Dempster–Shafer Target Identification for Radar Surveillance. IEEE Trans. on AES, 2000, 36 (2):432-447
[4] Pouliguen P, Lucas L, Muller F, Quete S et al. Calculation and analysis of electromagnetic scattering by helicopter rotating blades. IEEE Trans. on antennas and propagation,2002,50(10): 1193-1408
[5] Bell M R, Grubbs R A. JEM modeling and measurement for radar target identification. IEEE Trans on AES, 1993, 29(1):73-87
[6] Piazza E. Radar Signals Analysis and modellization presence of JEM application in the civilian ATC Radars. IEEE AES Systems Magazine,1999, 14(1):35-40.
[7] Martin J, Mulgrew B. Analysis of the theoretical radar return signal form aircraft propeller blades. The record of the IEEE International Conference Radar-90, New York:IEEE, 1990:569-572.
[8] Yang S, Yeh S. Electromagnetic backscattering from aircraft propeller blades. IEEE Trans on Magnetic,1997,33(3):1432-1435
[9] Martint J, Mulgrew B. Analysis of the effects of blade pitch on the radar return signal from rotating aircraft blades. IEE Conference Publication 365,NewYork: IEEE,1992:446-449.
[10] Elshafei M, Akhtar S, Ahmed M S. Parametric models for helicopter identification. IEEE Trans on AES,2000, 36(4):1242-1252
[11] Melendez G J,Kesler S B. Spectrum estimation by neural networks and their use for target classification by radar. ICASSP-95. New York:IEEE,1995:3615-3618
[12] Moses R L, Carl J W. Autoregressive modeling of radar data with application to target identification. Radar Conference 1988,NewYork:IEEE,1988:220-224
[13] Pellegrini-S P F, Pardini C S. Radar Signals Analysis oriented to target characterization applied to civilian ATC radar. Radar 1992.NewYork:IEEE,1992:438-445
[14]丁建江,张贤达,低分辨雷达螺旋桨飞机回波调制特性的研究.电子与信息学报,2003,25(4): 460-465
[15]丁建江,张贤达,防空雷达螺旋桨飞机回波JEM特征的模型与分析.清华大学学报(自然科学版),2003,43(3):418-421
[16]徐仲,张凯院,陆金.Toeplitz矩阵类矩阵的快速算法.西安:西北工业大学出版社,2006:212-255
[17]王贵平,鲍长春,张鹏.基于奇异值分解的低速率波形内插语音编码算法.电子学报,2006,34(1):135-139
[18] Duda R O, Hart P E, and Stork DG. Pattern classification,2nd ed. New York: John Wiley and Sons, 2001:259-264
[19] Stove A. A doppler-based target classifier using linear discriminants and principal components.High Resolution Imaging and Target Classification.London:IET,2006:171-176
[20] Jahangir M, Pointing K M,O'Loghlen J W. A robust doppler classification technique based on Hidden Markov Models. Processing Radar 2002.London: IEE, 2002:162-166.
[21] Jahangir M, Ponting K M, O'Loghlen J W. Correction to "Robust Doppler classification technique based on Hidden Markov models. IEE Proc.-Radar Sonar Navig,150(5),2003,387.
[22] Ji H, Li J, Xie W. Bispectrum Based Radar Target Classification. ICSP’98.NewYorK:IEEE,1992:419-422
[23] Andric M, Durovic Z, Zrnic B. Ground surveillance radar target classification based on fuzzy logic approach. EROCON 2005. NewYork:IEEE,2005:1390-1392
[24] Bullard BD,Dowdy P C . Pulse doppler signature of a rotary wing aircraft. Aerospace and Electronic Systerms Magaziene,1991, 36 (5):28-30
[25] Yoon S, Kim B, Kim Y, Helicopter classification using time-frequency analysis, Electronics Letters,2000,36(22):1871-1872
[26] Linde G. Use of wideband waveforms for target recognition with surveillanceradars.IEEE international radar conference 2000, NewYork:IEEE, 2000:128-133
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