基于高分辨距离像的雷达自动目标识别研究
|
摘要
由宽带雷达获取的高分辨距离像（HRRP）反映了目标散射中心沿雷达视线的分布情况，包含了更多有关目标的结构和形状信息，相比雷达二维像和三维像而言易于获取，并且可实现实时识别。因此，基于HRRP的目标识别方法是当前识别飞机等高速运动目标的有效手段。
本论文围绕国家十一五总装预研项目，针对空中飞机目标，对高分辨距离像雷达目标识别系统中的特征提取和选择、分类器设计及非库属目标拒识等相关问题展开研究。论文主要工作如下：
1.为了充分利用HRRP中包含的目标结构和形状信息，提出了自适应差分算法，有效地提取了目标在雷达视线上的投影长度特征，实现了对目标的预分类。
2.针对经典子空间方法中多目标分类边界模糊的问题，研究了加权的一对一分类（WOAO）策略，通过为每个分类器设置分类效果权值，解决了常规一对一（OAO）分类方法中多个目标因得票相同而出现的分类模糊问题。
3.从信息融合的角度出发，提出融合长度特征和子空间特征的多级组合分类器识别方案，利用长度特征进行预分类，缩小WOAO分类算法的搜索范围，提高识别率。
4.在利用经典Relief算法对距离像进行特征选择时，若目标在某维特征上分布不均匀，则会出现对该维特征的分类有效性评估不准确的问题，为此提出了改进的MRe特征选择算法，通过加大每对成功分类的样本对在权值累积时所占的比重，解决了目标分布不均匀对特征权值的影响，使得选择出的特征更有利于目标分类。
5.针对目标识别系统中非库属目标的拒识问题，研究了一种改进的广义置信度函数，通过为每类目标设置拒识门限，实现对非库属目标的可靠判别。
6.以仿生模式识别理论为基础，提出一种链条覆盖模型拒识算法。该算法为目标样本在特征空间的分布构建紧凑的封闭几何边界，以此边界来代表目标样本分布形状，通过判断测试样本是否位于目标覆盖边界内来实现对样本的拒识。
上述所有算法的性能均在仿真及实测飞机目标数据上得到了验证。
The high resolution range profile (HRRP) obtained from wideband radar reflectsthe distribution of the target scattering centers along the radar line of sight, whichcontains more information about the structure and shape of the target. Compared withtwo-dimensional or three-dimensional radar imagery, HRRP is easier to be captured.Besides, real-time recognition can be realized. Therefore, the target recognition basedon HRRP is currently an effective approach to identifying airplanes and otherhigh-speed moving targets.
This dissertation which is supported by the pre-research projects of GeneralArmament Department of the Eleventh Five-Year Plan, studies feature extraction andselection, classifier design and out-of-database target rejection of HRRP radar automatictarget recognition system, based on the target aircraft recognition. The main content andinnovation is summarized as follows:
1. To make full use of the information of target structure and shape included inHRRP, an adaptive difference operator is proposed to extract the target projection lengthalong the radar line of sight, and the targets are rough classified according to the lengthfeature.
2. In view of the vague borderline in multi-target classification of classicalsubspace method, we studies a weighted one against one (WOAO) classified strategies.By set weight for each classifier, this method solves the vague classification problem ofthe norm one against one (OAO) classifier in the case of that multiple targets get thesame votes.
3. From the perspective of information fusion, an identifying method with multiplecombinations of classifiers which integrate target length and subspace feature isproposed. The length feature is used to rough classify, and the classified results are usedto narrow the search scope of WOAO classifier, so as to improve the recognitionperformance.
4. When classical Relief algorithm is adopted to select features for HRRP, iftargets’ distribution is uneven on one feature, the evaluated result of this feature will be inaccurate. Thus, a modify Relief (MRe) feature selection algorithm is proposed, whichcan solve the influence of the targets’ uneven distribution to the feature weight byincreasing the proportion of each pair of successfully classified samples to weightedaccumulation. In this case, the selected features will be more beneficial for targetclassification
5. In order to solve the problem of the out-of-database target rejection in targetrecognition system, we study an improved generalized confidence function to realizereliable identification of out-of-database targets by setting the refuse threshold for everytarget.
6. On the basis of biomimetic pattern recognition theory, a rejection andrecognition algorithm based on the chain coverage model is proposed. By means of thisalgorithm, compact and closed geometric boundary is constructed in the feature space oftarget sample. This boundary represents the distribution shape of the target sample. Theacceptance or rejection of the samples is accomplished by judging whether or notsamples are within the target coverage area.
The performance of all above-mentioned algorithms are verified by experimentsbased on real measured data and simulated data.
本论文围绕国家十一五总装预研项目，针对空中飞机目标，对高分辨距离像雷达目标识别系统中的特征提取和选择、分类器设计及非库属目标拒识等相关问题展开研究。论文主要工作如下：
1.为了充分利用HRRP中包含的目标结构和形状信息，提出了自适应差分算法，有效地提取了目标在雷达视线上的投影长度特征，实现了对目标的预分类。
2.针对经典子空间方法中多目标分类边界模糊的问题，研究了加权的一对一分类（WOAO）策略，通过为每个分类器设置分类效果权值，解决了常规一对一（OAO）分类方法中多个目标因得票相同而出现的分类模糊问题。
3.从信息融合的角度出发，提出融合长度特征和子空间特征的多级组合分类器识别方案，利用长度特征进行预分类，缩小WOAO分类算法的搜索范围，提高识别率。
4.在利用经典Relief算法对距离像进行特征选择时，若目标在某维特征上分布不均匀，则会出现对该维特征的分类有效性评估不准确的问题，为此提出了改进的MRe特征选择算法，通过加大每对成功分类的样本对在权值累积时所占的比重，解决了目标分布不均匀对特征权值的影响，使得选择出的特征更有利于目标分类。
5.针对目标识别系统中非库属目标的拒识问题，研究了一种改进的广义置信度函数，通过为每类目标设置拒识门限，实现对非库属目标的可靠判别。
6.以仿生模式识别理论为基础，提出一种链条覆盖模型拒识算法。该算法为目标样本在特征空间的分布构建紧凑的封闭几何边界，以此边界来代表目标样本分布形状，通过判断测试样本是否位于目标覆盖边界内来实现对样本的拒识。
上述所有算法的性能均在仿真及实测飞机目标数据上得到了验证。
The high resolution range profile (HRRP) obtained from wideband radar reflectsthe distribution of the target scattering centers along the radar line of sight, whichcontains more information about the structure and shape of the target. Compared withtwo-dimensional or three-dimensional radar imagery, HRRP is easier to be captured.Besides, real-time recognition can be realized. Therefore, the target recognition basedon HRRP is currently an effective approach to identifying airplanes and otherhigh-speed moving targets.
This dissertation which is supported by the pre-research projects of GeneralArmament Department of the Eleventh Five-Year Plan, studies feature extraction andselection, classifier design and out-of-database target rejection of HRRP radar automatictarget recognition system, based on the target aircraft recognition. The main content andinnovation is summarized as follows:
1. To make full use of the information of target structure and shape included inHRRP, an adaptive difference operator is proposed to extract the target projection lengthalong the radar line of sight, and the targets are rough classified according to the lengthfeature.
2. In view of the vague borderline in multi-target classification of classicalsubspace method, we studies a weighted one against one (WOAO) classified strategies.By set weight for each classifier, this method solves the vague classification problem ofthe norm one against one (OAO) classifier in the case of that multiple targets get thesame votes.
3. From the perspective of information fusion, an identifying method with multiplecombinations of classifiers which integrate target length and subspace feature isproposed. The length feature is used to rough classify, and the classified results are usedto narrow the search scope of WOAO classifier, so as to improve the recognitionperformance.
