基于高分辨距离像的雷达自动目标识别方法研究
|
摘要
雷达目标的高分辨距离像(HRRP)可以反映目标散射点沿距离方向的分布情况,提供了目标重要的结构信息,被广泛用作雷达目标的分类与识别,成为雷达自动目标识别研究领域的一个热点。同时,随着军事需求的日益迫切,需要研究人员加快对目标识别的理论研究向工程实现迈进。因此,本论文主要围绕着国防预研及国家自然科学基金的相关项目,针对雷达高分辨距离像目标识别,从在杂波和噪声环境下的HRRP稳健性识别、基于变分贝叶斯(VB)方法的分类器设计以及对HRRP的特征提取与分层目标识别等三个方面进行了的研究。
本论文的主要内容可概括为如下六个部分:
第一部分,简要给出了雷达自动目标识别的基本概念,并列举了国内外基于HRRP自动目标识别的研究进展,介绍了本文的研究工作。
第二部分,研究了HRRP在杂波情况下如何保持稳健性识别性能的问题,关键技术是如何抑制杂波。与以检测为目的的杂波抑制相比,宽带目标识别雷达的杂波抑制需要在抑制杂波的同时,尽可能地保持目标信号的结构信息不变,这样才能进行下一步的目标识别任务。为达到这一目的,我们先后提出了三种不同的宽带目标识别雷达的杂波抑制方法。(1)设计滤波器直接在多普勒域将杂波滤除。该算法主要是利用杂波的起伏速度通常不是很大,在脉间的相关性比较强,因此可以通过在多普勒域杂波抑制后,再将信号变换回时域,进行相干积累,提高目标的信噪比。(2)在宽带雷达下,目标速度较大时,容易出现越距离单元走动(MTRC)的现象,方法1其实是没有考虑这一点的,因此我们进而提出先利用keystone变换校正目标MTRC,然后再对其采用方法1来抑制杂波,另外针对目标出现多普勒模糊而杂波没有模糊时,采用在频率-多普勒域直接将目标信号部分提取出来的方法来降低杂波的影响。(3)针对目标出现MTRC的现象,提出了另外的解决思路,不需要通过keystone变换校正MTRC,在频率-多普勒域利用Hough变换将目标信号部分的线段提取出来,同时如果可以大致估计出目标的速度,还可以采用更加简单的方法来处理,即将目标做运动补偿后,在频率-多普勒域提取对应的目标信号部分。
第三部分,研究了在噪声背景下的HRRP稳健识别问题。当目标与雷达距离较远时,其信噪比将会降低,因此识别算法对噪声的稳健性是HRRP目标识别在实际应用中需要解决的一个问题。我们基于概率主分量分析(PPCA)和自适应高斯分类器(AGC)模型分别提出了两种不同的算法,使得被噪声污染的测试样本能够较好地匹配在弱噪声样本条件下训练出来的模板。
第四部分,研究了将变分贝叶斯(VB)算法结合目前常用的一些统计模型来解决雷达HRRP目标识别问题。VB方法为近些年被广泛用于近似求解Bayes积分的方法,通过将Bayes积分表达式中所有参数和隐变量的联合概率分布简化为各个参数以及隐变量之间概率分布的乘积,即假设各参数以及隐变量是相互独立的,这样积分表达式的值就可以利用简单的形式来代替其下界,通过不断优化参数的值来提高其下界,使得下界不断逼近该积分表达式的真实值。基于VB方法,我们将Gaussian Mixture模型和混合因子分析(FA)模型应用到雷达HRRP目标识别中来,取得了不错的效果。
第五部分,利用对HRRP提取的新特征,对分层雷达目标识别做了相关研究。由于雷达HRRP可以反映目标散射点沿距离方向的分布情况,因此我们提取了关于目标尺寸大小的结构特征,即目标的支撑区长度。利用该特征首先对目标的大小做出初步分类,然后再利用常规目标识别算法进行更加精确的型号识别。同时针对螺旋桨飞机相邻回波的能量变化要大于喷气式飞机这一特点,我们提取了相邻回波之间相对能量差值大小这一特征来初步区分这两类飞机。
第六部分,对全文工作进行了总结,并对下一步需要研究的工作提出了建议。
Target high-resolution range profile (HRRP) represents the projection of the complex returned echoes from the target scattering centers onto the radar line-of-sight (LOS). It contains the target structure signatures, such as target size, scatterer distribution, etc., and thereby radar HRRP target recognition has received intensive attention from the radar automatic target recognition (RATR) community. Due to the increasing military demand, RATR are required to stride to practical realization from theoretical. In this dissertation, the theory and techniques for radar HRRP target recognition are researched from the three aspects, i.e. robust of HRRP recognition performance under the clutter and noise, the classifier designing based on variational Bayesian (VB), feature extraction and layered algorithm for target recognition, which are supported by Advanced Defense Research Programs of China and National Science Foundation of China.
The main content of this dissertation is summarized as follows:
The first part begins with a brief introduction of the fundamental theories of RATR and reviews some related work of other institutes. Then the main work of this thesis is introduced.
The second part focuses on the robustness of HRRP recognition performance under the clutter environment. The key point is how to suppress clutter. Compared with the clutter suppression for target detection, clutter suppression for wideband target recognition radar requires that the target structure signatures are not changed after the clutter is suppressed. We present three methods of clutter suppression for wideband target recognition radar to achieve this purpose. (1) Clutter is suppressed by a filter in Doppler domain. This algorithm mainly exploits the fact that the velocity of clutter is small, and the correlation of clutter between different pulses is high. After the clutter suppression, we can transform the signal to the time domain, and then perform coherent accumulation, aiming at improving the signal-to-noise ratio (SNR) by. (2) In wideband radar, the target’s migration though resolution cells (MTRCs) will occur when the velocity is high. But MTRC is not considered in Algorithm 1. Thereby, we utilize keystone formatting to mitigate the MTRCs, and then suppress clutter by algorithm1. Otherwise, if target has Doppler ambiguity while clutter does not, we extract the target directly to reduce the effect of clutter in Doppler-frequency domain. (3) If MTRC occurs, we can utilize the Hough transform to extract the line segment of target in Doppler-frequency domain even though MTRC is not mitigated via keystone formatting. A simple method is proposed if the velocity of target can be approximately estimated, which is extracting the line segment of target in Doppler-frequency domain after motion compensation.
The third part is contributed to noise robust in HRRP target recognition. The SNR will be decreased when target is far away from radar, and therefore, the robustness study of HRRP recognition algorithm is necessary. In this part, based on PPCA model and AGC model, a robust algorithm for HRRP statistical recognition is presented when test SNR is lower than training SNR.
The fourth part focuses on radar HRRP statistical recognition based on VB. VB method is widely used to approximately resolve Bayesian integral in recent decade. On the assumption that parameters and hidden variables are independent of each other, the jointly probability distribution over all parameters and hidden variables can be approximated with a simpler distribution which is a lower bound of original Bayesian integral. The lower bound is increased by optimizing parameters, and the aim is to approximate the real value of original Bayesian integral. We apply Gaussian mixture models and mixtures of factor analyzers model to radar HRRP statistical recognition based on VB method, and obtain a good performance with measured radar data.
In the fifth part, utilizing the new feature extracting from HRRP, the layered radar target recognition is focused. Due to the fact that HRRP represents the projection of the complex returned echoes from the target scattering centers onto the radar line-of-sight (LOS), we extract target size, one of the target structure signatures. First, we utilize this feature to classify different targets by their size, and then identify them exactly by normal target recognition algorithm. In addition, we can distinguish propeller-driven aircraft from jet plane by relative difference energy between coherent echoes, because the relative difference energy between coherent echoes of propeller-driven aircraft is large than jet plane’s.
Finally, we summarize the main results of the study which have led to this thesis; additionally, some conclusions are drawn and some recommendations for future work are given.