4. When classical Relief algorithm is adopted to select features for HRRP, iftargets’ distribution is uneven on one feature, the evaluated result of this feature will be inaccurate. Thus, a modify Relief (MRe) feature selection algorithm is proposed, whichcan solve the influence of the targets’ uneven distribution to the feature weight byincreasing the proportion of each pair of successfully classified samples to weightedaccumulation. In this case, the selected features will be more beneficial for targetclassification
5. In order to solve the problem of the out-of-database target rejection in targetrecognition system, we study an improved generalized confidence function to realizereliable identification of out-of-database targets by setting the refuse threshold for everytarget.
6. On the basis of biomimetic pattern recognition theory, a rejection andrecognition algorithm based on the chain coverage model is proposed. By means of thisalgorithm, compact and closed geometric boundary is constructed in the feature space oftarget sample. This boundary represents the distribution shape of the target sample. Theacceptance or rejection of the samples is accomplished by judging whether or notsamples are within the target coverage area.
The performance of all above-mentioned algorithms are verified by experimentsbased on real measured data and simulated data.
引文
[1] C. R. Smith, P. M. Goggana. Radar target recognition. IEEE Antennas and PropagationMagazine,1993,35(2):27-38
[2] D. R. Wehner. High Resolution Radar. Norwood, MA, Artech House, Inc.,1987,1-50
[3]刘宏伟,杜兰,袁莉等.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
[4]余贵水,卢建斌,魏钟记等.基于一维距离像的雷达目标识别研究进展.现代雷达,2010,32(6):5-10
[5]袁莉.基于高分辨距离像的雷达目标识别方法研究:[博士学位论文].西安:西安电子科技大学,2007,1-106
[6] B. Bhanu. Automatic target recognition: State of the art survey. IEEE Trans. on AES,1986,22(4):364-379
[7] M. N. Cohen. A survey of radar-based target recognition techniques. Proceeding of SPIE,1991,1470:233-242
[8] R. D. Strattan. Target identification from radar signatures. IEEE Conf. ASSP,1978:233-226
[9] V. G. Nebabin.. Methods and techniques of radar recognition [M]. Boston: Artech House.1997:1-50
[10]孙文峰.雷达目标识别技术述评.雷达与对抗,2001,3:1-8
[11]刘华林.高分辨距离像雷达自动目标识别研究:[博士学位论文].成都:电子科技大学,2008,1-100
[12]孟继成.雷达目标距离像识别研究:[博士学位论文].成都:电子科技大学,2005,1-106
[13] A.R. Webb. Gamma Mixture Models for Target Recognition. Pattern Recognition,2000,33:2045-2054
[14]边肇祺,张学工.模式识别(第二版).北京:清华大学出版社,2000,9-303
[15] N. Vapnik. The nature of statistical learning theory. New York: Springer Verlag,1995,1-188
[16] C. Cortes, V. Vapnik. Support-vector network. Machine Learning,1995,20(3):273-297
[17] C. J. C. Burges. A tutorial on support vector machines for pattern recognition. Data Miningand Knowledge Discovery,1998,2(2):121-167
[18] L. Yang. An overview of distance metric learning. Technical Report, School of ComputerScience, Carnegie Mellon University,2007
[19] Q. Zhao, Z. Bao. Radar target recognition using a radial basis function neural network. NeuralNetworks,1996,9(4):709–720
[20]杜兰.雷达高分辨距离像目标识别方法研究:[博士学位论文].西安:西安电子科技大学,2007,1-127
[21] D. K. Barton. Sputink п as observe by C band radar. IRE Nat. Conf. Rec,1959,7(5):67-73
[22] G. Linde. Use of Wide-band waveforms for Target Recognition with Surveillance Radar.Proceedings of IEEE International radar conference,2000:128-133
[23] H. Leung. Intelligent Radar Recognition System for Surveillance. Proceedings of IEEEInternational Conference on Systems, Man and Cybernetics,1995:2280-2285
[24] J. G. Teti. A Multi-feature Decision Space Approach to Radar Target Identification. IEEETrans. On AES.1996,32(1):480-487
[25] P. D. William, W. W. William. Radar Development at Lincoln Laboratory: An Overview of theFirst Fifty Years. Lincoln Laboratory Journal.2000,12(2):147-166
[26]周代英.雷达目标一维距离像识别研究:[博士学位论文].成都:电子科技大学,2001,1-92
[27] H. Hynes, R. E. Gardner. Doppler spectra of S band and X band signals. IEEE Trans. OnAES,1967,3(6):356-365
[28] G. G. Fliss, D. L. Mensa. Instrumentation for RCS measurements of modulation spectra ofAircraft blades. IEEE National Radar Conference,1986:12-13
[29] M. R. Bell, R. A. Grubbs. JEM modeling and measurement for radar target identification.IEEE Trans. on AES,1993,29(1):73-87
[30] D. Giuli. Polarization diversity in radars. Proc.IEEE,1986,74(2):245-269
[31] W. L. Cameron. Feature motivated polarization scattering matrix decomposition. IEEE Int.Radar Conf.,1990:549-557
[32]黄培康,殷红成,许小剑.雷达目标特性.北京:电子工业出版社,2005,229-278
[33]王晓丹,王积勤.雷达目标识别技术综述.现代雷达,2003,25(5):22-26
[34] W. Cameron. Feature motivated polarization scattering matrix decomposition. IEEE Int.Radar Conf.,1990:549-557
[35] N. F. Chamberlain, E. K. Walton, Garber F D. Radar target identification of aircraft usingpolarization-diverse features. IEEE Trans. on AES,1991,27(1):58-66
[36]冯德军,王雪松,肖顺平等.全极化高分辨雷达距离像统计识别方法.电子与信息学报,2006,28(3):517-521
[37] T. Tenoux, G. Y. Delisle, P. Fournet, et al. Analysis and interpretation of high resolutionpolarimetric SAR images.4e Colloque International sur le Radar,1994:358-363
[38] C. E. Baum. On the singularity expansion method for solution of electromagnetic interactionproblems. Air Force Weapons Lab. Interaction Notes88, Dec.1971
[39] L. W. Pearson, M. J. Blaricum, Mittra R. A new method for radar target recognition basedupon the singularity expansion method. Rec. IEEE Int. Radar Conf., Arlington, VA,1975:452-457
[40] M. J. Blaricum, R. Mittra. A technique for extracting the poles and residues of a systemdirectly form its transient response. IEEE Trans. on AP,1975,23:777-781
[41] V. K. Jain, T. K. Sarkar, D. D. Weiner. Rational modeling by pencil-of-funtions method. IEEETrans. on ASSP,1983,31(3):564-573
[42] Y. B. Hua, T. K. Sarkar. Generalized pencil-of-function method for extracting poles of an EMsystem from transient response. IEEE Trans. on AP,1989,37(2):229-234
[43] B. Drachman, E. Rothwell. A continuation method for identification of the natural frequenciesof an object using a measured response. IEEE Trans. on AP,1985,33:445-450
[44] A. J. Berni. Target identification by natural resonance estimation. IEEE Trans. on AES,1975,11(2):147-154
[45] E. M. Kennaugh. The K-pulse concept. IEEE Trans. on AP,1981,29(2):327-331
[46] K. M. Chen. Radar waveform synthesis method---a new radar detection scheme. IEEE Trans.on AP,1981,29(4):553-565
[47] K. M. Chen. Westmorelund. Radar waveform synthesis for exciting single-mode backscatterfrom a splure and application for target discrimination. Radio Science,1982,29(4):553-565
[48] E. J. Rothwell, D. P. Nyqist, K. M. Chen. Radar target discrimination using theextinction-pulse technique. IEEE Trans. on AP,1985,33(9):929-936
[49] M. W. Rath. Survey of neural network technology for automatic target recognition. IEEETrans. on NN,1990,1(1):28-43
[50]廖学军,保铮.雷达目标距离像分类机理研究.西安电子科技大学学报,1997,24:68-74
[51] L. M. Novak, G. J. Owirka. Radar target identification using an eigen---image approach. IEEENat. Radar Conf., Atlanta,1994:129-131
[52] L. E. Pierce, I. Sarabandi, F. T. Ulaby, et al. Knowledge-based classification of SAR images.IEEE IGARSS’93,1993,4:1611-1613
[53] N. S. Martin. SAR systems resolution reviewed for target classification in knowledge-basedenvironment. Proceedings of IEEE Electronic Technology Directions to the Year2000,1995,5:177-183
[54] H. Osman, S. D. Blostein. SAR image processing using probabilistic winner-take-all learningand artificial neural networks. Proc. IEEE Int. Conf. on IP,1996,2:613-616
[55] A. Maki. ISAR Image Analysis by Subspace Method: Automatic Extraction and Identificationof Ship Profile. IEEE Radar2001:523-528
[56] A. Maki. Automatic Ship Identification in ISAR Imagery: An On-line System using CMSM.IEEE Radar2002:206-211
[57]秦敬喜.基于散射中心模型的高分辨雷达目标识别方法研究:[硕士学位论文].长沙:国防科技大学,2008,1-77
[58] J. Li, P. Stoica. Efficient mixed-specturm estimation with application to feature extraction.IEEE Trans. on Signal Processing,1996,42(2):281-295
[59] K. T. Kim, D. K. Seo, H. T. Kim. Efficient radar target recognition using the Music algorithmand invariant features. IEEE Trans. on AP,2002,50(3):325-337
[60] R. Carriere, R. L. Moses. High resolution radar target modeling using a modified Pronyestimator. IEEE Trans. on AP,1992,40(1):13-18
[61] P. Bharadwaj, P. Runkle, L. Carin, et al. Multiaspect classification of airborne target viaphysics-based HMMs and matching pursuits. IEEE Trans. on AES,2001,37(2):595-606
[62] R. Carrierie, R. L. Moses. Autoregressive Moving Average Modeling of Radar TargetSignature. Proceedings of the IEEE1988National Radar Conference,1988:225-229
[63] R. M. Nuthalapati. Autoregressive Modeling of Wideband Radar signals. SPIE,1995,2562:116-124
[64] K. B. Eom, R. Chellappa. Noncooperative Target Classification Using Hierarchical Modelingof High-Range Resolution Radar Signatures. IEEE Transactions on Signal Processing,1997,45(9):2318-2327
[65]姬红兵,李洁,谢维信,贺伟.基于AR模型参数双谱估计的雷达目标识别.电子科学学刊,1999,21(6)：843-847
[66] H. J. Li, S. H. Yang. Using range profiles as feature vectors to identify aerospace objects.IEEE Trans. AP,1993,41(3):261–268
[67] A. Zyweck, R. E. Bogner. Radar target classification of commercial aircraft. IEEE Trans.A.E.S..1996,32(2).598-606
[68] R. Hu, Z. Zhu. Research on Radar Classification based on High Resolution Range Profiles. InProceedings of Aerospace and Electronics Conference1997,2:951-955
[69] A. Zyweck, R. E. Bogner. Coherent averaging of range profiles. In Proceedings of IEEEInternational Radar Conference,1995,456-461
[70] H. J. Li, Y. D. Wang, L. H. Wang. Matching score properties between range profile ofhigh-resolution radar targets. IEEE tans. A.P..1996,44(4):444-452
[71]陈凤,侯庆禹,刘宏伟等.一种新的雷达HRRP自适应划分角域建模方法.西安电子科技大学学报,2009,36(3):410-417
[72] B. Chen, H. Liu, L. Yuan, et al. Adaptively Segmenting Angular Sectors for Radar HRRP ATR.EURASIP Journal on Advances in Signal Processing,2009,36(3):410-417
[73] S. P. Jacobs. Automatic target recognition using high-resolution radar range profiles.[D],Washington University.1999,1-80
[74] J. P. Zwart, R. V. Heiden, S. Gelsema, et al. Fast translation invariant classification of HRRrange profiles in a zero phase representation. IEE Proc. Radar Sonar Navig.,2003,150(6):657-662
[75]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,2006,28(3):58-62
[76] E. J. Rothwell, K. M. Chen, D. P. Nyquist, et al. A radar target discrimination scheme usingthe discrete wavelet transform for reduced data storage. IEEE Trans. on AP,1994,42(7):1033-1037
[77] Q. Li, E. J. Rothwell, K. M. Chen, et al. Radar target discrmination shcemes usingtime-domain and frequency-domain methods for reduced data storage. IEEE Trans. on AP,1997,45(6):995-1000
[78] P. E. Zwicke, J. R. Imrekiss. A new implementation of the Mellin transform and itsapplication to radar classification of ships. IEEE Trans. on Pattern Analysis and MachineIntelligence,1983,5(2):256-264
[79] L. Potter, D. M. Chiang. A GTD–based parametric model for radar scattering．IEEE Trans. AP,1995,42(10):1058–1067
[80] X. Liao, Z. Bao. Circularly integrated bispectra：novel shift invariant feature for high–resolution radar target recognition．IEE Electronics Letters,1999,34(I9):1879–1880
[81] X. Zhang, Y. Shi, Z. Bao. A new feature vector using selected bispectra fo r signalclassification with application in radar target recognition．IEEE Trans on Signal Processing,2001,49(9): l875–l885
[82] Y. Shi, X. Zhang. A Gabor atom network for signal classification with application in radartarget recognition．IEEE Trans on Signal Processing，2001，49(12):2994–3004
[83] K. T. Kim, D. K. Seo, H. T. Kim. Efficient radar target recognition using the MUSICalgorithm and invariant feature. IEEE Trans A.P..2002,50(3):325-337
[84]袁莉,刘宏伟,保铮.基于中心矩特征的雷达HRRP自动目标识别.电子学报,2004,32(12):2078-2081
[85] I. Jouny, F. D. Garber, R. L. Moses. Radar target identification using the bispectrum: acomparative study. IEEE Trans. on AES,1995,31(1):69-77
[86] V. Chandran, S. L. Elgar. Pattern recognition using invariants defined from higher orderspectra one-dimensional inputs. IEEE Trans. on SP,1993,41(1):205-212