本论文的主要内容可概括为如下六个部分:
第一部分,简要给出了雷达自动目标识别的基本概念,并列举了国内外基于HRRP自动目标识别的研究进展,介绍了本文的研究工作。
第二部分,研究了HRRP在杂波情况下如何保持稳健性识别性能的问题,关键技术是如何抑制杂波。与以检测为目的的杂波抑制相比,宽带目标识别雷达的杂波抑制需要在抑制杂波的同时,尽可能地保持目标信号的结构信息不变,这样才能进行下一步的目标识别任务。为达到这一目的,我们先后提出了三种不同的宽带目标识别雷达的杂波抑制方法。(1)设计滤波器直接在多普勒域将杂波滤除。该算法主要是利用杂波的起伏速度通常不是很大,在脉间的相关性比较强,因此可以通过在多普勒域杂波抑制后,再将信号变换回时域,进行相干积累,提高目标的信噪比。(2)在宽带雷达下,目标速度较大时,容易出现越距离单元走动(MTRC)的现象,方法1其实是没有考虑这一点的,因此我们进而提出先利用keystone变换校正目标MTRC,然后再对其采用方法1来抑制杂波,另外针对目标出现多普勒模糊而杂波没有模糊时,采用在频率-多普勒域直接将目标信号部分提取出来的方法来降低杂波的影响。(3)针对目标出现MTRC的现象,提出了另外的解决思路,不需要通过keystone变换校正MTRC,在频率-多普勒域利用Hough变换将目标信号部分的线段提取出来,同时如果可以大致估计出目标的速度,还可以采用更加简单的方法来处理,即将目标做运动补偿后,在频率-多普勒域提取对应的目标信号部分。
第三部分,研究了在噪声背景下的HRRP稳健识别问题。当目标与雷达距离较远时,其信噪比将会降低,因此识别算法对噪声的稳健性是HRRP目标识别在实际应用中需要解决的一个问题。我们基于概率主分量分析(PPCA)和自适应高斯分类器(AGC)模型分别提出了两种不同的算法,使得被噪声污染的测试样本能够较好地匹配在弱噪声样本条件下训练出来的模板。
第四部分,研究了将变分贝叶斯(VB)算法结合目前常用的一些统计模型来解决雷达HRRP目标识别问题。VB方法为近些年被广泛用于近似求解Bayes积分的方法,通过将Bayes积分表达式中所有参数和隐变量的联合概率分布简化为各个参数以及隐变量之间概率分布的乘积,即假设各参数以及隐变量是相互独立的,这样积分表达式的值就可以利用简单的形式来代替其下界,通过不断优化参数的值来提高其下界,使得下界不断逼近该积分表达式的真实值。基于VB方法,我们将Gaussian Mixture模型和混合因子分析(FA)模型应用到雷达HRRP目标识别中来,取得了不错的效果。
第五部分,利用对HRRP提取的新特征,对分层雷达目标识别做了相关研究。由于雷达HRRP可以反映目标散射点沿距离方向的分布情况,因此我们提取了关于目标尺寸大小的结构特征,即目标的支撑区长度。利用该特征首先对目标的大小做出初步分类,然后再利用常规目标识别算法进行更加精确的型号识别。同时针对螺旋桨飞机相邻回波的能量变化要大于喷气式飞机这一特点,我们提取了相邻回波之间相对能量差值大小这一特征来初步区分这两类飞机。
第六部分,对全文工作进行了总结,并对下一步需要研究的工作提出了建议。
Target high-resolution range profile (HRRP) represents the projection of the complex returned echoes from the target scattering centers onto the radar line-of-sight (LOS). It contains the target structure signatures, such as target size, scatterer distribution, etc., and thereby radar HRRP target recognition has received intensive attention from the radar automatic target recognition (RATR) community. Due to the increasing military demand, RATR are required to stride to practical realization from theoretical. In this dissertation, the theory and techniques for radar HRRP target recognition are researched from the three aspects, i.e. robust of HRRP recognition performance under the clutter and noise, the classifier designing based on variational Bayesian (VB), feature extraction and layered algorithm for target recognition, which are supported by Advanced Defense Research Programs of China and National Science Foundation of China.
The main content of this dissertation is summarized as follows:
The first part begins with a brief introduction of the fundamental theories of RATR and reviews some related work of other institutes. Then the main work of this thesis is introduced.
The second part focuses on the robustness of HRRP recognition performance under the clutter environment. The key point is how to suppress clutter. Compared with the clutter suppression for target detection, clutter suppression for wideband target recognition radar requires that the target structure signatures are not changed after the clutter is suppressed. We present three methods of clutter suppression for wideband target recognition radar to achieve this purpose. (1) Clutter is suppressed by a filter in Doppler domain. This algorithm mainly exploits the fact that the velocity of clutter is small, and the correlation of clutter between different pulses is high. After the clutter suppression, we can transform the signal to the time domain, and then perform coherent accumulation, aiming at improving the signal-to-noise ratio (SNR) by. (2) In wideband radar, the target’s migration though resolution cells (MTRCs) will occur when the velocity is high. But MTRC is not considered in Algorithm 1. Thereby, we utilize keystone formatting to mitigate the MTRCs, and then suppress clutter by algorithm1. Otherwise, if target has Doppler ambiguity while clutter does not, we extract the target directly to reduce the effect of clutter in Doppler-frequency domain. (3) If MTRC occurs, we can utilize the Hough transform to extract the line segment of target in Doppler-frequency domain even though MTRC is not mitigated via keystone formatting. A simple method is proposed if the velocity of target can be approximately estimated, which is extracting the line segment of target in Doppler-frequency domain after motion compensation.
The third part is contributed to noise robust in HRRP target recognition. The SNR will be decreased when target is far away from radar, and therefore, the robustness study of HRRP recognition algorithm is necessary. In this part, based on PPCA model and AGC model, a robust algorithm for HRRP statistical recognition is presented when test SNR is lower than training SNR.
The fourth part focuses on radar HRRP statistical recognition based on VB. VB method is widely used to approximately resolve Bayesian integral in recent decade. On the assumption that parameters and hidden variables are independent of each other, the jointly probability distribution over all parameters and hidden variables can be approximated with a simpler distribution which is a lower bound of original Bayesian integral. The lower bound is increased by optimizing parameters, and the aim is to approximate the real value of original Bayesian integral. We apply Gaussian mixture models and mixtures of factor analyzers model to radar HRRP statistical recognition based on VB method, and obtain a good performance with measured radar data.
In the fifth part, utilizing the new feature extracting from HRRP, the layered radar target recognition is focused. Due to the fact that HRRP represents the projection of the complex returned echoes from the target scattering centers onto the radar line-of-sight (LOS), we extract target size, one of the target structure signatures. First, we utilize this feature to classify different targets by their size, and then identify them exactly by normal target recognition algorithm. In addition, we can distinguish propeller-driven aircraft from jet plane by relative difference energy between coherent echoes, because the relative difference energy between coherent echoes of propeller-driven aircraft is large than jet plane’s.
Finally, we summarize the main results of the study which have led to this thesis; additionally, some conclusions are drawn and some recommendations for future work are given.
引文
[1] Skolnik M.. Introduction to Radar Systems. Second Edition, New York: McGraw-Hill. 1980.
[2]向敬成,张明友.雷达系统.北京:电子工业出版社, 2001.
[3]丁鹭飞,耿富录.雷达原理.西安:西安电子科技大学出版社, 2002.
[4] Skolnik M. I.主编,王军等译.雷达手册(第二版).北京:电子工业出版社, 2003.
[5] Jacobs S. P.. Automatic target recognition using high-resolution radar range profiles. Ph. D. Dissertation. Washington University. 1999.
[6]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.
[7]周代英.雷达目标一维距离像识别研究.博士研究生学位论文.电子科技大学. 2001.
[8]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.
[9]付耀文.雷达目标识别技术研究.博士研究生学位论文.国防科技大学. 2003.
[10]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.
[11]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.
[12]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.
[13]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.
[14]王彩云.雷达高分辨距离像目标检测与识别研究.博士研究生学位论文.北京航空航天大学. 2008.
[15]陈凤.基于HRRP和JEM信号的雷达目标识别技术研究.博士研究生学位论文.西安电子科技大学. 2009.
[16]柯有安.雷达目标识别(上),国外电子技术, 1978, No.4, pp: 22-30.
[17]柯有安.雷达目标识别(下),国外电子技术, 1978, No.5, pp: 14-20.
[18]保铮.雷达目标识别与分类技术.国防科技项目技术报告(高分辨部分).西安电子科技大学雷达信号处理重点实验室, 1997.
[19]杜兰,保铮,邢孟道.直升机雷达回波的分析与检测.西安电子科技大学学报, 2003, Vol.30 (5), pp: 574-579.
[20]杜兰,刘宏伟,保铮.一种基于距离-多谱勒二维联合的群目标分辨方法.电子学报. 2004, Vol.32 (6), pp: 881-885.
[21]保铮,邢孟道,王彤.雷达成像技术.北京:电子工业出版社,2005.
[22] 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), pp: 444-452.
[23] Li H.-J., Yang S.-H.. Using range profiles as feature vectors to identify aerospace objects. IEEE Trans. A.P., 1993, 41(3), pp: 261-268.
[24] W.G.Carrara, R.S.Goodman, R.M.Majewski. Spotlight synthetic aperture radar: signal processing algorithms. Boston: Artech House, 1995.
[25]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.
[26]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.
[27]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), pp: 1328-1334.
[28] Moving and stationary target acquisition and recognition, Program technology review. Denver, CO. Nov. 1996. http:// www.mbvlab.wpafb.af.mil/public/MBVDATA.
[29] Marcoz Y., Miguel T.T., Feature Selection using Genetic Algorithms in SAR Airborne Imagery. http://www.cim.mcgill.ca,2001.
[30] Punkle P. et al.. Multi-aspect target detection for SAR imagery using Hidden Markov models. IEEE Trans. A.E.S.. 2001, Vol.39(1), pp: 46-55.
[31] 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, Vol. 3721, pp: 355-360.
[32] Musman S., Kerr D., Bachmann C.. Automatic Recognition of ISAR Images. IEEE Trans. A.E.S.. 1996, Vol.32(4), pp: 1392-1403.
[33] Rosenbach K., Schiller J., Construction and Test of a Classifier for Non-Cooperative Air-Target Identification based on 2-D-ISAR Images. Proceedings of IRS'98 International Radar Symposium. Sep. 1998, pp: 1023-1033.
[34] Maki A. et al.. ISAR Image Analysis by Subspace Method: Automatic Extraction and Identification of Ship Profile. IEEE Radar 2001, pp: 523-528.
[35] Maki A. et al.. Automatic Ship Identification in ISAR Imagery: An On-lineSystem using CMSM. IEEE Radar 2002, pp: 206-211.