[87] J. K. Tugnait. Detection of non-Gaussian signals using integrated polyspectrum. IEEE Trans.on SP,1994,42(11):3137-3149
[88]裴炳南,保铮.用对数平均双谱识别飞机的原理和方法.电子学报.2002,30(3):354-358
[89]刘传武,张智军,豆仁福.基于时域双谱特征的雷达目标识别.数据采集与处理,2009,24(6):710-713
[90] J. Spoelstra, E.C. Botha. New rotation—invariant features for radar target recognition. SignalProcessing,1996,55(3):351-367
[91]刘本永.子空间法雷达目标一维像识别研究:[博士学位论文].成都:电子科技大学,1999:1-95
[92]刘本永,杨万麟.基于正则变换的雷达目标成像识别方法.系统工程与电子技术,1999,21(3):31-33
[93]周代英,杨万麟.基于子像空间的雷达目标一维距离像识别.系统工程与电子技术,2001,23(6):19-21
[94]周代英,杨万麟.雷达目标一维距离像识别中的修正正则子空间法.系统工程与电子技术,2001,23(10):11-12
[95]周代英,沈晓峰,杨万麟.最优聚类中心雷达目标一维距离像识别.系统工程与电子技术,2002,24(4):44-46
[96]孟继成,杨万麟.雷达目标识别中的双距离像子空间法.系统工程与电子技术,2004,26(6):724-725
[97]孟继成,杨万麟.基于核函数的雷达一维距离像目标识别.电子与信息学报,2005,3:128-132
[98]刘华林,杨万麟.基于直接辨别分析的雷达目标一维距离像识别.电波科学学报,2007,22(6):1020-1024
[99]刘华林.双辨别子空间高分辨距离像雷达目标识别.电波科学学报,2009,5:150-154
[100]丛瑜,肖怀铁,付强.基于核主分量分析的高分辨雷达目标特征提取与识别.电光与控制,2008,15(2):31-35
[101]刘华林,杨万麟.基于QR分解的广义辨别分析用于雷达目标识别.红外与毫米波学报,2007,26(3):205-208
[102] B. Chen, L. Yuan, H. W. Liu, et al. Kernel Subclass Discriminant Analysis.Neurocomputing,2007,71:455-458
[103]张亮,何松华,庄朝文等.雷达目标一维距离像多分辨特征描述和快速匹配法研究.电子科学学刊,1998,20(3):374-378
[104] E. J. Rothwell, D. P. Nyquist, K. M. Chen, et al. A radar target discrimination shceme usingthe discrete wavelet transform for reduced data storage. IEEE Trans. on AP,1994,42(7):1033-1037
[105]李跃华,高敦堂,李兴国.基于小波神经网络的毫米波频率步进雷达目标识别.电子测量与仪器学报,2001,15(4):21-24
[106]赵鹏亮,席泽敏,肖欢.基于小波能量距的雷达距离像特征提取.雷达科学与技术,2007,5(4):242-245
[107]肖怀铁,付强,庄钊文等.宽带毫米波雷达目标时延神经网络识别新方法.红外与毫米波学报,2001,20(6):459-463
[108] E. C. Botha. Feature based classification of aerospace radar targets using neural networks.Neural Networks,1996,9(1):129-142
[109]朱志宇,张冰,刘维亭.小波网络的雷达目标识别方法.火力与指挥控制,2007,32(11):108-111
[110]陈大庆,保铮.基于多层前向网络的雷达目标一维距离像识别.西安电子科技大学学报,1997,24(1):1-6
[111]付强,胡磊,周剑雄.基于模糊-粗糙集和傅里叶-梅林变换的HRRP识别.现代雷达,2009,31(4):61-64
[112]周代英,沈晓峰,杨万麟.雷达目标一维距离像识别中的非训练目标判别.电波科学学报,2002,2：51-54
[113]刘传武,张智军,毕笃彦.雷达目标自动识别系统的拒识新算法.系统工程与电子技术,2009,31(8):1846-1850
[114]柴晶,刘宏伟,保铮.一种提高雷达HRRP识别和拒判性能的新方法.西安电子科技大学学报,2009,36(2):233-239
[115]柴晶,刘宏伟,保铮.提高雷达HRRP目标识别和拒判性能的核学习算法.西安电子科技大学学报,2009,36(5):793-800
[116]陶勇,胡卫东.基于知识的雷达目标识别研究.现代雷达,2009,31(1):62-68
[117]李莹,任勇,山秀明.基于支持向量机的高分辨距离像分类法.系统工程与电子技术,2002,24(11):8-10
[118] T. Quach, M. Farooq. Investigation of a Multiple Passive Sensor Tracking Algorithm. Proc.Of the37th Midwest Symposium on Circuits and Systems,1994,1:195-198
[119] H. J. Li, R.Y. Lane. Utilization of multiple polarization data for aerospace target identification.IEEE Erans.on AP,1995,43(12):1436-1440
[120] N. C. Parmar, M. Codex, M. M. Kokar. Target detection in fused X-band radar and IR imagesusing the functional minimization approach to data association. Proceedings of IEEE Int.Symp. on Intelligent Control,1994:51-56
[121]陈江峰,裴炳南.一种基于HMM的雷达目标识别方法.郑州大学学报,2003,35(1):52-56
[122]周德全.基于一维距离像的雷达目标识别研究:[博士学位论文].南京:南京理工大学,1998,1-90
[123] B. N. Pei, Z. Bao. Multi-aspect radar target recognition method based on scattering centersand HMMs Classifiers. IEEE Trans. on AES,2005,41(3):1067-1074
[124] L. R. Rabiner. A tutorial on hidden Markov models and selected application in speechrecognition. Proceedings of IEEE,1989,77(2):257-286
[125] N. Freidman. Bayesian network classifiers. Machine Learning,1997,29(2-3):131-163
[126] D. Heckerman. Bayesian networks for data mining. Data mining and knowledge discovery,1997,1:79-119
[127]于雪莲.基于核方法和流形学习的雷达目标距离像识别研究:[博士学位论文].成都:电子科技大学,2008,1-87
[128]廖学军.基于高分辨距离像的雷达目标识别:[博士学位论文].西安:西安电子科技大学,1999,1-100
[129]姜文利,唐白玉,徐可斌等.高频区雷达目标散射模型及其参数估计.电子学报,1998,26(3):70-74
[130] H. J. Li, Y. D. Wang, L. H. Wang. Matching score properties between range profile of high–resolution radar targets. IEEE Trans. AP,1996,44(4):444–452
[131] M. D. Xing, Z. Bao. The properties of range profile of aircraft. CIE International Conferenceon Radar Proceedings, Beijing,2001,1050–1054
[132] S. Hudson, D. Psaltis. Correlation filters for aircraft identification from radar range profile.IEEE Trans. AES,1993,29(3):741–746
[133] X. J. Liao, Z. Bao, M. D. Xing. On the sensitivity of high resolution range profiles and itsreduction methods. IEEE International Radar Conf.,2000:310–315
[134] S. A. Gorshkov, S. P. Leschenko, V. M. Orlenko, et al, Radar Target BackscatteringSimulation Software and User’s Manual. Boston, MA: Artech House,2002,1-71
[135] Y. D. Shirman, Computer Simulation of Aerial Target Radar Scattering, Recognition,Detection, and Tracking. Boston, MA: Artech House,2002,1-315
[136]柴晶.雷达高分辨距离像目标识别的拒判算法和特征提取技术研究:[博士学位论文].西安:西安电子科技大学,2010,1-115
[137]何松华,郭桂荣.基于宽带毫米波工作体制的雷达目标识别方法探讨.红外与毫米波学报,1992,11(6):435-440
[138] Q. Ma, Feature study for high-range-resolution based automatic target recognition: analysisand extraction:[Doctor Thesis]. OHIO: the Ohio State University,2001,1-110
[139]王涛,李士国,王秀春.一种基于高分辨距离像的目标长度特征提取算法.中国电子科学研究院学报,2006,1(6):532-535
[140]白向辉.基于高分辨距离像的雷达目标识别方法研究:[硕士学位论文].西安:西安电子科技大学,2006,1-96
[141] A. K. Shaw, V. Bhatngar. Automatic target recognition using eigen-template. Proceedings ofSPIE,1998,3370:448-459
[142] A. K. Shaw, R. Vashist. HRR-ATR using eigen-template with noisy obsevation in unknowntarget scenario. Proceedings of SPIIE,2000:467-478
[143] K. Fukunaga. Introduction to statistical pattern recognition. Orlando, FL: Academic Press,1990,1-592
[144] Richard O. Duda等著,李宏东等译.模式分类.北京:机械工业出版社,2007,1-500
[145]梁毅雄.基于子空间分析的人脸特征提取及识别研究:[博士学位论文].重庆:重庆大学,2005,1-93
[146] R. A. Fisher. The statistical utilization of multiple measurements. Ann. Eugenics,1938,8:376-386
[147] N. P. Belhumeur, J. P. Hespanha, D. J. kriefma. Eigenfaces vs. Fisherfaces: Recognition UsingClass Specific Linear Projection. IEEE Transactions On Pattern Analysis And MachineIntelligence,1997,19(7):711-720
[148]孙大瑞.统计人脸识别及面部特征检测:[博士学位论文].南京:东南大学,2003,1-90
[149]蒲书缙.复杂环境下目标识别的智能数据融合技术研究:[博士学位论文].哈尔滨:哈尔滨工程大学,2006,1-101
[150] C. Ren. Luo, G. Michael. Kay. Multisensor Integration and fusion in Intelligent Systems.IEEE Trans. Systems, Man and Cybernetics,1989,19(5):901-928
[151]付耀文.雷达目标融合识别研究:[博士学位论文].长沙:国防科技大学,2003,1-85