[36] Mitchell R. A., Dewall R.. Overview of high range resolution radar target identification. In Proceedings of Automatic Target Recognition Working Group. 1994.
[37] Eesterkamp J.. A Framework for 1D HRR ATR evaluation. AFRL Technical Report. Aug.1998.
[38] Anthony Zywechm and Robert E. Bogner. Radar target classification of commercial aircraft. IEEE Trans. on Aerospace and Electronic Systems, 1996, Vol. 32(2), pp: 598-606.
[39] Anthony Zywechm and Robert E. Bogner. Coherent averaging of range profiles. IEEE International Radar Conference, 1995, pp: 456-461.
[40] Mitchell R. A., Dewall R.. Overview of high range resolution radar target identification. In Proceedings of Automatic Target Recognition Working Group,1994.
[41] Heiden R, Groen F.C.A The box-cox metric for nearest neighbor classification improvement. Pattern Recognition. 1997, pp: 273-279.
[42]刘宏伟,马建华,保铮. Box-Cox变换提高雷达高分辨距离像识别性能的物理机理分析.第九届全国雷达年会,烟台, 2004,8, pp: 354-357.
[43] Webb A. R.. Gamma Mixture Models for Target Recognition. Pattern Recognition. Vol. 33, 2000, pp: 2045-2054.
[44] S. P. Jacobs, J.A. O’sollivan, Automatic target recognition using sequences of high resolution radar range-profiles. IEEE Trans. AES,2000, Vol.36, No. 2, pp: 364-380.
[45] Yuan Li, Liu H W, Bao Z. Automatic target recognition using multiple radar high range resolution profiles. CIE International Conference on Radar, Shanghai, China, 2006, pp: 846-894.
[46] R. Williams, J. Westerkamp, et al, Automatic target recognition of time critical moving targets using 1D high range resolution (HRR) radar, IEEE AES Magazine, 2000, April, pp: 37-43.
[47] 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, Vol. 38(4), pp: 1230-1242.
[48] P. Bharadwaj, P. Runkle, L. Carin, et al, Multiaspect classification of airborne targets via physics-based HMMs and matching pursuits, IEEE Trans. AES, 2001, Vol.37(2), pp: 595-606.
[49]王雪松.宽带极化信息处理研究.博士研究生学位论文.国防科学技术大学. 1999.
[50] Du Lan, Liu Hongwei, Bao Zheng, Zhang Junying. A two-distribution compounded statistical model for radar HRRP target recognition. IEEE Trans. on Signal Processing. 2006, 54 (6), pp: 2226-2238.
[51] M. Tipping and C.M. Bishop. Probabilistic Principal Component Analysis. J. Royal Statistical Soc. B, 1999,21(3), pp: 611-622.
[52] Du Lan, Liu Hongwei, Bao Zheng. Radar HRRP statistical recognition: parametric model and model selection . IEEE Trans. on Signal Processing, 2008, 56(5), pp: 1931–1944.
[53] Rubin D., Thayer D.. EM algorithms for ML factor analysis. Psychometrika, 1982,47(1), pp: 69-76.
[54] Xing M.-D., Bao Z., Pei B.. The properties of high-resolution range profiles. Optical Engineering. 2002, 41(2), pp: 493-504.
[55] Rong Hu and Zhaoda Zhu, Research on Radar Classification based on High Resolution Range Profiles. Proceedings of Aerospace and Electronics Conference, 1997, 2, pp: 951-955.
[56] X. D. Zhang, Y. Shi, Z. Bao. A new feature vector using selected bispectra for signal classification. IEEE Trans on SP, 2001, 49(9), pp: 1875-1885.
[57] 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), pp: 2359-2368.
[58] Hongwei Liu, Zheng Bao, Radar HRR profiles recognition based on SVM with power-transformed-correlation kernel, LNCS 3174 (I) , 2004, pp:531-536.
[59] 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, 2005, pp: 528-533.
[60] Bo Chen, Hongwei Liu, Zheng Bao. Kernel Subclass Discriminant Analysis. Neurocomputing, 2007, 71, pp: 455-458.
[61] Bo Chen, Hongwei Liu, Zheng Bao. A Kernel Optimization Method Based on the Localized Kernel Fisher. Pattern Recognition, 2008, 41(3), pp: 1098-1109.
[62]刘敬,张军英,杜兰.基于最大相关系数的雷达高分辨距离像分帧方法.电子与信息学报, 2008,30(9), pp: 2060-2064.
[63]侯庆禹,刘宏伟,保铮.宽带目标识别雷达的杂波抑制.现代雷达, 2007, 29,(9), pp: 44-47.
[64]侯庆禹,刘宏伟,保铮.一种新的宽带目标识别雷达杂波抑制方法.西安电子科技大学学报, 2008,35,(5), pp: 769-773.
[65]侯庆禹,刘宏伟,保铮.基于keystone变换方法的宽带目标识别雷达杂波抑制.系统工程与电子技术, 2009,31,(1),pp.49-53.
[66] Q.Y. Hou, H.W. Liu, F. Chen and Z. Bao. Adaptive Statistical Model for Radar HRRP Recognition. IET Radar Conference 2009, Accepted.
[67] Feng Chen, Hongwei Liu, Qingyu Hou, Zeng Bao. Radar automatic target recognition for alterable noise environment. IET Radar conference 2009. Accepted.
[68] Cohen M. N.. Variability of ultra-high range resolution profiles and some implications for target recognition. SPIE. 1992. pp: 256-266.
[69] Li H J, Farhat N H, Shen Y, et al. Image understanding and prediction in microwave diversity imaging. IEEE Trans. on AP, 1989, 37(8), pp: 1048-1057.
[70] Li H J, Liu T Y, Yang S H. superhigh imaging resolution for microwave imaging. Int. J. Imaging Systems and Technol. 1990, vol(2), pp: 37-46.
[71] Zyweck A., Bogner R. E.. Radar target classification of commercial aircraft. IEEE Trans. A.E.S.. 1996, Vol.32(2), pp: 598-606.
[72] Scott Hudson, Demetri Psaltis. Correlation Filters for Aircraft Identification From Radar Range Profiles. IEEE Trans. Aerospace and Electronics System, 1993, Vol. 29,No.3,pp: 741-748.
[73]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, 2008, 30(1), pp: 198-202.
[74] C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. The MIT Press, Cambridge, MA, 2006.
[75] L. Csató. Gaussian Processes - Iterative Sparse Approximations. PhD thesis. Neural Computing Research Group, Aston University. 2002.
[76] Hyun-Chul Kim, Zoubin Ghahramani. Bayesian Gaussian Process Classification with the EM-EP Algorithm .IEEE Trans. on PAMI, 2006, 28(12), pp: 1948-1959.
[77] Chen Bo, Liu Hongwei, Yuan Li, Bao Zheng. Adaptively Segmenting Angular Sectors for Radar HRRP ATR. EURASIP Journal on Advances in Signal Processing Volume 2008 (2008), Article ID 641709, 6 pages.
[78]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, Vol. 26 (8), pp: 1040-1043.
[79]杜兰,保铮,刘宏伟,高分辨距离像雷达自动目标识别的模板匹配问题,第九届全国雷达学术年会论文集, 2004, 8, pp: 509-512.
[80] 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, Vol.150(6), pp: 411-418.
[81]陈渤,刘宏伟,保铮.基于三种不同绝对对齐方法的分类器的分析与研究.现代雷达,2006, Vol. 28 (3), pp: 58-62.
[82] 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, Vol.50(3), pp: 325-337.
[83]袁莉,刘宏伟,保铮.基于中心矩特征的雷达HRRP自动目标识别[J].电子学报.2004,32(12), pp: 2078-2081.
[84] Duda R. O., Hart P. E., Stork D. G.. Pattern Classification. Second Edition. New York: John Wiley and Sons. 2001.
[85] Bo Chen, Hongwei Liu, Zheng Bao. An Efficient Kernel Optimization Method for Radar High-resolution Range Profile Recognition, EURASIP Journal on Applied Signal Processing. Vol. 2007, Article ID 49597, 10 pages, 2007.
[86]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), pp: 3459-3463.
[87] Li J., Stoica P.. Efficient mixed-spectrum estimation with application to feature extraction. IEEE Trans. on Signal Processing. Feb. 1996, Vol.42(2), pp: 281-295.
[88] Bharadwaj P., Runkle P., Carin L.. Multiaspct classification of airborne targets via physics-based HMMs and matching pursuits. IEEE Trans. on A.E.S.. 2001, Vol.37(2), pp: 595-606.
[89]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, Vol.33 (3), pp: 411-415.
[90]杜兰,刘宏伟,保铮,张军英.一种用于雷达HRRP功率谱的加权特征压缩方法.西安电子科技大学学报. 2006, Vol. 33(2), pp: 173-177.
[91] Bo Chen, Hongwei Liu, Zheng Bao. A Kernel Optimization Method Based on the Localized Kernel Fisher, Pattern Recognition, 2008, Vol. 41(3), pp: 1098-1109.
[92] M. Tipping and C.M. Bishop. Probabilistic Principal Component Analysis. J. Royal Statistical Soc. B, 1999,21(3), pp: 611-622
[93] Yu X. Neural network directed bayes decision rule for moving target class. IEEE Trans on AES, 2000, 36(1), pp: 176-188.
[94] C. Cortes and V. Vapnik, Support-vector networks. Machine Learning, 1995, Vol. 20, pp: 273-297.
[95] Tipping M E. Sparse Bayesian learning and relevance vector machine. Journal of Machine Learning Research, 2001,(6), pp:211-244.
[96] Ghahramani Z. and Beal M.: Variational inference for Bayesian mixture of factor analysers. Advances in NIPS, 2000, 12, pp: 449-455.
[97] Attias H. Inferring parameters and structure of latent variable models by variational Bayes. Proceeding of 15th Conference on Uncertainty in Artificial Intelligence, 1999.
[98] Mattew J.Beal. Variational Algorithms for Approximate Bayesian Inference. Ph.D. Dissertation. University of Cambrige, UK, 2003.
[99]陈文驰,保铮,刑孟道.基于Keystone变换的低信噪比ISAR成像.西安电子科技大学学报(自然科学版), 2003, 30, (2), pp: 155-159.
[100]章毓晋.图像处理和分析.清华大学出版社, 1999.
[101]郭科,陈聆,魏友华.最优化方法及其应用.高等教育出版社, 2007.
[102] Mood A M, Graybill F A, Boes D C. Introduction to the Theory of Statistics (3rd Ed.). McGraw-Hill, 1974.
[1]贺知明,黄巍,向敬成.宽带雷达中消除“盲速”的动显方法研究.电子科技大学学报,2003, 32, (6), pp: 593-597.
[2]贺知明,向敬成,黄巍. NMTI方法在宽带雷达系统中的应用.电子与信息学报,2003, 25, (12), pp: 1628-1633.
[3]贺知明,黄巍,张一冰,向敬成.适用于宽带雷达的非相干杂波抑制方法.系统工程与电子技术, 2004, 26, (5), pp: 572-574.
[4]侯庆禹,刘宏伟,保铮.宽带目标识别雷达的杂波抑制.现代雷达, 2007, 29, (9), pp:44-47.
[5] Perrv R P. Dipietro R C. Fante R L. SAR Imaging of Moving Targets. IEEE Trans on AES, 1999, 35, (1), pp:188-199.
[6]陈文驰,保铮,刑孟道.基于Keystone变换的低信噪比ISAR成像.西安电子科技大学学报(自然科学版), 2003, 30, (2), pp:155-159.
[7] Mengdao Xing, Renbiao Wu, Jinqiao Lan, Zheng Bao. Migration Through Resolution Cell Compensation in ISAR Imaging. IEEE GRS Letter, 2004, 1, (2), pp:141-144.
[8]陈文驰,刑孟道.基于Keystone变换的多目标ISAR成像算法.现代雷达, 2005, 27, (3), pp:40-42.
[9]侯庆禹,刘宏伟,保铮.基于keystone变换方法的宽带目标识别雷达杂波抑制.系统工程与电子技术, 2009, 31, (1), pp:49-53.
[10] Duda R.O,Hart P.E. Use of Hough transform to detect lines and curves in pictures.comm.of ACM, 1990, 15, (1), pp:11-15.
[11] Xu.L, Oja.E, Kultanen P. A new curve Detection Method: Randomized HoughTransform (RHT).Pattern Recognition Letters, 1990, 11, (5), pp: 331-338.
[12]章毓晋.图像处理和分析.清华大学出版社, 1999, pp:187-188.
[13]华顺刚,逄岭,欧宗瑛.一种改进的Hough变换算法及图像特征点提取.中国图像图形学报, 2003, 8, (A), pp:153-156.
[14]徐牧,王雪松,肖顺平.基于Hough变换与目标主轴提取的SAR图像目标方位角估计方法.电子与信息学报, 2007, 29, (2),pp:370-374.
[15] Zhou Jianxiong, Zhao Hongzhong, etc. Extracting Global 3D Scattering Center Model of Radar Target from Multiple HRR profiles. Radar conference, 2007, pp:811-816.
[16]侯庆禹,刘宏伟,保铮.一种新的宽带目标识别雷达杂波抑制方法.西安电子科技大学学报, 2008, 35, (5), pp:769-773.
[17] H-J. Li, S.-H. Yang. Using range profiles as features vectors to identify aerospace objects. IEEE Trans. A.P., 1993, 41, (3), pp: 261-268.
[18]保铮,刑孟道,王彤.雷达成像技术.北京:电子工业出版社, 2005, pp:35-36.
[19]杜兰.飞机目标的雷达回波特性研究.西安电子科技大学硕士学位论文, 2004.
[20] V.Anastassopoulos, G.A.Lampoulos, A.Drosoulos, M.Rey. High Resolution Radar Clutter Statistics .IEEE. Trans on AES, 1999, 35, (1), pp: 43-60.
[21] David A. Shnidman. Generalized Radar Clutter Model. IEEE. Trans on AES, 1999, 35, (3), pp: 857-865.
[22]蒋咏梅,陆铮.相关非高斯分布杂波的建模与仿真.系统工程与电子技术,1999, 21, (10), pp:27-30.
[23]杨凤凤,周智敏.基于ZMNL法的雷达杂波仿真.现代雷达, 2003, 9, pp:22-24.
[24]胡航,靖涛.一种二阶递归MTI滤波器的参数设计方法.哈尔滨工业大学学报, 2001, 33, (3), pp:375-377.
[25]陈建春,耿富录.自适应运动杂波抑制技术.西安电子科技大学学报, 1999, 26, (2), pp:174-1777.
[26]刘宏伟,马建华,保铮. BOX-COX变换提高雷达高分辨距离像识别性能的物理机理分析,第九届全国雷达年会,2004. 8,烟台. pp: 354-357.
[27] Yang Li, Tao Zeng, Teng Long, Zheng Wang. Range Migration Compensation and Doppler Ambiguity Resolution by Keystone Transform. Proceedings of 2006 CIE International Conference on Radar, pp: 1466-1469.
[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, Vol. 54(6), pp: 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), pp: 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, vol. 39, no. 4, pp: 1201–1217.
[8] A. R. Webb, Gamma mixture models for target recognition, Pattern Recog., 2000, vol. 33, pp: 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. E. Tipping, C. M. Bishop. Probabilistic Principle Component Analysis. Journal of the Royal Statistical Society, 1999, B, (61), Part 3, pp: 611-622.
[12] M. E. Tipping, C. M. Bishop. Mixtures of Probabilistic Principal Component Analysers. Technical Report NCRG/97/003, Neural Computing Research Group, Aston University, June1997.
[13] Q.Y. Hou, H.W. Liu, F. Chen and Z. Bao. Adaptive Statistical Model for Radar HRRP Recognition. IET Radar 2009, Accepted
[14]侯庆禹,陈凤,刘宏伟,保铮.噪声背景下的雷达高分辨距离像识别,已投电子与信息学报.
[15]张贤达.矩阵分析与应用.北京:清华大学出版社, 2006,pp.466-468.
[16]侯庆禹,陈凤,刘宏伟,保铮.一种稳健的雷达高分辨距离像目标识别算法.已投系统工程与电子技术.
[17]郭科,陈聆,魏友华.最优化方法及其应用.高等教育出版社, 2007.
[18] Duda R. O., Hart P. E., Stork D. G.. Pattern Classification. Second Edition. New York: John Wiley and Sons. 2001.
[19] S. P. Jacobs, J.O. O’sollivan. Automatic target recognition using sequences of high resolution radar range profiles. IEEE Trans on AES.2000, 36(3), pp: 364-380.
[20]盛骤,谢式千,潘承毅.概率论与数理统计.北京:高等教育出版社, 1989.
[21] Levent Sendur, Ivan W. Selesnick. Bivariate Shrinkage With Local Variance Estimation. IEEE. Signal Processing Letters, 2002, 9(12), pp: 438-441.
[22] K.Rank, M.Lendl, R.Unbehauen. Estimation of image noise variance. IEEProc-Vis. Image Signal Process, 1999, 146(2), pp: 80-84.
[23] Lili He,Ian R. Greenshields. A Nonlocal Maximum Likelihood Estimation Method for Rician Noise Reduction in MR Images. IEEE. Trans. on Medical Imaging, 2009, 28(2), pp: 165-172.
[24] S. Grace Chang, Bin Yu, Martin Vetterli. Spatially Adaptive Wavelet Thresholding with Context Modeling for Image Denoising. IEEE Trans. on Image Processing, 2000, 9(9), pp: 1522-1531.
[25] D.L. Donoho and I.M. Johnstone. Ideal spatial adaption by wavelet shrinkage. Biometrika, 1996, vol 8, pp: 425–455.
[26] V. Zlokolica, A. Pizurica, E. Vansteenkiste, W. Philips. SPATIO-TEMPORAL APPROACH FOR NOISE ESTIMATION. ICASSP2006, pp: 145-148.
[27] Iain M. Johnstone, Bernard W. Silverman. Wavelet threshold estimators for data with correlated noise. J. Roy. Statist. Soc. Ser. B, 1997.
[28] Mood. Alexander McFarlane, Graybill. Franklin A., Boes. Duane C., Introduction to the theory of statistics. McGraw-Hill, 1974.
[29]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.
[30] K.Fukunaga. Introduction to Statistic Pattern Recognition (2nd). Boston, Academic, 1990.
[31]刘宏伟,马建华,保铮. BOX-COX变换提高雷达高分辨距离像识别性能的物理机理分析,第九届全国雷达年会,2004.8,烟台, pp:354-357.