[152] L. Xu, A. Krzyzak, C. Suen. Methods of combining multiple classifiers and their applicationsto handwriting recognition. IEEE Trans. on SMC,1992,22(3):418-435
[153] J. Kittler, M. Hatef. On combining classifier. IEEE Trans. on Patt. Recog. And MachIntell.,1998,20(3):226-239
[154] C. M. Bishop. Neural Networks for Pattern Recognition. Oxford: Clarendon Press,1995,1-504
[155] A. k. Jain, R. Duin, J. C. Mao. Statistical pattern recognition: a review. IEEE Transactions onPattern Analysis and Machine Intelligence,2000,22(1):4-37
[156]张丽新.高维数据的特征选择及基于特征选择的集成学习研究:[博士学位论文].北京:清华大学,2004,1-130
[157] H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Boston:Kluwer Academic Publishers,1998,1-200
[158] S. Davies, S. Russl. Np-completeness of searches for smallest possible feature sets.Proceedings of the AAAI Fall94Symposium on Relevance. Menlo Park, CA.:AAAP Press,1994,37-39
[159] Weimin Ding, Hualong Bu, Shangzhi Zheng, et al. Tumor Classification by Using PCA withRelief Wrapper, Computer Science and Information Technology ICCSIT2009, Beijing, China,Aug,2009,514-517
[160] Yijun Sumn. Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications[J], IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(6):1035-1051
[161] Buqun Zhang, Shangzhi Zheng, Hualong Bu, et al. Relief wrapper based Kernel Partial LeastSquares subspace selection[C], Computer Science and Information Technology ICCSIT2009,Beijing, China, Aug,2009,44-48
[162] K. Kira, L. A. Rendell. The feature selection problem: traditional methods and a newalgorithm, Proceedings of the Ninth National conference on Aritifical Intelligence, Anaheim,California, USA,1992,129-134
[163] I. Kononenko. Estimation attributes: analysis and extension of RELIEF, Proceedings of the1994European Conference on Machine Learning,1994,171-182
[164] Marko Robnik-sikonja, Igor Kononenko. Theoretical and Empirical Analysis of ReliefF andRreliefF, Machine Learning,2003,53:23-69
[165] Jianjie Zhang, Hao Lin, Mingguo Zhao. A Fast Algorithm for Hand Gesture RecognitionUsing Relief, Sixth International Conference on Fuzzy Systems and Knowledge Discovery,China, Aug,2009,8-12
[166] P. M. Lewis, The characteristic selection problem in recognition system. IRE Transaction onInformation Theory,1962,8:171-178
[167] L. Yu, H. Liu. Feature selection for high-dimensional data: a fast correlating-based filtersolution. Proceedings of the20thInternational Conference on Machine Learning, Menlo Park,CA.:AAAI press,2003,856-863
[168] Hui Peng, Gang Rong, Zhaoqi Bian. Automated Face Recognition Using Adaptive SubspaceMethod. IEEE Int. Conf. on SYC,1996,1:88-92
[169] P. Langley. Selection of relevant features in machine learning. Proceedings of the AAAI FallSymposium on Relevance. Menlo Park, CA.:AAAI Press,1994,140-144
[170]林晓帆,丁晓青,吴佑寿等.字符识别的置信度分析.清华大学学报,1998,38(9):47-50
[171]罗烈.置信度判别嵌入式隐马尔可夫模型人脸识别.计算机应用研究,2010,5:393-396
[172]国玉晶,刘刚,刘健,郭军.基于环境特征的语音识别置信度研究.清华大学学报,20091:142-146
[173]黄石磊,匡镜明.基于SVM的置信度综合方法在语音识别中的应用.北京理工大学学报,2007,3:71-75
[174] X F, Lin. Adaptive confidence transform based classifier combination for Chinese characterrecognition. Pattern Recognition Letters,1998,19(10):975-988
[175]吴健辉,张国云,杨坤涛.基于置信度的多分类器互补集成手写数字识别.计算机工程与应用,2007,30:232-234
[176]林晓帆,丁晓青,吴佑寿.最近邻分类器置信度估计的理论分析.科学通报,1998,43(3):322-325
[177]王守觉.仿生模式识别(拓扑模式识别)——一种模式识别新模型的理论与应用.电子学报,2002,30(10):1417-1420
[178] Wang Shou-jue, Liu Xing-xing. Mathematical symbols and computing methods in highdimensional biomimetic informatics and their applications. Chinese Journal of Electronics,2008,17(1):1-7
[179] Wu Li-li, Wang Shou-jue. Study on closed-set speaker identification based on biomimeticpattern recognition. Chinese Journal of Electronics,2009,18(2):259-261
[180]来疆亮,王守觉.最小球覆盖集合算法及其在模式识别中的应用.模式识别与人工智能.2006,19(2):271-276
[2] D. R. Wehner. High Resolution Radar. Norwood, MA, Artech House, Inc.,1987,1-50
[3]刘宏伟,杜兰,袁莉等.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
[4]余贵水,卢建斌,魏钟记等.基于一维距离像的雷达目标识别研究进展.现代雷达,2010,32(6):5-10
[5]袁莉.基于高分辨距离像的雷达目标识别方法研究:[博士学位论文].西安:西安电子科技大学,2007,1-106
[6] B. Bhanu. Automatic target recognition: State of the art survey. IEEE Trans. on AES,1986,22(4):364-379
[7] M. N. Cohen. A survey of radar-based target recognition techniques. Proceeding of SPIE,1991,1470:233-242
[8] R. D. Strattan. Target identification from radar signatures. IEEE Conf. ASSP,1978:233-226
[9] V. G. Nebabin.. Methods and techniques of radar recognition [M]. Boston: Artech House.1997:1-50
[10]孙文峰.雷达目标识别技术述评.雷达与对抗,2001,3:1-8
[11]刘华林.高分辨距离像雷达自动目标识别研究:[博士学位论文].成都:电子科技大学,2008,1-100
[12]孟继成.雷达目标距离像识别研究:[博士学位论文].成都:电子科技大学,2005,1-106
[13] A.R. Webb. Gamma Mixture Models for Target Recognition. Pattern Recognition,2000,33:2045-2054
[14]边肇祺,张学工.模式识别(第二版).北京:清华大学出版社,2000,9-303
[15] N. Vapnik. The nature of statistical learning theory. New York: Springer Verlag,1995,1-188
[16] C. Cortes, V. Vapnik. Support-vector network. Machine Learning,1995,20(3):273-297
[17] C. J. C. Burges. A tutorial on support vector machines for pattern recognition. Data Miningand Knowledge Discovery,1998,2(2):121-167
[18] L. Yang. An overview of distance metric learning. Technical Report, School of ComputerScience, Carnegie Mellon University,2007
[19] Q. Zhao, Z. Bao. Radar target recognition using a radial basis function neural network. NeuralNetworks,1996,9(4):709–720
[20]杜兰.雷达高分辨距离像目标识别方法研究:[博士学位论文].西安:西安电子科技大学,2007,1-127
[21] D. K. Barton. Sputink п as observe by C band radar. IRE Nat. Conf. Rec,1959,7(5):67-73
[22] G. Linde. Use of Wide-band waveforms for Target Recognition with Surveillance Radar.Proceedings of IEEE International radar conference,2000:128-133
[23] H. Leung. Intelligent Radar Recognition System for Surveillance. Proceedings of IEEEInternational Conference on Systems, Man and Cybernetics,1995:2280-2285
[24] J. G. Teti. A Multi-feature Decision Space Approach to Radar Target Identification. IEEETrans. On AES.1996,32(1):480-487
[25] P. D. William, W. W. William. Radar Development at Lincoln Laboratory: An Overview of theFirst Fifty Years. Lincoln Laboratory Journal.2000,12(2):147-166