[32]《实用积分表》编委会.实用积分表.中国科学技术大学出版社, 2006.
[1] Jocobs S.P.. Automatic target recognition using high-resolution radar rang profiles. Ph.D. Dissertation. Washington University, 1999.
[2] Feng Chen, Lan Du, Hongwei Liu, Zheng Bao, et al. An amplitude-scale and time-shift jointly optimization for Radar HRRP recognition. Proceeding of 2008 International Conference on Radar, Adelaide, Australia, September, 2008, pp: 515–518.
[3] Lan Du, Hongwei Liu, Zheng Bao, et al. A Two-Distribution Compounded Statistical Model for Radar HRRP target Recognition. IEEE Transactions on Signal Processing, 2006, 54(6), pp: 2226–2238.
[4] Du L, Hongwei Liu, Zheng Bao. Radar HRRP statistical recognition parametric model and model selection. IEEE Transactions on Signal Processing, 2008, 56(5), pp: 1931–1944.
[5]杜兰.雷达高分辨距离像目标识别方法研究.博士研究生论文,西安电子科技大学,2007.
[6] Ueda N, Nakano R, Ghahramani Z, and G. E. Hinton. SMEM algorithm for mixture models. Neural Computation, 2000, 12(9), pp: 2109–2128.
[7] Ghahramani Z. and Beal M.: Variational inference for Bayesian mixture of factor analysers. Advances in NIPS, 2000, 12, pp: 449-455.
[8] Attias H. Inferring parameters and structure of latent variable models by variational Bayes. Proceeding of 15th Conference on Uncertainty in Artificial Intelligence, 1999.
[9] D. J. C. MacKay. Probable networks and plausible predictions—a review of practical Bayesian methods for supervised neural networks. Network: Computation in Neural Systems, 1995, 6, pp: 469–505.
[10] G. Schwarz. Estimating the dimension of a model. The Annals of Statistics, 1978, 6, pp: 461–464.
[11] P. Cheeseman and J. Stutz. Bayesian classification: Theory and results. Advances in Knowledge Discovery and Data Mining, 1996, pp: 153–180.
[12] R. M. Neal. Connectionist learning of belief networks. Artificial Intelligence, 1992, 56, pp: 71–113.
[13] Stephen P. Brooks. Markov chain Monte Carlo method and its application. TheStatistician, 1998, 47, Part 1, pp: 69-100.
[14] C. Peterson and J. Anderson. A mean field theory learning algorithm for neural networks. Complex Systems, 1987, 1, pp: 995–1019.
[15] G.E. Hinton and D. van Camp. Keeping neural networks simple by minimizing the description length of the weights. In Proceedings of the Sixth Annual Conference on Computational Learning Theory, 1993, pp: 5–13.
[16] C.M. Bishop, N. Lawrence, T.S. Jaakkola, and M.I. Jordan. Approximating posterior distributions in belief networks using mixtures. Advances in Neural Information Processing Systems 10, 1998.
[17] Z. Ghahramani and M.J. Beal. Propagation algorithms for Variational Bayesian learning. In T. Leen et.al, editor, NIPS 13, Cambridge, MA, 2001. MIT Press.
[18] W.D. Penny, S.J. Kiebel, and K.J. Friston. Variational Bayesian Inference for fMRI time series. NeuroImage, 2003, 19(3), pp: 727–741.
[19] Ryan Gomes, Max Welling, Pietro Perona. Incremental Learning of Nonparametric Bayesian Mixture Models.. IEEE Conference on Computer Vision and Pattern Recognition, 23-28 June 2008, pp: 1– 8.
[20] Duda R.O., Hart P.E., Stork D. G.. Pattern Classification. Second Edition. New York: John Wiley and Sons, 2001.
[21] F. V. Jensen. Introduction to Bayesian Networks. Springer-Verlag, New York, 1996.
[22] W. D. Penny. Kullback-Liebler Divergences of Normal, Gamma, Dirichlet and Wishart densities. Technical report, Wellcome Department of Cognitive Neurology, 2001.
[23] Mattew J.Beal. Variational Algorithms for Approximate Bayesian Inference. Ph.D. Dissertation. University of Cambrige, UK, 2003.
[24]茆诗松,王静龙.高等数理统计.高等教育出版社, 2006.
[25]侯庆禹,刘宏伟,保铮.基于VB方法的高斯模型在雷达目标识别中的应用.待投
[26] H.Attias. A Variational Bayesian Framework for Graphical Models. In T. Leen et al, editor, NIPS 12, Cambridge, MA, MIT Press, 2000.
[27] W. D. Penny. Variational Bayes for d-dimensional Gaussian mixture models. Technical report, Wellcome Department of Cognitive Neurology, University College London, 2001.
[28] Qingyu Hou, Feng Chen, Hongwei Liu, Zheng Bao. A New Statistical Model forRadar HRRP Target Recognition. Systems Engineering and Electronics, Accepted.
[29] Z. Ghahramani and M. J. Beal. Variational inference for Bayesian mixtures of factor analysers. In Advances in Neural Information Processing Systems 12, Cambridge, MA, 2000. MIT Press.
[30] Ghahramani Z and Hinton G.E. The EM Algorithm for Mixtures of Factor Analyzers. Technical Report CRG-TR-96-1, Dept. of Computer Science, University of Toronto, 1996.
[1] Ghadaki H, Dizaji R. Target track classification for airport surveillance radar (ASR). Radar 2006. New York: IEEE, 2006, pp: 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), pp:21-24.
[3] Leung H, Wu J. Bayesian and Dempster–Shafer Target Identification for Radar Surveillance. IEEE Trans. on AES, 2000, 36 (2), pp: 432-447.
[4] Bell M R, Grubbs R A. JEM modeling and measurement for radar target identification. IEEE Trans on AES, 1993, 29(1), pp: 73-87.
[5] Piazza E. Radar Signals Analysis and modellization presence of JEM application in the civilian ATC Radars. IEEE AES Systems Magazine, 1999, 14(1), pp: 35-40.
[6]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), pp: 460-465.
[7]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), pp:418-421.
[8] Chen Feng, Liu Hongwei, Bao Zheng. Target classification with low-resolution radar based on dispersion situations of eigenvalue spectra. Science in China Series F-Information Sciences. Accepted
[9]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
[10] Tait. P.D.F.. Development in target recognition technology for tracker radars. XIth Air Defence conference. Jun, 2004.
[11] J. Li,H. Ling. Application of adaptive chirplet representation for ISAR feature extraction from targets with rotating parts. IEE proc.-Radar Sonar Naving, 2003, 150(4), pp: 284-291.
[12] Liu Jing, Zhang Junying, Zhao Feng. A feature for Distinguishing Properller-Driven Airplanes from Turbing-Driven Airplanes. IEEE Trans. on AES, Accepted.
[13]刘敬.雷达一维距离像特征提取和识别方法研究.博士研究生学位论文.西安电子科技大学, 2009.
[14]保铮,邢孟道,王彤.雷达成像技术.北京,电子工业出版社,2006.
[15]王伟,邱兆坤,姜卫东,陈曾平.一种基于低分辨雷达的目标识别方法.系统工程与电子技术, 2003, 25(3), pp: 379-382.
[16]张汉华,王伟,姜卫东,陈曾平.低分辨雷达基于波形特征的飞机架次判别方法.国防科技大学学报, 2003, 25(4), pp:38-41.
[17] Duda R. O., Hart P. E., Stork D. G.. Pattern Classification. Second Edition. New York: John Wiley and Sons. 2001.
[18]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.
[2]向敬成,张明友.雷达系统.北京:电子工业出版社, 2001.
[3]丁鹭飞,耿富录.雷达原理.西安:西安电子科技大学出版社, 2002.
[4] Skolnik M. I.主编,王军等译.雷达手册(第二版).北京:电子工业出版社, 2003.
[5] Jacobs S. P.. Automatic target recognition using high-resolution radar range profiles. Ph. D. Dissertation. Washington University. 1999.
[6]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.
[7]周代英.雷达目标一维距离像识别研究.博士研究生学位论文.电子科技大学. 2001.
[8]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.
[9]付耀文.雷达目标识别技术研究.博士研究生学位论文.国防科技大学. 2003.
[10]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.
[11]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.
[12]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.
[13]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.
[14]王彩云.雷达高分辨距离像目标检测与识别研究.博士研究生学位论文.北京航空航天大学. 2008.
[15]陈凤.基于HRRP和JEM信号的雷达目标识别技术研究.博士研究生学位论文.西安电子科技大学. 2009.
[16]柯有安.雷达目标识别(上),国外电子技术, 1978, No.4, pp: 22-30.
[17]柯有安.雷达目标识别(下),国外电子技术, 1978, No.5, pp: 14-20.
[18]保铮.雷达目标识别与分类技术.国防科技项目技术报告(高分辨部分).西安电子科技大学雷达信号处理重点实验室, 1997.
[19]杜兰,保铮,邢孟道.直升机雷达回波的分析与检测.西安电子科技大学学报, 2003, Vol.30 (5), pp: 574-579.
[20]杜兰,刘宏伟,保铮.一种基于距离-多谱勒二维联合的群目标分辨方法.电子学报. 2004, Vol.32 (6), pp: 881-885.
[21]保铮,邢孟道,王彤.雷达成像技术.北京:电子工业出版社,2005.
[22] 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), pp: 444-452.
[23] Li H.-J., Yang S.-H.. Using range profiles as feature vectors to identify aerospace objects. IEEE Trans. A.P., 1993, 41(3), pp: 261-268.
[24] W.G.Carrara, R.S.Goodman, R.M.Majewski. Spotlight synthetic aperture radar: signal processing algorithms. Boston: Artech House, 1995.
[25]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.
[26]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.
[27]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), pp: 1328-1334.
[28] Moving and stationary target acquisition and recognition, Program technology review. Denver, CO. Nov. 1996. http:// www.mbvlab.wpafb.af.mil/public/MBVDATA.
[29] Marcoz Y., Miguel T.T., Feature Selection using Genetic Algorithms in SAR Airborne Imagery. http://www.cim.mcgill.ca,2001.
[30] Punkle P. et al.. Multi-aspect target detection for SAR imagery using Hidden Markov models. IEEE Trans. A.E.S.. 2001, Vol.39(1), pp: 46-55.
[31] 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, Vol. 3721, pp: 355-360.
[32] Musman S., Kerr D., Bachmann C.. Automatic Recognition of ISAR Images. IEEE Trans. A.E.S.. 1996, Vol.32(4), pp: 1392-1403.
[33] Rosenbach K., Schiller J., Construction and Test of a Classifier for Non-Cooperative Air-Target Identification based on 2-D-ISAR Images. Proceedings of IRS'98 International Radar Symposium. Sep. 1998, pp: 1023-1033.
[34] Maki A. et al.. ISAR Image Analysis by Subspace Method: Automatic Extraction and Identification of Ship Profile. IEEE Radar 2001, pp: 523-528.
[35] Maki A. et al.. Automatic Ship Identification in ISAR Imagery: An On-lineSystem using CMSM. IEEE Radar 2002, pp: 206-211.
[36] Mitchell R. A., Dewall R.. Overview of high range resolution radar target identification. In Proceedings of Automatic Target Recognition Working Group. 1994.
[37] Eesterkamp J.. A Framework for 1D HRR ATR evaluation. AFRL Technical Report. Aug.1998.
[38] Anthony Zywechm and Robert E. Bogner. Radar target classification of commercial aircraft. IEEE Trans. on Aerospace and Electronic Systems, 1996, Vol. 32(2), pp: 598-606.
[39] Anthony Zywechm and Robert E. Bogner. Coherent averaging of range profiles. IEEE International Radar Conference, 1995, pp: 456-461.
[40] Mitchell R. A., Dewall R.. Overview of high range resolution radar target identification. In Proceedings of Automatic Target Recognition Working Group,1994.
[41] Heiden R, Groen F.C.A The box-cox metric for nearest neighbor classification improvement. Pattern Recognition. 1997, pp: 273-279.
[42]刘宏伟,马建华,保铮. Box-Cox变换提高雷达高分辨距离像识别性能的物理机理分析.第九届全国雷达年会,烟台, 2004,8, pp: 354-357.
[43] Webb A. R.. Gamma Mixture Models for Target Recognition. Pattern Recognition. Vol. 33, 2000, pp: 2045-2054.
[44] S. P. Jacobs, J.A. O’sollivan, Automatic target recognition using sequences of high resolution radar range-profiles. IEEE Trans. AES,2000, Vol.36, No. 2, pp: 364-380.
[45] Yuan Li, Liu H W, Bao Z. Automatic target recognition using multiple radar high range resolution profiles. CIE International Conference on Radar, Shanghai, China, 2006, pp: 846-894.
[46] R. Williams, J. Westerkamp, et al, Automatic target recognition of time critical moving targets using 1D high range resolution (HRR) radar, IEEE AES Magazine, 2000, April, pp: 37-43.
[47] 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, Vol. 38(4), pp: 1230-1242.
[48] P. Bharadwaj, P. Runkle, L. Carin, et al, Multiaspect classification of airborne targets via physics-based HMMs and matching pursuits, IEEE Trans. AES, 2001, Vol.37(2), pp: 595-606.
[49]王雪松.宽带极化信息处理研究.博士研究生学位论文.国防科学技术大学. 1999.
[50] Du Lan, Liu Hongwei, Bao Zheng, Zhang Junying. A two-distribution compounded statistical model for radar HRRP target recognition. IEEE Trans. on Signal Processing. 2006, 54 (6), pp: 2226-2238.
[51] M. Tipping and C.M. Bishop. Probabilistic Principal Component Analysis. J. Royal Statistical Soc. B, 1999,21(3), pp: 611-622.
[52] Du Lan, Liu Hongwei, Bao Zheng. Radar HRRP statistical recognition: parametric model and model selection . IEEE Trans. on Signal Processing, 2008, 56(5), pp: 1931–1944.
[53] Rubin D., Thayer D.. EM algorithms for ML factor analysis. Psychometrika, 1982,47(1), pp: 69-76.
[54] Xing M.-D., Bao Z., Pei B.. The properties of high-resolution range profiles. Optical Engineering. 2002, 41(2), pp: 493-504.
[55] Rong Hu and Zhaoda Zhu, Research on Radar Classification based on High Resolution Range Profiles. Proceedings of Aerospace and Electronics Conference, 1997, 2, pp: 951-955.
[56] X. D. Zhang, Y. Shi, Z. Bao. A new feature vector using selected bispectra for signal classification. IEEE Trans on SP, 2001, 49(9), pp: 1875-1885.
[57] 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), pp: 2359-2368.
[58] Hongwei Liu, Zheng Bao, Radar HRR profiles recognition based on SVM with power-transformed-correlation kernel, LNCS 3174 (I) , 2004, pp:531-536.
[59] 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, 2005, pp: 528-533.
[60] Bo Chen, Hongwei Liu, Zheng Bao. Kernel Subclass Discriminant Analysis. Neurocomputing, 2007, 71, pp: 455-458.
[61] Bo Chen, Hongwei Liu, Zheng Bao. A Kernel Optimization Method Based on the Localized Kernel Fisher. Pattern Recognition, 2008, 41(3), pp: 1098-1109.
[62]刘敬,张军英,杜兰.基于最大相关系数的雷达高分辨距离像分帧方法.电子与信息学报, 2008,30(9), pp: 2060-2064.
[63]侯庆禹,刘宏伟,保铮.宽带目标识别雷达的杂波抑制.现代雷达, 2007, 29,(9), pp: 44-47.
[64]侯庆禹,刘宏伟,保铮.一种新的宽带目标识别雷达杂波抑制方法.西安电子科技大学学报, 2008,35,(5), pp: 769-773.
[65]侯庆禹,刘宏伟,保铮.基于keystone变换方法的宽带目标识别雷达杂波抑制.系统工程与电子技术, 2009,31,(1),pp.49-53.
[66] Q.Y. Hou, H.W. Liu, F. Chen and Z. Bao. Adaptive Statistical Model for Radar HRRP Recognition. IET Radar Conference 2009, Accepted.
[67] Feng Chen, Hongwei Liu, Qingyu Hou, Zeng Bao. Radar automatic target recognition for alterable noise environment. IET Radar conference 2009. Accepted.
[68] Cohen M. N.. Variability of ultra-high range resolution profiles and some implications for target recognition. SPIE. 1992. pp: 256-266.
[69] Li H J, Farhat N H, Shen Y, et al. Image understanding and prediction in microwave diversity imaging. IEEE Trans. on AP, 1989, 37(8), pp: 1048-1057.
[70] Li H J, Liu T Y, Yang S H. superhigh imaging resolution for microwave imaging. Int. J. Imaging Systems and Technol. 1990, vol(2), pp: 37-46.
[71] Zyweck A., Bogner R. E.. Radar target classification of commercial aircraft. IEEE Trans. A.E.S.. 1996, Vol.32(2), pp: 598-606.
[72] Scott Hudson, Demetri Psaltis. Correlation Filters for Aircraft Identification From Radar Range Profiles. IEEE Trans. Aerospace and Electronics System, 1993, Vol. 29,No.3,pp: 741-748.
[73]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, 2008, 30(1), pp: 198-202.
[74] C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. The MIT Press, Cambridge, MA, 2006.
[75] L. Csató. Gaussian Processes - Iterative Sparse Approximations. PhD thesis. Neural Computing Research Group, Aston University. 2002.
[76] Hyun-Chul Kim, Zoubin Ghahramani. Bayesian Gaussian Process Classification with the EM-EP Algorithm .IEEE Trans. on PAMI, 2006, 28(12), pp: 1948-1959.
[77] Chen Bo, Liu Hongwei, Yuan Li, Bao Zheng. Adaptively Segmenting Angular Sectors for Radar HRRP ATR. EURASIP Journal on Advances in Signal Processing Volume 2008 (2008), Article ID 641709, 6 pages.
[78]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, Vol. 26 (8), pp: 1040-1043.
[79]杜兰,保铮,刘宏伟,高分辨距离像雷达自动目标识别的模板匹配问题,第九届全国雷达学术年会论文集, 2004, 8, pp: 509-512.
[80] 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, Vol.150(6), pp: 411-418.
[81]陈渤,刘宏伟,保铮.基于三种不同绝对对齐方法的分类器的分析与研究.现代雷达,2006, Vol. 28 (3), pp: 58-62.
[82] 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, Vol.50(3), pp: 325-337.