[26]周代英.雷达目标一维距离像识别研究:[博士学位论文].成都:电子科技大学,2001,1-92
[27] H. Hynes, R. E. Gardner. Doppler spectra of S band and X band signals. IEEE Trans. OnAES,1967,3(6):356-365
[28] G. G. Fliss, D. L. Mensa. Instrumentation for RCS measurements of modulation spectra ofAircraft blades. IEEE National Radar Conference,1986:12-13
[29] M. R. Bell, R. A. Grubbs. JEM modeling and measurement for radar target identification.IEEE Trans. on AES,1993,29(1):73-87
[30] D. Giuli. Polarization diversity in radars. Proc.IEEE,1986,74(2):245-269
[31] W. L. Cameron. Feature motivated polarization scattering matrix decomposition. IEEE Int.Radar Conf.,1990:549-557
[32]黄培康,殷红成,许小剑.雷达目标特性.北京:电子工业出版社,2005,229-278
[33]王晓丹,王积勤.雷达目标识别技术综述.现代雷达,2003,25(5):22-26
[34] W. Cameron. Feature motivated polarization scattering matrix decomposition. IEEE Int.Radar Conf.,1990:549-557
[35] N. F. Chamberlain, E. K. Walton, Garber F D. Radar target identification of aircraft usingpolarization-diverse features. IEEE Trans. on AES,1991,27(1):58-66
[36]冯德军,王雪松,肖顺平等.全极化高分辨雷达距离像统计识别方法.电子与信息学报,2006,28(3):517-521
[37] T. Tenoux, G. Y. Delisle, P. Fournet, et al. Analysis and interpretation of high resolutionpolarimetric SAR images.4e Colloque International sur le Radar,1994:358-363
[38] C. E. Baum. On the singularity expansion method for solution of electromagnetic interactionproblems. Air Force Weapons Lab. Interaction Notes88, Dec.1971
[39] L. W. Pearson, M. J. Blaricum, Mittra R. A new method for radar target recognition basedupon the singularity expansion method. Rec. IEEE Int. Radar Conf., Arlington, VA,1975:452-457
[40] M. J. Blaricum, R. Mittra. A technique for extracting the poles and residues of a systemdirectly form its transient response. IEEE Trans. on AP,1975,23:777-781
[41] V. K. Jain, T. K. Sarkar, D. D. Weiner. Rational modeling by pencil-of-funtions method. IEEETrans. on ASSP,1983,31(3):564-573
[42] Y. B. Hua, T. K. Sarkar. Generalized pencil-of-function method for extracting poles of an EMsystem from transient response. IEEE Trans. on AP,1989,37(2):229-234
[43] B. Drachman, E. Rothwell. A continuation method for identification of the natural frequenciesof an object using a measured response. IEEE Trans. on AP,1985,33:445-450
[44] A. J. Berni. Target identification by natural resonance estimation. IEEE Trans. on AES,1975,11(2):147-154
[45] E. M. Kennaugh. The K-pulse concept. IEEE Trans. on AP,1981,29(2):327-331
[46] K. M. Chen. Radar waveform synthesis method---a new radar detection scheme. IEEE Trans.on AP,1981,29(4):553-565
[47] K. M. Chen. Westmorelund. Radar waveform synthesis for exciting single-mode backscatterfrom a splure and application for target discrimination. Radio Science,1982,29(4):553-565
[48] E. J. Rothwell, D. P. Nyqist, K. M. Chen. Radar target discrimination using theextinction-pulse technique. IEEE Trans. on AP,1985,33(9):929-936
[49] M. W. Rath. Survey of neural network technology for automatic target recognition. IEEETrans. on NN,1990,1(1):28-43
[50]廖学军,保铮.雷达目标距离像分类机理研究.西安电子科技大学学报,1997,24:68-74
[51] L. M. Novak, G. J. Owirka. Radar target identification using an eigen---image approach. IEEENat. Radar Conf., Atlanta,1994:129-131
[52] L. E. Pierce, I. Sarabandi, F. T. Ulaby, et al. Knowledge-based classification of SAR images.IEEE IGARSS’93,1993,4:1611-1613
[53] N. S. Martin. SAR systems resolution reviewed for target classification in knowledge-basedenvironment. Proceedings of IEEE Electronic Technology Directions to the Year2000,1995,5:177-183
[54] H. Osman, S. D. Blostein. SAR image processing using probabilistic winner-take-all learningand artificial neural networks. Proc. IEEE Int. Conf. on IP,1996,2:613-616
[55] A. Maki. ISAR Image Analysis by Subspace Method: Automatic Extraction and Identificationof Ship Profile. IEEE Radar2001:523-528
[56] A. Maki. Automatic Ship Identification in ISAR Imagery: An On-line System using CMSM.IEEE Radar2002:206-211
[57]秦敬喜.基于散射中心模型的高分辨雷达目标识别方法研究:[硕士学位论文].长沙:国防科技大学,2008,1-77
[58] J. Li, P. Stoica. Efficient mixed-specturm estimation with application to feature extraction.IEEE Trans. on Signal Processing,1996,42(2):281-295
[59] K. T. Kim, D. K. Seo, H. T. Kim. Efficient radar target recognition using the Music algorithmand invariant features. IEEE Trans. on AP,2002,50(3):325-337
[60] R. Carriere, R. L. Moses. High resolution radar target modeling using a modified Pronyestimator. IEEE Trans. on AP,1992,40(1):13-18
[61] P. Bharadwaj, P. Runkle, L. Carin, et al. Multiaspect classification of airborne target viaphysics-based HMMs and matching pursuits. IEEE Trans. on AES,2001,37(2):595-606
[62] R. Carrierie, R. L. Moses. Autoregressive Moving Average Modeling of Radar TargetSignature. Proceedings of the IEEE1988National Radar Conference,1988:225-229
[63] R. M. Nuthalapati. Autoregressive Modeling of Wideband Radar signals. SPIE,1995,2562:116-124
[64] K. B. Eom, R. Chellappa. Noncooperative Target Classification Using Hierarchical Modelingof High-Range Resolution Radar Signatures. IEEE Transactions on Signal Processing,1997,45(9):2318-2327
[65]姬红兵,李洁,谢维信,贺伟.基于AR模型参数双谱估计的雷达目标识别.电子科学学刊,1999,21(6)：843-847
[66] H. J. Li, S. H. Yang. Using range profiles as feature vectors to identify aerospace objects.IEEE Trans. AP,1993,41(3):261–268
[67] A. Zyweck, R. E. Bogner. Radar target classification of commercial aircraft. IEEE Trans.A.E.S..1996,32(2).598-606
[68] R. Hu, Z. Zhu. Research on Radar Classification based on High Resolution Range Profiles. InProceedings of Aerospace and Electronics Conference1997,2:951-955
[69] A. Zyweck, R. E. Bogner. Coherent averaging of range profiles. In Proceedings of IEEEInternational Radar Conference,1995,456-461
[70] H. J. Li, Y. D. Wang, L. H. Wang. Matching score properties between range profile ofhigh-resolution radar targets. IEEE tans. A.P..1996,44(4):444-452
[71]陈凤,侯庆禹,刘宏伟等.一种新的雷达HRRP自适应划分角域建模方法.西安电子科技大学学报,2009,36(3):410-417
[72] B. Chen, H. Liu, L. Yuan, et al. Adaptively Segmenting Angular Sectors for Radar HRRP ATR.EURASIP Journal on Advances in Signal Processing,2009,36(3):410-417
[73] S. P. Jacobs. Automatic target recognition using high-resolution radar range profiles.[D],Washington University.1999,1-80
[74] J. P. Zwart, R. V. Heiden, S. Gelsema, et al. Fast translation invariant classification of HRRrange profiles in a zero phase representation. IEE Proc. Radar Sonar Navig.,2003,150(6):657-662
[75]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,2006,28(3):58-62
[76] E. J. Rothwell, K. M. Chen, D. P. Nyquist, et al. A radar target discrimination scheme usingthe discrete wavelet transform for reduced data storage. IEEE Trans. on AP,1994,42(7):1033-1037
[77] Q. Li, E. J. Rothwell, K. M. Chen, et al. Radar target discrmination shcemes usingtime-domain and frequency-domain methods for reduced data storage. IEEE Trans. on AP,1997,45(6):995-1000
[78] P. E. Zwicke, J. R. Imrekiss. A new implementation of the Mellin transform and itsapplication to radar classification of ships. IEEE Trans. on Pattern Analysis and MachineIntelligence,1983,5(2):256-264
[79] L. Potter, D. M. Chiang. A GTD–based parametric model for radar scattering．IEEE Trans. AP,1995,42(10):1058–1067
[80] X. Liao, Z. Bao. Circularly integrated bispectra：novel shift invariant feature for high–resolution radar target recognition．IEE Electronics Letters,1999,34(I9):1879–1880
[81] X. Zhang, Y. Shi, Z. Bao. A new feature vector using selected bispectra fo r signalclassification with application in radar target recognition．IEEE Trans on Signal Processing,2001,49(9): l875–l885
[82] Y. Shi, X. Zhang. A Gabor atom network for signal classification with application in radartarget recognition．IEEE Trans on Signal Processing，2001，49(12):2994–3004
[83] K. T. Kim, D. K. Seo, H. T. Kim. Efficient radar target recognition using the MUSICalgorithm and invariant feature. IEEE Trans A.P..2002,50(3):325-337
[84]袁莉,刘宏伟,保铮.基于中心矩特征的雷达HRRP自动目标识别.电子学报,2004,32(12):2078-2081
[85] I. Jouny, F. D. Garber, R. L. Moses. Radar target identification using the bispectrum: acomparative study. IEEE Trans. on AES,1995,31(1):69-77
[86] V. Chandran, S. L. Elgar. Pattern recognition using invariants defined from higher orderspectra one-dimensional inputs. IEEE Trans. on SP,1993,41(1):205-212
[87] J. K. Tugnait. Detection of non-Gaussian signals using integrated polyspectrum. IEEE Trans.on SP,1994,42(11):3137-3149
[88]裴炳南,保铮.用对数平均双谱识别飞机的原理和方法.电子学报.2002,30(3):354-358
[89]刘传武,张智军,豆仁福.基于时域双谱特征的雷达目标识别.数据采集与处理,2009,24(6):710-713
[90] J. Spoelstra, E.C. Botha. New rotation—invariant features for radar target recognition. SignalProcessing,1996,55(3):351-367
[91]刘本永.子空间法雷达目标一维像识别研究:[博士学位论文].成都:电子科技大学,1999:1-95
[92]刘本永,杨万麟.基于正则变换的雷达目标成像识别方法.系统工程与电子技术,1999,21(3):31-33
[93]周代英,杨万麟.基于子像空间的雷达目标一维距离像识别.系统工程与电子技术,2001,23(6):19-21
[94]周代英,杨万麟.雷达目标一维距离像识别中的修正正则子空间法.系统工程与电子技术,2001,23(10):11-12
[95]周代英,沈晓峰,杨万麟.最优聚类中心雷达目标一维距离像识别.系统工程与电子技术,2002,24(4):44-46
[96]孟继成,杨万麟.雷达目标识别中的双距离像子空间法.系统工程与电子技术,2004,26(6):724-725
[97]孟继成,杨万麟.基于核函数的雷达一维距离像目标识别.电子与信息学报,2005,3:128-132
[98]刘华林,杨万麟.基于直接辨别分析的雷达目标一维距离像识别.电波科学学报,2007,22(6):1020-1024
[99]刘华林.双辨别子空间高分辨距离像雷达目标识别.电波科学学报,2009,5:150-154
[100]丛瑜,肖怀铁,付强.基于核主分量分析的高分辨雷达目标特征提取与识别.电光与控制,2008,15(2):31-35
[101]刘华林,杨万麟.基于QR分解的广义辨别分析用于雷达目标识别.红外与毫米波学报,2007,26(3):205-208
[102] B. Chen, L. Yuan, H. W. Liu, et al. Kernel Subclass Discriminant Analysis.Neurocomputing,2007,71:455-458
[103]张亮,何松华,庄朝文等.雷达目标一维距离像多分辨特征描述和快速匹配法研究.电子科学学刊,1998,20(3):374-378
[104] E. J. Rothwell, D. P. Nyquist, K. M. Chen, et al. A radar target discrimination shceme usingthe discrete wavelet transform for reduced data storage. IEEE Trans. on AP,1994,42(7):1033-1037
[105]李跃华,高敦堂,李兴国.基于小波神经网络的毫米波频率步进雷达目标识别.电子测量与仪器学报,2001,15(4):21-24
[106]赵鹏亮,席泽敏,肖欢.基于小波能量距的雷达距离像特征提取.雷达科学与技术,2007,5(4):242-245
[107]肖怀铁,付强,庄钊文等.宽带毫米波雷达目标时延神经网络识别新方法.红外与毫米波学报,2001,20(6):459-463
[108] E. C. Botha. Feature based classification of aerospace radar targets using neural networks.Neural Networks,1996,9(1):129-142
[109]朱志宇,张冰,刘维亭.小波网络的雷达目标识别方法.火力与指挥控制,2007,32(11):108-111
[110]陈大庆,保铮.基于多层前向网络的雷达目标一维距离像识别.西安电子科技大学学报,1997,24(1):1-6
[111]付强,胡磊,周剑雄.基于模糊-粗糙集和傅里叶-梅林变换的HRRP识别.现代雷达,2009,31(4):61-64
[112]周代英,沈晓峰,杨万麟.雷达目标一维距离像识别中的非训练目标判别.电波科学学报,2002,2：51-54
[113]刘传武,张智军,毕笃彦.雷达目标自动识别系统的拒识新算法.系统工程与电子技术,2009,31(8):1846-1850
[114]柴晶,刘宏伟,保铮.一种提高雷达HRRP识别和拒判性能的新方法.西安电子科技大学学报,2009,36(2):233-239
[115]柴晶,刘宏伟,保铮.提高雷达HRRP目标识别和拒判性能的核学习算法.西安电子科技大学学报,2009,36(5):793-800
[116]陶勇,胡卫东.基于知识的雷达目标识别研究.现代雷达,2009,31(1):62-68
[117]李莹,任勇,山秀明.基于支持向量机的高分辨距离像分类法.系统工程与电子技术,2002,24(11):8-10
[118] T. Quach, M. Farooq. Investigation of a Multiple Passive Sensor Tracking Algorithm. Proc.Of the37th Midwest Symposium on Circuits and Systems,1994,1:195-198
[119] H. J. Li, R.Y. Lane. Utilization of multiple polarization data for aerospace target identification.IEEE Erans.on AP,1995,43(12):1436-1440
[120] N. C. Parmar, M. Codex, M. M. Kokar. Target detection in fused X-band radar and IR imagesusing the functional minimization approach to data association. Proceedings of IEEE Int.Symp. on Intelligent Control,1994:51-56
[121]陈江峰,裴炳南.一种基于HMM的雷达目标识别方法.郑州大学学报,2003,35(1):52-56
[122]周德全.基于一维距离像的雷达目标识别研究:[博士学位论文].南京:南京理工大学,1998,1-90
[123] B. N. Pei, Z. Bao. Multi-aspect radar target recognition method based on scattering centersand HMMs Classifiers. IEEE Trans. on AES,2005,41(3):1067-1074
[124] L. R. Rabiner. A tutorial on hidden Markov models and selected application in speechrecognition. Proceedings of IEEE,1989,77(2):257-286
[125] N. Freidman. Bayesian network classifiers. Machine Learning,1997,29(2-3):131-163
[126] D. Heckerman. Bayesian networks for data mining. Data mining and knowledge discovery,1997,1:79-119
[127]于雪莲.基于核方法和流形学习的雷达目标距离像识别研究:[博士学位论文].成都:电子科技大学,2008,1-87
[128]廖学军.基于高分辨距离像的雷达目标识别:[博士学位论文].西安:西安电子科技大学,1999,1-100
[129]姜文利,唐白玉,徐可斌等.高频区雷达目标散射模型及其参数估计.电子学报,1998,26(3):70-74
[130] H. J. Li, Y. D. Wang, L. H. Wang. Matching score properties between range profile of high–resolution radar targets. IEEE Trans. AP,1996,44(4):444–452
[131] M. D. Xing, Z. Bao. The properties of range profile of aircraft. CIE International Conferenceon Radar Proceedings, Beijing,2001,1050–1054
[132] S. Hudson, D. Psaltis. Correlation filters for aircraft identification from radar range profile.IEEE Trans. AES,1993,29(3):741–746
[133] X. J. Liao, Z. Bao, M. D. Xing. On the sensitivity of high resolution range profiles and itsreduction methods. IEEE International Radar Conf.,2000:310–315
[134] S. A. Gorshkov, S. P. Leschenko, V. M. Orlenko, et al, Radar Target BackscatteringSimulation Software and User’s Manual. Boston, MA: Artech House,2002,1-71
[135] Y. D. Shirman, Computer Simulation of Aerial Target Radar Scattering, Recognition,Detection, and Tracking. Boston, MA: Artech House,2002,1-315
[136]柴晶.雷达高分辨距离像目标识别的拒判算法和特征提取技术研究:[博士学位论文].西安:西安电子科技大学,2010,1-115
[137]何松华,郭桂荣.基于宽带毫米波工作体制的雷达目标识别方法探讨.红外与毫米波学报,1992,11(6):435-440
[138] Q. Ma, Feature study for high-range-resolution based automatic target recognition: analysisand extraction:[Doctor Thesis]. OHIO: the Ohio State University,2001,1-110
[139]王涛,李士国,王秀春.一种基于高分辨距离像的目标长度特征提取算法.中国电子科学研究院学报,2006,1(6):532-535
[140]白向辉.基于高分辨距离像的雷达目标识别方法研究:[硕士学位论文].西安:西安电子科技大学,2006,1-96
[141] A. K. Shaw, V. Bhatngar. Automatic target recognition using eigen-template. Proceedings ofSPIE,1998,3370:448-459
[142] A. K. Shaw, R. Vashist. HRR-ATR using eigen-template with noisy obsevation in unknowntarget scenario. Proceedings of SPIIE,2000:467-478
[143] K. Fukunaga. Introduction to statistical pattern recognition. Orlando, FL: Academic Press,1990,1-592
[144] Richard O. Duda等著,李宏东等译.模式分类.北京:机械工业出版社,2007,1-500
[145]梁毅雄.基于子空间分析的人脸特征提取及识别研究:[博士学位论文].重庆:重庆大学,2005,1-93
[146] R. A. Fisher. The statistical utilization of multiple measurements. Ann. Eugenics,1938,8:376-386
[147] N. P. Belhumeur, J. P. Hespanha, D. J. kriefma. Eigenfaces vs. Fisherfaces: Recognition UsingClass Specific Linear Projection. IEEE Transactions On Pattern Analysis And MachineIntelligence,1997,19(7):711-720
[148]孙大瑞.统计人脸识别及面部特征检测:[博士学位论文].南京:东南大学,2003,1-90
[149]蒲书缙.复杂环境下目标识别的智能数据融合技术研究:[博士学位论文].哈尔滨:哈尔滨工程大学,2006,1-101
[150] C. Ren. Luo, G. Michael. Kay. Multisensor Integration and fusion in Intelligent Systems.IEEE Trans. Systems, Man and Cybernetics,1989,19(5):901-928
[151]付耀文.雷达目标融合识别研究:[博士学位论文].长沙:国防科技大学,2003,1-85
[152] L. Xu, A. Krzyzak, C. Suen. Methods of combining multiple classifiers and their applicationsto handwriting recognition. IEEE Trans. on SMC,1992,22(3):418-435
[153] J. Kittler, M. Hatef. On combining classifier. IEEE Trans. on Patt. Recog. And MachIntell.,1998,20(3):226-239
[154] C. M. Bishop. Neural Networks for Pattern Recognition. Oxford: Clarendon Press,1995,1-504
[155] A. k. Jain, R. Duin, J. C. Mao. Statistical pattern recognition: a review. IEEE Transactions onPattern Analysis and Machine Intelligence,2000,22(1):4-37
[156]张丽新.高维数据的特征选择及基于特征选择的集成学习研究:[博士学位论文].北京:清华大学,2004,1-130
[157] H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Boston:Kluwer Academic Publishers,1998,1-200
[158] S. Davies, S. Russl. Np-completeness of searches for smallest possible feature sets.Proceedings of the AAAI Fall94Symposium on Relevance. Menlo Park, CA.:AAAP Press,1994,37-39
[159] Weimin Ding, Hualong Bu, Shangzhi Zheng, et al. Tumor Classification by Using PCA withRelief Wrapper, Computer Science and Information Technology ICCSIT2009, Beijing, China,Aug,2009,514-517
[160] Yijun Sumn. Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications[J], IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(6):1035-1051
[161] Buqun Zhang, Shangzhi Zheng, Hualong Bu, et al. Relief wrapper based Kernel Partial LeastSquares subspace selection[C], Computer Science and Information Technology ICCSIT2009,Beijing, China, Aug,2009,44-48
[162] K. Kira, L. A. Rendell. The feature selection problem: traditional methods and a newalgorithm, Proceedings of the Ninth National conference on Aritifical Intelligence, Anaheim,California, USA,1992,129-134
[163] I. Kononenko. Estimation attributes: analysis and extension of RELIEF, Proceedings of the1994European Conference on Machine Learning,1994,171-182
[164] Marko Robnik-sikonja, Igor Kononenko. Theoretical and Empirical Analysis of ReliefF andRreliefF, Machine Learning,2003,53:23-69
[165] Jianjie Zhang, Hao Lin, Mingguo Zhao. A Fast Algorithm for Hand Gesture RecognitionUsing Relief, Sixth International Conference on Fuzzy Systems and Knowledge Discovery,China, Aug,2009,8-12
[166] P. M. Lewis, The characteristic selection problem in recognition system. IRE Transaction onInformation Theory,1962,8:171-178
[167] L. Yu, H. Liu. Feature selection for high-dimensional data: a fast correlating-based filtersolution. Proceedings of the20thInternational Conference on Machine Learning, Menlo Park,CA.:AAAI press,2003,856-863
[168] Hui Peng, Gang Rong, Zhaoqi Bian. Automated Face Recognition Using Adaptive SubspaceMethod. IEEE Int. Conf. on SYC,1996,1:88-92
[169] P. Langley. Selection of relevant features in machine learning. Proceedings of the AAAI FallSymposium on Relevance. Menlo Park, CA.:AAAI Press,1994,140-144
[170]林晓帆,丁晓青,吴佑寿等.字符识别的置信度分析.清华大学学报,1998,38(9):47-50
[171]罗烈.置信度判别嵌入式隐马尔可夫模型人脸识别.计算机应用研究,2010,5:393-396
[172]国玉晶,刘刚,刘健,郭军.基于环境特征的语音识别置信度研究.清华大学学报,20091:142-146
[173]黄石磊,匡镜明.基于SVM的置信度综合方法在语音识别中的应用.北京理工大学学报,2007,3:71-75
[174] X F, Lin. Adaptive confidence transform based classifier combination for Chinese characterrecognition. Pattern Recognition Letters,1998,19(10):975-988
[175]吴健辉,张国云,杨坤涛.基于置信度的多分类器互补集成手写数字识别.计算机工程与应用,2007,30:232-234
[176]林晓帆,丁晓青,吴佑寿.最近邻分类器置信度估计的理论分析.科学通报,1998,43(3):322-325
[177]王守觉.仿生模式识别(拓扑模式识别)——一种模式识别新模型的理论与应用.电子学报,2002,30(10):1417-1420
[178] Wang Shou-jue, Liu Xing-xing. Mathematical symbols and computing methods in highdimensional biomimetic informatics and their applications. Chinese Journal of Electronics,2008,17(1):1-7
[179] Wu Li-li, Wang Shou-jue. Study on closed-set speaker identification based on biomimeticpattern recognition. Chinese Journal of Electronics,2009,18(2):259-261
[180]来疆亮,王守觉.最小球覆盖集合算法及其在模式识别中的应用.模式识别与人工智能.2006,19(2):271-276
© 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 |