[83]袁莉,刘宏伟,保铮.基于中心矩特征的雷达HRRP自动目标识别[J].电子学报.2004,32(12), pp: 2078-2081.
[84] Duda R. O., Hart P. E., Stork D. G.. Pattern Classification. Second Edition. New York: John Wiley and Sons. 2001.
[85] Bo Chen, Hongwei Liu, Zheng Bao. An Efficient Kernel Optimization Method for Radar High-resolution Range Profile Recognition, EURASIP Journal on Applied Signal Processing. Vol. 2007, Article ID 49597, 10 pages, 2007.
[86]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), pp: 3459-3463.
[87] Li J., Stoica P.. Efficient mixed-spectrum estimation with application to feature extraction. IEEE Trans. on Signal Processing. Feb. 1996, Vol.42(2), pp: 281-295.
[88] Bharadwaj P., Runkle P., Carin L.. Multiaspct classification of airborne targets via physics-based HMMs and matching pursuits. IEEE Trans. on A.E.S.. 2001, Vol.37(2), pp: 595-606.
[89]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, Vol.33 (3), pp: 411-415.
[90]杜兰,刘宏伟,保铮,张军英.一种用于雷达HRRP功率谱的加权特征压缩方法.西安电子科技大学学报. 2006, Vol. 33(2), pp: 173-177.
[91] Bo Chen, Hongwei Liu, Zheng Bao. A Kernel Optimization Method Based on the Localized Kernel Fisher, Pattern Recognition, 2008, Vol. 41(3), pp: 1098-1109.
[92] M. Tipping and C.M. Bishop. Probabilistic Principal Component Analysis. J. Royal Statistical Soc. B, 1999,21(3), pp: 611-622
[93] Yu X. Neural network directed bayes decision rule for moving target class. IEEE Trans on AES, 2000, 36(1), pp: 176-188.
[94] C. Cortes and V. Vapnik, Support-vector networks. Machine Learning, 1995, Vol. 20, pp: 273-297.
[95] Tipping M E. Sparse Bayesian learning and relevance vector machine. Journal of Machine Learning Research, 2001,(6), pp:211-244.
[96] Ghahramani Z. and Beal M.: Variational inference for Bayesian mixture of factor analysers. Advances in NIPS, 2000, 12, pp: 449-455.
[97] Attias H. Inferring parameters and structure of latent variable models by variational Bayes. Proceeding of 15th Conference on Uncertainty in Artificial Intelligence, 1999.
[98] Mattew J.Beal. Variational Algorithms for Approximate Bayesian Inference. Ph.D. Dissertation. University of Cambrige, UK, 2003.
[99]陈文驰,保铮,刑孟道.基于Keystone变换的低信噪比ISAR成像.西安电子科技大学学报(自然科学版), 2003, 30, (2), pp: 155-159.
[100]章毓晋.图像处理和分析.清华大学出版社, 1999.
[101]郭科,陈聆,魏友华.最优化方法及其应用.高等教育出版社, 2007.
[102] Mood A M, Graybill F A, Boes D C. Introduction to the Theory of Statistics (3rd Ed.). McGraw-Hill, 1974.
[1]贺知明,黄巍,向敬成.宽带雷达中消除“盲速”的动显方法研究.电子科技大学学报,2003, 32, (6), pp: 593-597.
[2]贺知明,向敬成,黄巍. NMTI方法在宽带雷达系统中的应用.电子与信息学报,2003, 25, (12), pp: 1628-1633.
[3]贺知明,黄巍,张一冰,向敬成.适用于宽带雷达的非相干杂波抑制方法.系统工程与电子技术, 2004, 26, (5), pp: 572-574.
[4]侯庆禹,刘宏伟,保铮.宽带目标识别雷达的杂波抑制.现代雷达, 2007, 29, (9), pp:44-47.
[5] Perrv R P. Dipietro R C. Fante R L. SAR Imaging of Moving Targets. IEEE Trans on AES, 1999, 35, (1), pp:188-199.
[6]陈文驰,保铮,刑孟道.基于Keystone变换的低信噪比ISAR成像.西安电子科技大学学报(自然科学版), 2003, 30, (2), pp:155-159.
[7] Mengdao Xing, Renbiao Wu, Jinqiao Lan, Zheng Bao. Migration Through Resolution Cell Compensation in ISAR Imaging. IEEE GRS Letter, 2004, 1, (2), pp:141-144.
[8]陈文驰,刑孟道.基于Keystone变换的多目标ISAR成像算法.现代雷达, 2005, 27, (3), pp:40-42.
[9]侯庆禹,刘宏伟,保铮.基于keystone变换方法的宽带目标识别雷达杂波抑制.系统工程与电子技术, 2009, 31, (1), pp:49-53.
[10] Duda R.O,Hart P.E. Use of Hough transform to detect lines and curves in pictures.comm.of ACM, 1990, 15, (1), pp:11-15.
[11] Xu.L, Oja.E, Kultanen P. A new curve Detection Method: Randomized HoughTransform (RHT).Pattern Recognition Letters, 1990, 11, (5), pp: 331-338.
[12]章毓晋.图像处理和分析.清华大学出版社, 1999, pp:187-188.
[13]华顺刚,逄岭,欧宗瑛.一种改进的Hough变换算法及图像特征点提取.中国图像图形学报, 2003, 8, (A), pp:153-156.
[14]徐牧,王雪松,肖顺平.基于Hough变换与目标主轴提取的SAR图像目标方位角估计方法.电子与信息学报, 2007, 29, (2),pp:370-374.
[15] Zhou Jianxiong, Zhao Hongzhong, etc. Extracting Global 3D Scattering Center Model of Radar Target from Multiple HRR profiles. Radar conference, 2007, pp:811-816.
[16]侯庆禹,刘宏伟,保铮.一种新的宽带目标识别雷达杂波抑制方法.西安电子科技大学学报, 2008, 35, (5), pp:769-773.
[17] H-J. Li, S.-H. Yang. Using range profiles as features vectors to identify aerospace objects. IEEE Trans. A.P., 1993, 41, (3), pp: 261-268.
[18]保铮,刑孟道,王彤.雷达成像技术.北京:电子工业出版社, 2005, pp:35-36.
[19]杜兰.飞机目标的雷达回波特性研究.西安电子科技大学硕士学位论文, 2004.
[20] V.Anastassopoulos, G.A.Lampoulos, A.Drosoulos, M.Rey. High Resolution Radar Clutter Statistics .IEEE. Trans on AES, 1999, 35, (1), pp: 43-60.
[21] David A. Shnidman. Generalized Radar Clutter Model. IEEE. Trans on AES, 1999, 35, (3), pp: 857-865.
[22]蒋咏梅,陆铮.相关非高斯分布杂波的建模与仿真.系统工程与电子技术,1999, 21, (10), pp:27-30.
[23]杨凤凤,周智敏.基于ZMNL法的雷达杂波仿真.现代雷达, 2003, 9, pp:22-24.
[24]胡航,靖涛.一种二阶递归MTI滤波器的参数设计方法.哈尔滨工业大学学报, 2001, 33, (3), pp:375-377.
[25]陈建春,耿富录.自适应运动杂波抑制技术.西安电子科技大学学报, 1999, 26, (2), pp:174-1777.
[26]刘宏伟,马建华,保铮. BOX-COX变换提高雷达高分辨距离像识别性能的物理机理分析,第九届全国雷达年会,2004. 8,烟台. pp: 354-357.
[27] Yang Li, Tao Zeng, Teng Long, Zheng Wang. Range Migration Compensation and Doppler Ambiguity Resolution by Keystone Transform. Proceedings of 2006 CIE International Conference on Radar, pp: 1466-1469.
[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, Vol. 54(6), pp: 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), pp: 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, vol. 39, no. 4, pp: 1201–1217.
[8] A. R. Webb, Gamma mixture models for target recognition, Pattern Recog., 2000, vol. 33, pp: 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. E. Tipping, C. M. Bishop. Probabilistic Principle Component Analysis. Journal of the Royal Statistical Society, 1999, B, (61), Part 3, pp: 611-622.
[12] M. E. Tipping, C. M. Bishop. Mixtures of Probabilistic Principal Component Analysers. Technical Report NCRG/97/003, Neural Computing Research Group, Aston University, June1997.
[13] Q.Y. Hou, H.W. Liu, F. Chen and Z. Bao. Adaptive Statistical Model for Radar HRRP Recognition. IET Radar 2009, Accepted
[14]侯庆禹,陈凤,刘宏伟,保铮.噪声背景下的雷达高分辨距离像识别,已投电子与信息学报.
[15]张贤达.矩阵分析与应用.北京:清华大学出版社, 2006,pp.466-468.
[16]侯庆禹,陈凤,刘宏伟,保铮.一种稳健的雷达高分辨距离像目标识别算法.已投系统工程与电子技术.
[17]郭科,陈聆,魏友华.最优化方法及其应用.高等教育出版社, 2007.
[18] Duda R. O., Hart P. E., Stork D. G.. Pattern Classification. Second Edition. New York: John Wiley and Sons. 2001.
[19] S. P. Jacobs, J.O. O’sollivan. Automatic target recognition using sequences of high resolution radar range profiles. IEEE Trans on AES.2000, 36(3), pp: 364-380.
[20]盛骤,谢式千,潘承毅.概率论与数理统计.北京:高等教育出版社, 1989.
[21] Levent Sendur, Ivan W. Selesnick. Bivariate Shrinkage With Local Variance Estimation. IEEE. Signal Processing Letters, 2002, 9(12), pp: 438-441.
[22] K.Rank, M.Lendl, R.Unbehauen. Estimation of image noise variance. IEEProc-Vis. Image Signal Process, 1999, 146(2), pp: 80-84.
[23] Lili He,Ian R. Greenshields. A Nonlocal Maximum Likelihood Estimation Method for Rician Noise Reduction in MR Images. IEEE. Trans. on Medical Imaging, 2009, 28(2), pp: 165-172.
[24] S. Grace Chang, Bin Yu, Martin Vetterli. Spatially Adaptive Wavelet Thresholding with Context Modeling for Image Denoising. IEEE Trans. on Image Processing, 2000, 9(9), pp: 1522-1531.
[25] D.L. Donoho and I.M. Johnstone. Ideal spatial adaption by wavelet shrinkage. Biometrika, 1996, vol 8, pp: 425–455.
[26] V. Zlokolica, A. Pizurica, E. Vansteenkiste, W. Philips. SPATIO-TEMPORAL APPROACH FOR NOISE ESTIMATION. ICASSP2006, pp: 145-148.
[27] Iain M. Johnstone, Bernard W. Silverman. Wavelet threshold estimators for data with correlated noise. J. Roy. Statist. Soc. Ser. B, 1997.
[28] Mood. Alexander McFarlane, Graybill. Franklin A., Boes. Duane C., Introduction to the theory of statistics. McGraw-Hill, 1974.
[29]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.
[30] K.Fukunaga. Introduction to Statistic Pattern Recognition (2nd). Boston, Academic, 1990.
[31]刘宏伟,马建华,保铮. BOX-COX变换提高雷达高分辨距离像识别性能的物理机理分析,第九届全国雷达年会,2004.8,烟台, pp:354-357.
[32]《实用积分表》编委会.实用积分表.中国科学技术大学出版社, 2006.
[1] Jocobs S.P.. Automatic target recognition using high-resolution radar rang profiles. Ph.D. Dissertation. Washington University, 1999.
[2] Feng Chen, Lan Du, Hongwei Liu, Zheng Bao, et al. An amplitude-scale and time-shift jointly optimization for Radar HRRP recognition. Proceeding of 2008 International Conference on Radar, Adelaide, Australia, September, 2008, pp: 515–518.
[3] Lan Du, Hongwei Liu, Zheng Bao, et al. A Two-Distribution Compounded Statistical Model for Radar HRRP target Recognition. IEEE Transactions on Signal Processing, 2006, 54(6), pp: 2226–2238.
[4] Du L, Hongwei Liu, Zheng Bao. Radar HRRP statistical recognition parametric model and model selection. IEEE Transactions on Signal Processing, 2008, 56(5), pp: 1931–1944.
[5]杜兰.雷达高分辨距离像目标识别方法研究.博士研究生论文,西安电子科技大学,2007.
[6] Ueda N, Nakano R, Ghahramani Z, and G. E. Hinton. SMEM algorithm for mixture models. Neural Computation, 2000, 12(9), pp: 2109–2128.
[7] Ghahramani Z. and Beal M.: Variational inference for Bayesian mixture of factor analysers. Advances in NIPS, 2000, 12, pp: 449-455.
[8] Attias H. Inferring parameters and structure of latent variable models by variational Bayes. Proceeding of 15th Conference on Uncertainty in Artificial Intelligence, 1999.
[9] D. J. C. MacKay. Probable networks and plausible predictions—a review of practical Bayesian methods for supervised neural networks. Network: Computation in Neural Systems, 1995, 6, pp: 469–505.
[10] G. Schwarz. Estimating the dimension of a model. The Annals of Statistics, 1978, 6, pp: 461–464.
[11] P. Cheeseman and J. Stutz. Bayesian classification: Theory and results. Advances in Knowledge Discovery and Data Mining, 1996, pp: 153–180.
[12] R. M. Neal. Connectionist learning of belief networks. Artificial Intelligence, 1992, 56, pp: 71–113.
[13] Stephen P. Brooks. Markov chain Monte Carlo method and its application. TheStatistician, 1998, 47, Part 1, pp: 69-100.
[14] C. Peterson and J. Anderson. A mean field theory learning algorithm for neural networks. Complex Systems, 1987, 1, pp: 995–1019.
[15] G.E. Hinton and D. van Camp. Keeping neural networks simple by minimizing the description length of the weights. In Proceedings of the Sixth Annual Conference on Computational Learning Theory, 1993, pp: 5–13.
[16] C.M. Bishop, N. Lawrence, T.S. Jaakkola, and M.I. Jordan. Approximating posterior distributions in belief networks using mixtures. Advances in Neural Information Processing Systems 10, 1998.
[17] Z. Ghahramani and M.J. Beal. Propagation algorithms for Variational Bayesian learning. In T. Leen et.al, editor, NIPS 13, Cambridge, MA, 2001. MIT Press.
[18] W.D. Penny, S.J. Kiebel, and K.J. Friston. Variational Bayesian Inference for fMRI time series. NeuroImage, 2003, 19(3), pp: 727–741.
[19] Ryan Gomes, Max Welling, Pietro Perona. Incremental Learning of Nonparametric Bayesian Mixture Models.. IEEE Conference on Computer Vision and Pattern Recognition, 23-28 June 2008, pp: 1– 8.
[20] Duda R.O., Hart P.E., Stork D. G.. Pattern Classification. Second Edition. New York: John Wiley and Sons, 2001.
[21] F. V. Jensen. Introduction to Bayesian Networks. Springer-Verlag, New York, 1996.
[22] W. D. Penny. Kullback-Liebler Divergences of Normal, Gamma, Dirichlet and Wishart densities. Technical report, Wellcome Department of Cognitive Neurology, 2001.
[23] Mattew J.Beal. Variational Algorithms for Approximate Bayesian Inference. Ph.D. Dissertation. University of Cambrige, UK, 2003.
[24]茆诗松,王静龙.高等数理统计.高等教育出版社, 2006.
[25]侯庆禹,刘宏伟,保铮.基于VB方法的高斯模型在雷达目标识别中的应用.待投
[26] H.Attias. A Variational Bayesian Framework for Graphical Models. In T. Leen et al, editor, NIPS 12, Cambridge, MA, MIT Press, 2000.
[27] W. D. Penny. Variational Bayes for d-dimensional Gaussian mixture models. Technical report, Wellcome Department of Cognitive Neurology, University College London, 2001.
[28] Qingyu Hou, Feng Chen, Hongwei Liu, Zheng Bao. A New Statistical Model forRadar HRRP Target Recognition. Systems Engineering and Electronics, Accepted.
[29] Z. Ghahramani and M. J. Beal. Variational inference for Bayesian mixtures of factor analysers. In Advances in Neural Information Processing Systems 12, Cambridge, MA, 2000. MIT Press.
[30] Ghahramani Z and Hinton G.E. The EM Algorithm for Mixtures of Factor Analyzers. Technical Report CRG-TR-96-1, Dept. of Computer Science, University of Toronto, 1996.
[1] Ghadaki H, Dizaji R. Target track classification for airport surveillance radar (ASR). Radar 2006. New York: IEEE, 2006, pp: 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), pp:21-24.
[3] Leung H, Wu J. Bayesian and Dempster–Shafer Target Identification for Radar Surveillance. IEEE Trans. on AES, 2000, 36 (2), pp: 432-447.
[4] Bell M R, Grubbs R A. JEM modeling and measurement for radar target identification. IEEE Trans on AES, 1993, 29(1), pp: 73-87.
[5] Piazza E. Radar Signals Analysis and modellization presence of JEM application in the civilian ATC Radars. IEEE AES Systems Magazine, 1999, 14(1), pp: 35-40.
[6]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), pp: 460-465.
[7]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), pp:418-421.
[8] Chen Feng, Liu Hongwei, Bao Zheng. Target classification with low-resolution radar based on dispersion situations of eigenvalue spectra. Science in China Series F-Information Sciences. Accepted
[9]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
[10] Tait. P.D.F.. Development in target recognition technology for tracker radars. XIth Air Defence conference. Jun, 2004.
[11] J. Li,H. Ling. Application of adaptive chirplet representation for ISAR feature extraction from targets with rotating parts. IEE proc.-Radar Sonar Naving, 2003, 150(4), pp: 284-291.
[12] Liu Jing, Zhang Junying, Zhao Feng. A feature for Distinguishing Properller-Driven Airplanes from Turbing-Driven Airplanes. IEEE Trans. on AES, Accepted.
[13]刘敬.雷达一维距离像特征提取和识别方法研究.博士研究生学位论文.西安电子科技大学, 2009.
[14]保铮,邢孟道,王彤.雷达成像技术.北京,电子工业出版社,2006.
[15]王伟,邱兆坤,姜卫东,陈曾平.一种基于低分辨雷达的目标识别方法.系统工程与电子技术, 2003, 25(3), pp: 379-382.
[16]张汉华,王伟,姜卫东,陈曾平.低分辨雷达基于波形特征的飞机架次判别方法.国防科技大学学报, 2003, 25(4), pp:38-41.
[17] Duda R. O., Hart P. E., Stork D. G.. Pattern Classification. Second Edition. New York: John Wiley and Sons. 2001.
[18]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.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |