弹道中段目标雷达识别与评估研究
|
摘要
弹道中段是导弹防御(MD)系统目标识别最具挑战性的阶段,雷达是中段最主要的传感器,其目标识别能力在很大程度上反映了MD系统中段目标识别的总体水平。深入开展弹道中段目标雷达识别与评估研究,对于发展我国新体制雷达探测技术及增强我国弹道导弹生存和突防能力具有十分重大的军事意义和理论价值。
本文源于国家安全重大基础研究973项目、全国百优博士论文专项资金资助项目、总装国防预研项目等多项项目。论文以弹道导弹突防为研究背景,以中段目标群为研究对象,深入系统地研究了弹道中段目标的雷达特性、弹道中段目标的特征提取方法、弹道中段目标雷达识别方法和评估方法等多项关键技术。论文充分利用了微波暗室测量数据并通过逼真的战情仿真,构造了弹道目标识别的动态仿真平台,在若干典型战情下进行了目标特性、特征提取、目标识别和评估研究。在研究中注重所提取特征的物理意义及所提出识别和评估方法的工程可实现性,得到了一系列有价值的结果。
雷达目标特性研究是雷达特征提取的基石。为此,论文首先研究了中段目标的雷达特性。在理论上详细分析了弹道中段目标的运动特性,建立了相应的运动学模型。然后,结合已有的测量数据,对中段目标的静态散射特性进行了分析。在此基础上,提出了一种中段目标动态散射特性的仿真方法,该方法能逼真反映中段目标的雷达散射特性。最后,通过所提出的方法分析了弹头和诱饵的窄带和宽带特性。
特征提取是雷达目标识别的关键环节,能否提取出物理意义清晰、可分性强的特征在一定程度上决定了识别的成功与否。论文根据中段目标群的运动特性和结构特性,提出了一系列的特征提取方法,其中包括基于窄带序列的RCS周期特征估计方法、基于单个回波和回波序列的运动特征提取方法、基于单个宽带回波的结构特征提取方法及基于非高斯模型的结构特征提取方法。通过这些方法,可以获得目标的章动(或翻滚)周期、速度、径向距离、长度等物理意义清晰的特征,为后续的识别奠定了良好的基础。
对弹道目标进行ISAR成像是当前弹道目标特征提取的一个重要方向。论文根据中段目标高速飞行的特点及宽带回波全去斜处理的工作方式,详细论述了对高速目标距离像的展宽补偿方法和残余相位补偿方法,提出了对弹道目标进行ISAR超分辨成像的酉ESPRIT方法,该方法能明显改善成像质量、提高成像效率。接着,论文分析了中段目标的ISAR成像距离及成像积累时间。最后,通过对实测弹头和缩比模型的成像,分析了弹头类目标的ISAR图像特点,在此基础上提出了弹头类目标的ISAR图像特征提取方法。
识别器设计与评估是目标识别的中心环节。论文根据弹道目标识别特点,设计了两种识别器:基于多特征综合的模糊识别器和基于分类树的识别器。在特征评估方面,提出了基于可分测度的评估方法和基于模糊理论的评估方法;在识别评估方面,提出了基于置信度的可靠性评估方法和基于ROC曲线的评估方法。
论文紧紧围绕弹道中段目标雷达识别与评估这一主题,进行了深入、系统的理论分析,开展了一系列的理论创新和方法创新,创新点主要体现在:
1.在理论上深入分析了弹道中段目标的运动特点,通过结合微波暗室测量数据,提出了一种弹道目标动态散射特性的仿真方法,该方法可构造逼真的空间目标识别电磁环境,有效解决弹道目标识别研究中的数据缺乏问题;
2.多层次、多角度地研究了弹道中段目标的特征提取问题,提出了获取目标运动特性、结构特征的多种新方法,这些物理意义清晰的特征获取为后续的目标识别奠定了良好基础;
3.深入、系统地研究了弹道中段目标的ISAR成像及特征提取问题,提出了酉ESPRIT超分辨成像算法,估算了成像距离和成像积累时间,通过对大量实测数据成像分析了弹头类目标的ISAR图像特点并在此基础上提出了相应的特征提取方法;
4.针对弹道目标识别先验信息相对缺乏的特点,设计了两种分类器:基于多特征综合的模糊分类器和基于分类树的分类器,这两种方法均只需要相对简单的先验信息,适用于弹道真假目标识别场景;
5.开展了弹道目标特征及识别方法评估研究,提出了基于可分性测度的特征评估、基于置信度的识别可靠性评估等多种方法,为分类器的设计、组合和优化提供了理论支撑和可行手段。
在注重理论研究的同时,论文通过仿真手段并充分利用已有测量数据,进行了大量仿真试验,着力使所提出的方法得到验证。最后还要指出的是,本论文研究内容来源于实际项目需求,而大多数研究成果也已成功应用于工程项目中,并取得了良好效果。
In missile defense program, the midcourse of trajectory is the most difficult phase for alleging fraud. As the main sensor, the ground-based radar represents the classification capability of the missile defence system to a great extent. Making researche on radar target recognition and evaluation in midcourse is provided with great significance not only for developing new-type radar, but also for promoting the surviving and penetrating ability for our ballistic missile.
This dissertation is sponsored by several funds, including the fund of national excellent dissertation, the 973 momentous foundation fund of nation security and the advance research fund of general equipment office. Under the background of ballistic missiles' penetration, a series of key technique of radar target recognition are invested systematically, including the radar signature of midcourse objects, the method of feature extraction, the method of pattern recognition and the method of evaluation. By using static measurement data and simulating the real scene of recognition, the dissertation establishes a platform of dynamic recognition, on which recognition and evaluation for typical scenario of ballistic missile penetration in midcourse are investigated and valuable results are obtained.
The study on signature of radar target is a foundation for feature extraction. So, the radar signatures of midcourse objects are studied at first. After the movement feature of midcourse objects analyzed in theory, a novel approach is proposed, which can silmulate the dynamic scattering feature of midcourse objects. Lastly, the narrowband signatures and wideband signatures of warhead and decoy are analyzed through the proposed method.
Feature extraction plays a key role in radar target recognition, and is thoroughly analyzed in this dissertation. A series of feature extraction methods are put forward, including the RCS periodicity estimation algorithms based on narrowband echoes, the moving feature extraction algorithms based on narrowband and wideband echoes, the shape feature extraction algorithms based on wideband echoes. Through those methods, many features of midcourse objects are obtained, such as the length, the nutation rate, the velocity and range of object relative to radar, and so on, which facilitate subsequent alleging fraud.
ISAR imaging of ballistic target is an important means of distinguishing decoy. Based on the signature analysis of wideband echo of moving objects with high velocity in midcourse, the compensation methods for high-resolution rangeprofile (HRRP) and for residual video phase (RVP) are presented detailedly. And then, a new ISAR imaging algorithm is proposed based on the unitary ESPRIT technique, which improves ISAR imaging evidently with a reduced computational burden. After estimating the range and rotation angle of ISAR imaging, the dissertation analyzes the ISAR image characteristic of warhead, by using measurement experimental data. Lastly, the approach to extraction ISAR image feature of warhead is presented.
The design and evaluation on classifier are pivotal steps in target recognition. Two classifiers, the fuzzy classifier based on multi-feature fusion and the classifier based on decision tree, are proposed for alleging fraud in view of the absence of apriori information.On the other hand, two evaluation approaches, the evaluation approach based on separability criterion and the evaluation approach based on fuzzy theory are put forward to evaluate the extracted features. Furthermore, the dissertation also makes contributions towards the evaluation of radar target recognition classification systems.
The work of the dissertation is focused on research of radar target rcognition and evaluation in midcourse. Some valuable results which bring forth new ideas are achieved. The main creativeness is listed as the following:
1. After the kinetic characteristic of target in midcourse anlyzed thoroughly, by using static measurement data, a novel simulation approach to dynamic scattering signature of radar target is proposed, which provides a solution to the absence of real recognition data;
2. On the basis of feature extraction study of midcourse objects in the round, many new feature extraction methods are presented, including the methods of extracting movement feature and the methods of extracting shape feature of objects, which facilitate later alleging fraud;
3. The dissertation makes a deep research on ISAR imaging of ballistic objects in midcourse. After the range and coherent accumulating time for imaging are discussed, the. dissertation analyzes the peculiarity of warhead image and presentes the corresponding feature extraction method. In addition, a new ISAR imaging algorithm is proposed based on the unitary ESPRIT;
4. Considering the absence of apriori information, the dissertation designs the fuzzy classifer based on multi-feature fusion and the classifier based on decision tree, both of which are suitable for discriminating decoy because they only need simple apriori information;
5. Multiple evaluation methods are demonstrated for feature and classifier evaluation, such as the feature evaluation method based on separability criterion and the classifer evaluation method based on confidence. Those methods provide theoretic support and feasible means for designing, choosing and combining classifiers.
The dissertation not only attaches lots of importance to theory research, at the same time, by taking the advantage of measurement data and simulation means, it also performs lots of simulation experimentation to test the proposed theories and methods.At last, we must point out that the research in the dissertation stems from practical projects, and its conclusion and methods are mostly applied in practice with fairly good effects.
本文源于国家安全重大基础研究973项目、全国百优博士论文专项资金资助项目、总装国防预研项目等多项项目。论文以弹道导弹突防为研究背景,以中段目标群为研究对象,深入系统地研究了弹道中段目标的雷达特性、弹道中段目标的特征提取方法、弹道中段目标雷达识别方法和评估方法等多项关键技术。论文充分利用了微波暗室测量数据并通过逼真的战情仿真,构造了弹道目标识别的动态仿真平台,在若干典型战情下进行了目标特性、特征提取、目标识别和评估研究。在研究中注重所提取特征的物理意义及所提出识别和评估方法的工程可实现性,得到了一系列有价值的结果。
雷达目标特性研究是雷达特征提取的基石。为此,论文首先研究了中段目标的雷达特性。在理论上详细分析了弹道中段目标的运动特性,建立了相应的运动学模型。然后,结合已有的测量数据,对中段目标的静态散射特性进行了分析。在此基础上,提出了一种中段目标动态散射特性的仿真方法,该方法能逼真反映中段目标的雷达散射特性。最后,通过所提出的方法分析了弹头和诱饵的窄带和宽带特性。
特征提取是雷达目标识别的关键环节,能否提取出物理意义清晰、可分性强的特征在一定程度上决定了识别的成功与否。论文根据中段目标群的运动特性和结构特性,提出了一系列的特征提取方法,其中包括基于窄带序列的RCS周期特征估计方法、基于单个回波和回波序列的运动特征提取方法、基于单个宽带回波的结构特征提取方法及基于非高斯模型的结构特征提取方法。通过这些方法,可以获得目标的章动(或翻滚)周期、速度、径向距离、长度等物理意义清晰的特征,为后续的识别奠定了良好的基础。
对弹道目标进行ISAR成像是当前弹道目标特征提取的一个重要方向。论文根据中段目标高速飞行的特点及宽带回波全去斜处理的工作方式,详细论述了对高速目标距离像的展宽补偿方法和残余相位补偿方法,提出了对弹道目标进行ISAR超分辨成像的酉ESPRIT方法,该方法能明显改善成像质量、提高成像效率。接着,论文分析了中段目标的ISAR成像距离及成像积累时间。最后,通过对实测弹头和缩比模型的成像,分析了弹头类目标的ISAR图像特点,在此基础上提出了弹头类目标的ISAR图像特征提取方法。
识别器设计与评估是目标识别的中心环节。论文根据弹道目标识别特点,设计了两种识别器:基于多特征综合的模糊识别器和基于分类树的识别器。在特征评估方面,提出了基于可分测度的评估方法和基于模糊理论的评估方法;在识别评估方面,提出了基于置信度的可靠性评估方法和基于ROC曲线的评估方法。
论文紧紧围绕弹道中段目标雷达识别与评估这一主题,进行了深入、系统的理论分析,开展了一系列的理论创新和方法创新,创新点主要体现在:
1.在理论上深入分析了弹道中段目标的运动特点,通过结合微波暗室测量数据,提出了一种弹道目标动态散射特性的仿真方法,该方法可构造逼真的空间目标识别电磁环境,有效解决弹道目标识别研究中的数据缺乏问题;
2.多层次、多角度地研究了弹道中段目标的特征提取问题,提出了获取目标运动特性、结构特征的多种新方法,这些物理意义清晰的特征获取为后续的目标识别奠定了良好基础;
3.深入、系统地研究了弹道中段目标的ISAR成像及特征提取问题,提出了酉ESPRIT超分辨成像算法,估算了成像距离和成像积累时间,通过对大量实测数据成像分析了弹头类目标的ISAR图像特点并在此基础上提出了相应的特征提取方法;
4.针对弹道目标识别先验信息相对缺乏的特点,设计了两种分类器:基于多特征综合的模糊分类器和基于分类树的分类器,这两种方法均只需要相对简单的先验信息,适用于弹道真假目标识别场景;
5.开展了弹道目标特征及识别方法评估研究,提出了基于可分性测度的特征评估、基于置信度的识别可靠性评估等多种方法,为分类器的设计、组合和优化提供了理论支撑和可行手段。
在注重理论研究的同时,论文通过仿真手段并充分利用已有测量数据,进行了大量仿真试验,着力使所提出的方法得到验证。最后还要指出的是,本论文研究内容来源于实际项目需求,而大多数研究成果也已成功应用于工程项目中,并取得了良好效果。
In missile defense program, the midcourse of trajectory is the most difficult phase for alleging fraud. As the main sensor, the ground-based radar represents the classification capability of the missile defence system to a great extent. Making researche on radar target recognition and evaluation in midcourse is provided with great significance not only for developing new-type radar, but also for promoting the surviving and penetrating ability for our ballistic missile.
This dissertation is sponsored by several funds, including the fund of national excellent dissertation, the 973 momentous foundation fund of nation security and the advance research fund of general equipment office. Under the background of ballistic missiles' penetration, a series of key technique of radar target recognition are invested systematically, including the radar signature of midcourse objects, the method of feature extraction, the method of pattern recognition and the method of evaluation. By using static measurement data and simulating the real scene of recognition, the dissertation establishes a platform of dynamic recognition, on which recognition and evaluation for typical scenario of ballistic missile penetration in midcourse are investigated and valuable results are obtained.
The study on signature of radar target is a foundation for feature extraction. So, the radar signatures of midcourse objects are studied at first. After the movement feature of midcourse objects analyzed in theory, a novel approach is proposed, which can silmulate the dynamic scattering feature of midcourse objects. Lastly, the narrowband signatures and wideband signatures of warhead and decoy are analyzed through the proposed method.
Feature extraction plays a key role in radar target recognition, and is thoroughly analyzed in this dissertation. A series of feature extraction methods are put forward, including the RCS periodicity estimation algorithms based on narrowband echoes, the moving feature extraction algorithms based on narrowband and wideband echoes, the shape feature extraction algorithms based on wideband echoes. Through those methods, many features of midcourse objects are obtained, such as the length, the nutation rate, the velocity and range of object relative to radar, and so on, which facilitate subsequent alleging fraud.
ISAR imaging of ballistic target is an important means of distinguishing decoy. Based on the signature analysis of wideband echo of moving objects with high velocity in midcourse, the compensation methods for high-resolution rangeprofile (HRRP) and for residual video phase (RVP) are presented detailedly. And then, a new ISAR imaging algorithm is proposed based on the unitary ESPRIT technique, which improves ISAR imaging evidently with a reduced computational burden. After estimating the range and rotation angle of ISAR imaging, the dissertation analyzes the ISAR image characteristic of warhead, by using measurement experimental data. Lastly, the approach to extraction ISAR image feature of warhead is presented.
The design and evaluation on classifier are pivotal steps in target recognition. Two classifiers, the fuzzy classifier based on multi-feature fusion and the classifier based on decision tree, are proposed for alleging fraud in view of the absence of apriori information.On the other hand, two evaluation approaches, the evaluation approach based on separability criterion and the evaluation approach based on fuzzy theory are put forward to evaluate the extracted features. Furthermore, the dissertation also makes contributions towards the evaluation of radar target recognition classification systems.
The work of the dissertation is focused on research of radar target rcognition and evaluation in midcourse. Some valuable results which bring forth new ideas are achieved. The main creativeness is listed as the following:
1. After the kinetic characteristic of target in midcourse anlyzed thoroughly, by using static measurement data, a novel simulation approach to dynamic scattering signature of radar target is proposed, which provides a solution to the absence of real recognition data;
2. On the basis of feature extraction study of midcourse objects in the round, many new feature extraction methods are presented, including the methods of extracting movement feature and the methods of extracting shape feature of objects, which facilitate later alleging fraud;
3. The dissertation makes a deep research on ISAR imaging of ballistic objects in midcourse. After the range and coherent accumulating time for imaging are discussed, the. dissertation analyzes the peculiarity of warhead image and presentes the corresponding feature extraction method. In addition, a new ISAR imaging algorithm is proposed based on the unitary ESPRIT;
4. Considering the absence of apriori information, the dissertation designs the fuzzy classifer based on multi-feature fusion and the classifier based on decision tree, both of which are suitable for discriminating decoy because they only need simple apriori information;
5. Multiple evaluation methods are demonstrated for feature and classifier evaluation, such as the feature evaluation method based on separability criterion and the classifer evaluation method based on confidence. Those methods provide theoretic support and feasible means for designing, choosing and combining classifiers.
The dissertation not only attaches lots of importance to theory research, at the same time, by taking the advantage of measurement data and simulation means, it also performs lots of simulation experimentation to test the proposed theories and methods.At last, we must point out that the research in the dissertation stems from practical projects, and its conclusion and methods are mostly applied in practice with fairly good effects.
引文
[1]August W, Rihaczek, Stephen J, Hershkowitz. Theory and practice of radar target idenfitication. Boston. London, Artech House, 2000
[2]V G Nebabin, Methods and techniques of radar recognition. Boston, London, Artech House, 1995
[3]黄培康.反导系统中的目标识别技术.战略防御,1981(5):1～14
[4]郭桂蓉,庄钊文,陈曾平.电磁特征抽取与目标识别.长沙:国防科技大学出版社,1995
[5]王树生,李学焦,吴常信,等.前苏联反导系统的技术发展历程.飞航导弹,2001,31(12):34～42
[6]杨华,陈昌明,高桂清,等.美国反导系统的发展历程和技术现状.飞航导弹,2001,31(7):11～15
[7]David R, Tanks. National missile defense: policy issues and technological capabilities. Washington, SvecConway Priting Inc, 2000
[8]蒋艳清.TMD战略对东亚及我国安全的影响.硕士学位论文,武汉:华中师范大学,2002.03
[9]甘斯勒.美国国家导弹防御计划与拦截飞行试验.863先进防御技术(A类),2000(5):13～33
[10]马骏声.目标识别与GBR地基成像雷达.航天电子对抗,1996,12(4):32～35
[11]史别.识别真假目标是NMD的最大技术难题.863先进防御技术通讯(A类),2001(8):30～32
[12]863先进防御技术通讯(A类)编辑部.美国国防部声称NMD能够识别真假目标.863先进防御技术通讯简讯,2000(14):1～2
[13]薛雷达译.美国国家导弹防御系统地基雷达测量与识别能力.863先进防御技术通讯(A类),2000(6):1～17
[14]刘延柱.陀螺力学.北京:科学出版社,1986
[15]D. J Fouts, P. E. Pace, C. Karow, et al. A single-chip false target radarimage generator for countering wideband image radar. IEEE Journal of Solid State Circuits, 2001,37(6):751～758
[16]P. E. Pace, D. J. Fouts, S. Ekestrm, et al. Digital false-target image synthesizer for countering ISAR. IEE Proceedings-Radar, Sonar and Navigation, 2002,149(5):248～257
[17]Femando A.L. D. Performance analysis of digital image synthesizer as a counter-measure against inverse svntheticradar. Master's Thesis, Naval Postgraduate School, Monterey CA, 2002
[18]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
[19]黄培康,闫锦.弹道导弹(BM)对抗中的识别与反识别技术.航天电子对抗,2005,21(3):1～3
[20]裴云天.红外技术在美国国家导弹防御计划中的应用.红外,2002(1):1～6.
[21]I J.凯斯(美).光探测器与红外探测器.北京:科学出版社,1984
[22]徐南荣,卞南华.红外幅射与制导.北京:国防工业出版社,1997.
[23]王军.弹道目标空间反识别措施发展态势浅析.系统工程与电子技术,2002,24(7):53～57
[24]杨华,凌永顺,陈昌明,等.美国反导系统红外探测、跟踪和识别技术分析.红外技术,2001,23(4):1～3
[25]李群章.弹道导弹弹道中段和再入段弹头红外光学识别方法研究.红外与激光工程,1999,28(5):1～5
[26]赵锋伟,沈振康,李吉成.红外诱饵辨识的仿真研究(一)灰度时间序列分析.红外与激光工程,2002,31(4):286～289
[27]赵锋伟,沈振康,李吉成.红外诱饵辨识的仿真研究(一)运动特征分析.红外与激光工程,2002,31(5):395～398
[28]J. D. Mill, B. D. Guilmain. The MSX mission objectives. John Hopkins APL technical digest, 1996,17(1):4～10
[29]王伟,张汉华,姜卫东,等.低分辨雷达的目标特征提取方法.国防科技大学学报,2002,24(2):31～35
[30]Van Blaricum, Mittra R. A technique for extracting the poles and residues of a system directly from its transient response. IEEE Trans. on AES, 1975,23(6):1046～1051
[31]Bennett C. L. Toomey J. P. Target classification with multiple frenquency illumination. IEEE Trans. on AP, 1981,29(2):352～358
[32]Kennaugh E. M. The K-pulse concept. IEEE Trans. on AP, 1981,29(2):327～331
[33]Chen K M. Radar wave synthesis method-a new radar detection schem. IEEE Trans. on AP, 1981,29(4):553～565
[34]Keller, J. B. Geometrical theory of diffraction. J. Opt. Soc. Am. 1962,52(2):116～118
[35]Borden B. High frequency statistical classification of complex targets using severely aspectlimited data. IEEE Trans. on AP, 1986,34(12):1455～1459
[36]Mr. Thomas Foster. Application of pattern recognition techniques for early warning radar (EWR) discrimination. ADA298895,27 Jan 1995
[37]Peter Swerling. Radar probability of detection for additional fluctuaing target case. IEEE Trans. on AES, 1997,33(2):698～709
[38]Xu Xiaojian, Huang Peikang. A new RCS statiscal model of radar target. IEEE Trans. on AES, 1997,33(2):710～714
[39]许小剑,黄培康.利用RCS幅度信息进行雷达目标识别.系统工程与电子技术.1992,14(6):1～9
[40]NanZhi Jiang, Jian Li. Super resolution feature extraction of moving targets. IEEE Tans. on AES, 2001,37(3):781～792
[41]Zhaoqiang Bi, Rebao Wu, Jian Li. Simultaneous HRR feature extraction and doppler shift estimation of moving target with rigid bodies. SPIE Conference, 1999,3721:425～435
[42]Li H, Yang S H. Using rangeprofiles as feature vectors to identify aerospace objects. IEEE Trans. on AP. 1993,41(3):261～268
[43]Hudson S, Psaltis D. Correlation filters for aircraft identification from radar range profiles. IEEE Trans. on AES, 1993,29(3):741～748
[44]E. J. Rothwell, D. P. Nyquist, K. M Chen. A radar target discrimination scheme using the discrete wavelet transform for reduced data storage. IEEE Trans. on AP, 1994,42(7):1033～1037
[45]Yu Shi, Xianda Zhang. Local bispectra based high-resolution radar classification. IEEE Proceedings of ICSE, 2000,1968～1971
[46]K. Wong. Non-cooperative target recognition in the frequency domain. IEE Proc-Radar, Sonar and Navigation, 2004,151(2):77～84
[47]Junshui Ma, Stanley Ahalt. Analysis of complex HRR range signature. Proceeding of SPIE, 2000,4029:130～135
[48]Zwike Pe, Kiss I. A new implementation of the Mellin transforma and its application to radar classification of ships. IEEE Trans. on PAMI, 1989,5(2):191～199
[49]Hu R, Zhu Z D. Researches on radar target classification based on high resolution range profiles. IEEE Conf., 1997:951～955
[50]V. Gouaillier, L. Gagnon. Ship silhouette recognition using principal components analysis. SPIE, 1997,3164:59-69
[51]Steedly Wm. Moses RL. High resolution exponential modeling of fully polarized radar returns. IEEE Trans. on AES, 1991,27(5):459～469
[52]Jiang Yicheng. A HRR radar aircraff identification method based statistical feature. Proceeding of SPIE, 2001,4554:77～81
[53]Steven P. Jacobs. Automatic target recognition using sequences of high resolution radar range-profiles. IEEE Trans. on AES, 2000,36(2):364-381
[54]Keitth Copsey, Andres Webb. Bayesian Gamma mixture model approach to radar target recognition. IEEE Trans. on AES, 2003,39(4):1201～1216
[55]Kim K, Choi IS, Kim H. Efficient radar target identification using adaptive joint time-frequency processing. IEEE Trans. on AP, 2000,48(12):1789～1801
[56]Cheryl. Resch. Neural network for exo-atmospheric target discrimination. SPIE, 1998, 3371:119～128
[57]马骏声.NMD-GBR雷达的测量能力及其性能参数.航天电子对抗,2002,18(5):1～8
[58]Andrew M. S, John M. Cl, Bob Dietz, et al. Countermeasures-A technical evaluation of the operational effectiveness of the planned US national missile defense system. Union of Concerned Scienst, Cambridge MA, 2000
[59]David wright, Libeth Gronlund. Decoys and discrimination in tercept test IFT-8. Union of Concerned Scienst, Cambridge MA. 2002
[60]Libeth Gronlund, David wright, Stephen Young. An assessment of the intercept test program of the ground-based midcourse national missile defencse system. Union of Concerned Scienst, Cambridge MA, 2001
[61]J. Katechis, J. Caldwell. Exoatmospheric intercept-a gold mine for signature and impact data. ADA345814. Alabama, 1992
[62]United States General Accounting Office. Missile defense: review of result and limtitations of an early national defense flight test. ADA399464. Washinton. 2002
[63]United States General Accounting Office. Missile defense: review of result and limtitations of an early national defense flight test. ADA399465, Washington, 2002
[64]James L. Rasmussen, Randy L. Haupt, Michael L. Walker. RCS feature extraction from simple targets using time-frequency analysis, SPIE 2845
[65]M. A. Hussian. HRR, length and velocity decision regions for rapid target identification. SPIE conference, 1999,3810:40～52
[66]M. E. Clark. High range resolution techniques for ballistic missile targets. British Aerospace PIC, Cowes United Kingdom, 1999
[67]李永祯,王雪松等.GBR有源假目标极化鉴别能力分析.第九届全国雷达学术会议,烟台,2003
[68]William R. Chadwick. Augmentation of high altidude maneuver performance of a tail controlled missile using lateral thrust. ADA328973, Dahlgren, Va., 1997
[69]Bassem Mahafza, Stephen Welstead, Dale Champagne, et al. Real time radar signal simulation for the ground based radar for national missile defense. IEEE radar conference, New Ybrk, 1998
[70]D. Mehrholz. Radar oservation in low earth orbit. Adv. Space Res. 1997,19(2):203～212
[71]T. J. Settecerri. An introduction to processing orbital debris radar data. 38th Aerospace Sciences Meeting & Exhibit, 2000, AIAA-2000-0761:1～8
[72]Tour Sato. Shape estimation of space debris using single-range doppler interferometry. IEEE Trans. Geoscience and Remote Sensing, 1999,37(2):1000～1005
[73]Su May Hsu. Extracting target features from angle-angle and range-doppler Image. The Lincoln Laboratory Journal, 1993.6
[74]C C Chen, Harry C. Andrews. Target-motion-induced radar imaging. IEEE Trans. on AES, 1980,16(1):2～14
[75]S. Musman, D. Kerr, C. Bachmnn. Automatic recognition of ISAR ship images. IEEE Trans. on AES, 1996,32(4):1392～1403
[76]张剑云,张庆文.轨道飞行目标的雷达回波模拟及成像.电子学报,1995,23(9):28～31
[77]VC. Chen, W. J. Miceli. Simulation of ISAR imaging of moving target. IEE Proc. Radar Sonar and navigation, 2001,148(3):160～166
[78]V. C. Chen, W. J. Miceli. Time-varying spectral analysis for radar imaging of maneuvering targets. IEE Proc. Sonar and navigation, 1998,145(5):262～268
[79]863-308技术专家组.逆合成孔径雷达论文集.1996.03
[80]B. D. Steinberg. Radar imaging from a distorted array: the radip camera algorithm and experiment. IEEE Trans. on AP, 1981,29(5):740～748
[81]Zebker H A. Vanzyl J J. Imaging radar polarimetry: areview. Proceeding IEEE, 1991,79(1):1583～1606
[82]HaiQing Wu, Gilles Y. Dellsle. Precision Tracking Algorithms for ISAR Imaging. IEEE Trans. on AES, 1996,32(1):243～254
[83]Rao M Nuthalapati. High Resolution Reconstruction of ISAR Images. IEEE Trans. on AES, 1992,28(2):462～472
[84]D. A. Ausherman, A. Kozma, J. L. Walker, et a. Development in radar imaging. IEEE Trans. on AES 1984,20(4):363～399
[85]Xu X, Narayanan R M. Three～dimensional interferometric ISAR imaging for target scattering diagnosis and modeling. IEEE Trans. on image processing, 2001,10(7):1094～1102
[86]许小剑,黄培康.防空雷达中的目标识别技术.系统工程与电子技术, 1996,18(5):48～61
[87]王涛,王雪松.基于目标全极信息的弹头及诱饵识别方法研究.第九届全国雷达学术年会论文集,烟台,2003
[88]Brickel S. H. Some invariant properties of the polarization scattering matrix. Proc. IEEE, 1965,53(8):100～1072
[89]Lowenschuss O. Scattering matrix applications. Proc. IEEE, 1965,53(8):988～992
[90]Huynen J. R. Phenomenologycal theory of radar target, electromagnetic scattering. Academic Press, 1978
[91]Kostinski A. B., Boerner W. M. On foundaion of radar polarimetry. IEEE Trans. on AP, 1986,34(12):1395～1403
[92]Krogager E. New decoposition of the radar target scattering matrix. Electrics letters, 1990,26(18):1525～1527
[93]何松华.高距离分辨率毫米波雷达目标识别的理论与应用.博士学位论文,长沙:国防科技大学电子技术系,1993
[94]Smith C R, Goggans P M. Radar target identification. IEEE AP Magazine, 1993,35(2):27～38
[95]Xing M, Wu R, Lan J, et al. Migration through resolution cell compensation in ISAR imaging. IEEE GRS. Letters, 2004,1(2):141～144
[96]Giuli D. Polarixation diversity in radars. Proc. IEEE, 1986,74(2):245～269
[97]Arnold C. W. Improve ATR performance evaluation via mode seeking. Signal processing, sensor fusion, and target recognition IV Conference. Orlando, 1995
[98]KH. Rosenbach, J. Schiler. Non-coperative air target identification using radar identification rate as a function of signal bandwidth. 2000 international radar conference, 2000, Virginia.
[99]王国玉.基于雷达对抗的战区导弹突防仿真研究.博士学位论文,长沙:国防科学技术大学研究生院,1999
[100]阮颖铮.雷达截面与隐身技术.北京:国防工业出版社,1998
[101]彭望泽.防空导弹武器系统电子对抗技术.北京:宇航出版社,1999
[102]边肇祺,张学工.模式识别.北京:清华大学出版社,1999.9
[103]K. Fukunage. Introduction to statiscal pattern recognition. New York, Acadecmic Press. 1990
[104]Richard A. Citchell, John J. Westerkamp. Robust statistical feature based aircraft identification. IEEE Trans. on AES, 1999,35(3):1077～1094
[105]Yakov D. Shirman. Computer simulation of aerial target radar scattering, recognition, detection, and tracking. London: Artech House, 2002
[106]Jerome J. Sheehy. Optimum detection of signals in non-Gaussian noise. Journal acoustic society America, 1978,63(1):81～90
[107]Ron Mahler, Constantino Rago, Tim Zajic, et al. Joint tracking, pose estimation and identification using HRRR data. SPIE Proceedings, 2000,4052:195～206
[108]T. Zajic, C. Rago, R. Mahler. Joint tracking, pose estimation and target recognition using HRRR and track data: new result, SPIE Proceedings, 2001,4380:196～206
[109]B. D. Baker. TMR processing procddures for GSRS and attitude measurements. ADA074947,1979
[110]罗宏.动态雷达目标的建模与识别研究.博士学位论文.北京:航天科工集团公司第二研究院,2000.4
[111]Frank Hanson, Guy Beaghler. Discriminating interceptor technology program (DITP) laser radar. Conference on laser radar technology and application IV, Oriando, Florida, 1999
[112]刘永祥.导弹防御系统中的雷达目标综合识别研究.博士学位论文,长沙:国防科技大学研究生院,2004.3
[113]D. Giuli. Polarization diversity in radars. Proceedding of IEEE, 1986,74(2):245～269
[114]肖顺平.宽带极化雷达目标识别的理论与应用.博士学位论文,长沙,国防科技大学研究生院,1995.12
[115]Gene Greneker, David Asbell. Extraction of micro-doppler from vehicle targets of X-band frenquences. Proceeding of SPIE, 2001,4374:1～9
[116]V. C. Chen, F. Li, S. -S. Ho, et al. Analysis of micro-doppler signatures. IEE Proc. Radar Sonar Navig., 2003,150(4):271～276
[117]Burges C J. A tutorial on support vector machines for pattern recognition. Data Miming and Knowledeg Discovery, 1998,2(2):121～167
[118]Q. Zhao, J. C. Principle. Support vector machines for SAR automatic target recognition. IEEE Trans. on AES, 2001,37(2):643～654
[119]Tipping M E.. Sparse Bayesian learning and relvenance vector maching. Journal of Machine Learning Research, 2001(6):211～244
[120]P. Bharadwaj, P. Runkle, L. Carin, et al. Multiaspect classification of airborne targets via physics-based HMMs and maching pursuits. IEEE Trans. on AES, 2001,37(2):595～606
[121]J. G. Teti, W. A.Berger. A multifeature decision space approach to radar target identification. IEEE Trans on AES, 1996,32(1):480～487
[122]Richard T. Ivey, Allen M. Waxman. Learning-while-tracking, feature discovery and fusion of high-resolution radar range profile. ISIF, 2003, 741 -748
[123] Michael Boshra, Bir Bhanu. Predicting an upper bound on SAR ATR performance. IEEE Trans. on AES, 2001, 37(3): 876-888
[124] John A. Richards, Wallace J.Bow. An informative confidence metric for ATR. Proceeding of SPIE, 2003, 5095, 336-347
[125] David Blacknell. A comparison of SAR ATR performance with information theoretic predictions. Proceeding of SPIE, 2003, 5095, 385-395
[126] Scott D. Briles. Information-theoretic performance bounding of Bayesian identifiers. Proceeding of SPIE, 1993,1960,255-266
[127] Fred Garber, Ed Zelnio. On some simple estimates of ATR performance, and initial comparisons for a small data set. Proceeding of SPIE, 1997, 3070, 150-161
[128] Ed Zelnio, Fred Garber . A characterization of ATR performance evaluation. Proceeding of SPIE, 1996,2755,2-13
[129] Curtis E. Martin, Steven K. Rogers. Nonparametric Bayes error estimation for HRR identification. Proceedings of SPIE, 1994,2243:1-10
[130] Arnold Williams. A comparative study of the Gaussian form of the Bhattacharyya metric for ATR performance evaluation. SPIE, 1997, 3070, 258-266
[131] Joseph A. O'sullivan, Michael D. Devore. SAR ATR performance using a conditionally Gaussian model . IEEE Trans. on AES, 2001, 37(1):91-106
[132] F. D. Garber, A. Djouadi. Bounds on the Bayes classification error based on pairwise risk functions. IEEE Trans. on PAMI, 1988, 10(2): 281-288
[133] Luc Devroye, Laszlo Gyorfi. A probabilistic theory of recognition. New York, Springer, 1996
[134] Rob Williams, D. Gross.1D HRR data analysis and ATR assessment. Proceeding of SPIE, 1998,3020,54-59
[135] Kh. Rosenbach, J. Schiller. Non co-operative air target identification using radar imagery: identification rate as a function of signal bandwidth. IEEE international radar conference, Alexandria VA., 2000
[136] Duy H. Nguyen, John H. Kay, et al. Improving HRR ATR performance at low SNR by multi-look adaptive weighting. Proceeding of SPIE, 2001,4379, 216-228
[137] D. E. Dugeon. ATR performance modeling and estimation. Massachusetts: Lincoln laboratory, 1998
[138] Andrew P. Bradley. ROC curvesand x~2 test. Pattern Recognition Letters, 1996, (17), 287-294
[139] C. Brian Bassham. Automatic target recognition classification system evaluation methology. Doctor dissertation, Ohio, Air ForceInstitute of Technology, 2002
[140]Robert C. Holte. Very simple classification rulesperform well on most commonly used data sets. Machine Learning, 1993,11(1):63～91
[141]Anil K. Jain, Richard C. Dubes, Chaur-Chin Chen. Boostrap techniques for error estimation. IEEE Trans on PAMI, 1998,20(5):628～633
[142]David H. Wolpert. The mathematics of generalization. MA: Addison-Wesley, Reading, 1995
[143]Satoshi Watanabe. Pattern recognition: human and mechanical. New York, Wiley, 1985
[144]Richard O. Duba, Peter E. Hart, David G. Stork. Pattern classification (second edition). New York: John Wiley & Sons Inc., 2003
[145]Xing M D, Zheng B. The properties of range profile of aircraft. IEEE International Radar Conference, 2001,1050～1054.
[146]Li H J, Wang Y D. Matching score properties between range profiles of high-resolution radar targets. IEEE Trans. On AP, 1996,44(4):444～452
[147]章仁为.静止卫星的轨道和姿态控制.北京:科学出版社,1987
[148]张守信.外弹道测量与卫星轨道测量基础.北京:国防工业出版社,1992.10
[149]冯德军,陶华敏,刘中,等.对宽带高分辨雷达的转发式干扰方法.现代雷达,2005,27(11):19～23
[150]陈建文,倪晋麟.弹道导弹的雷达目标识别技术.第八届全国雷达学术年会论文集,合肥,2002
[151]M. E. Clark. High range resolution techniques for ballistic missile targets. British Aerospace PIC, 1999,6.1～6.13
[152]J. A. Richard, W. J. Bow, B. k. Bray. An informative confidence metric for ATR. SPIE Proceeding, 5095,336～348
[153]刘宏伟,保铮.基于复合特征及分层特征选择的雷达HRRP识别.系统工程与电子技术,2005,27(4):596～599
[154]R. Lambour, T. Morgan, etc. Assessment orbital debris size estimation from radar cross section measurements. 2001 Core technologies for space systems Conf, 2001
[155]庄钊文,肖顺平,王雪松.雷达极化信息处理及应用.北京:国防工业出版社,1999
[156]刘永祥,黎湘,庄钊文.空间目标进动特性及其在雷达目标识别中的应用.自然科学进展,2004,11(11):1329～1332
[157]金文彬,刘永祥,任双桥,等.锥体目标空间进动特性分析及其参数提取.宇 航学报,2004,25(4):408～410
[158]闫锦,黄培康.雷达目标识别中的维数压缩.系统工程与电子技术,2001,21(12):35～38
[159]李跃华,沈庆宏,等.小波神经网络的毫米波雷达目标一维像识别.南京理工大学学报,2002,26(1):20～23
[160]了解国家导弹防御技术.863先进防御技术通讯(A类),2000(10):1～42
[161]陈建文,李士国.基于目标运动分辨技术提取导弹目标运动特征.现代雷达,2003,25(6):5～7
[162]章仁为.卫星姿态动力学与控制.北京:北京航空航天大学出版社,1997.4
[163]张居凤,冯德军,等.雷达目标动态RCS仿真研究.系统仿真学报,2005,17(4):834～837
[164]杨行峻,迟惠生.语音信号数字处理.北京:电子工业出版社,1995
[165]M. Ross, H. Shaffer, et al. Average magnitude difference function pitch extractor. IEEE Trans. on ASSP, 1974,22(5):353～362
[166]Wolfgang Hess. Pitch determination of speech signals. New York: Springer-Verlag, 1983
[167]顾良,刘润生.高性能汉语语音基音周期估计.电子学报,1999,27(1):8～11
[168]张文耀,许刚,王裕国.循环AMDF及其语音周期估计算法.2003,31(6):886～890
[169]Caputi W J. Stretch: a time-transformation technique. IEEE Trans on AES, 1971,7(2):269～278.
[170]曹志道,刘永坦,许荣庆.实验ISAR若干参数的选择和实践.逆合成孔径雷达论文集,1996,20～26
[171]鹿国春,黄槐,王金奎,沈德泉.实验ISAR总体设计和实验.逆合成孔径雷达论文集,1996,27～46
[172]B. M. Welsh, R. W. Hawley. Linear FM radar effects on moving target signatures. Proceeding of SPIE, 2001,4382:161～173
[173]丁鹭飞,耿富录.雷达原理. 西安:西安电子科技大学出版社,1984
[174]Wood John C. Barry Daniel T. Radon Transformation of time-frequency distribution for analysis of multi-component signals. IEEE Trans. on Signal Processing, 1994,42(11):3166～3177.
[175]Wang Minsheng, Chan Andrew K, Chui Charles K. Linear frequency-modulated signal detection using radon-ambiguity transformation. IEEE Trans. Signal Processing, 1998,46(3):571～586.
[176]Abatzoglou T. J. Fast maximum likelihood joint estimation of frequency and frequency rate. IEEE Trans.on AES, 1986,22(6):708～715.
[177]刘渝.快速解线性调频技术.数据采集与处理.1999.6,14(2):175～178
[178]Peleg S, Porat B. Linear FM signal parameter estimation from discrete-time observations. IEEE Trans. on AES, 1991,27(4):607～615.
[179]冯德军,王雪松,削顺平,王国玉.基于单个宽带脉沖的至间目标测距和测速方法.信号处理,2006,22(1):73～77.
[180]张贤达.现代信号处理.北京:清华大学出版社,2002
[181]David Iny, Martin Monoi. Quanntative analysis of HRR NCTR performance drivers. SPIE, 1996,2747:144～152
[182]M. A. Hussain. HRR, length and velocity decision regions for rapid target identification. SPIE. 1999.3810:40～52
[183]孙文峰.宽带毫米波雷达精确制导信息处理方法研究.博士学位论文,长沙,国防科技大学研究生院,1998.07
[184]JianGuo Wang, Peikun He. Use of the radial velocity measurement in target tracking. IEEE Trans. on AES, 2003,39(2):401～413
[185]黄小红.目标高速运动对一维距离像的影响及补偿方法研究.信号处理,2001,18(6):487～490
[186]张毅.弹道导弹弹道学.长沙:国防科技大学出版社,1999
[187]Merrill I. Skolnik. Radar Handdbook, Second edition. McGraw-Hill Companies. Inc. 1990
[188]冯德军,王雪松,肖顺平,王国玉.弹道目标中段雷达成像仿真研究.系统仿真学报,2004,16(11):2511～2516
[189]H T Wu, J F Yang, F K Chen. Source number estimators using transformed Gerchgorin disks. Proc ICASSP, Adelaide: IEEE, 1994,261～264.
[190]H T Wu, J F Yang, F K Chen. Source number estimators using transformed Gerchgorin radii. IEEE Trans on SP, 1995,43(6):1325～1333
[191]H T Wu, C L Chen. A new Gerschgorin radii based method for source number detection. Proc 10th IEEE Workshop on Statistical Signal and Array Processing, Pennsylvania, IEEE Conf, 2000,104～107
[192]姜卫东,庄钊文,陈曾平.基于一维距离像的目标识别方法.现代雷达,1992,21(1):19～22
[193]E. J. Rothwell, D. P. Nyquist, K. M Chen. A radar target discrimination scheme using the discrete wavelet transform for reduced data storage. IEEE Trans. AP, 1994,42(7):1033～1037
[194]Hurst M P, Mittra R. Scattering center analysis via Prony'S method. IEEE Trans on AP, 1987,35(8):986～988
[195]Zyewck A., Bogner. Rader target classification of commercial aircraft. IEEE Trans. on AES, 1996,32(2):598～606
[196]Ricard A. Mitchell, John J. Westerkamp. Statistical feature based HRR radar classification. RTO SCI Symposium on non-cooperative air target identification using radar, Mannheim, Germany, 998
[197]Andrew R. Webb. Statistical pattern recognition. London, John Wiley & Sons, 2002
[198]Andrew R. Webb. Gamma mixture models for target recognition. RTO SCI Symposium on non-cooperative air target identification using radar, Mannheim, Germany, 998
[199]R. Heiden, F. Groen. The BOX-COX metric for nerest neighbor classification improvement. Pattern Recognition, 1997,30(2):273～279
[200]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.第九届全国雷达学术会议论文集,烟台,2004
[201]David Middleton. Statistical-Physical models of electromagnetic interference. IEEE Trans. on Electromagnet, 1977,19(3):106～126
[202]Leslile A. Berry. Understanding Middleton's canonical formula for Class A noise. IEEE Trans. on Electromagnet, 1981,23(4):337～344
[203]S. M. Zabin and H. V. Poor. Parameter estimation for Middleton Class A interference processes. IEEE Trans. on Communication, 1989,37(10):1042～1051
[204]Serena M. Zabin and H. Vincent Poor. Efficient estimation of Class A noise parameters via the EM algorithm. 1991, IEEE Trans. on Inform. Theory, 137(1):60～72
[205]Serena M. Zabin and H. Vincent Poor. Recursive algorithms for identification of impulsive noise channels. IEEE Trans. on Inform. Theory, 1990,36(3):559～578
[206]B. D. Steinberg. Microwave imaging with large antenna arrays: radio camera principles and techniques. New York: John wiley and Sons, 1983
[207]沈永欢,梁在中,等编.实用数学手册.北京:科学出版社,1992
[208]刘永坦.雷达成像技术,哈尔滨:哈尔滨工业大学出版社,1999
[209]保铮,邢孟道,王彤.雷达成像技术.北京:电子工业出版社,2005
[210]D. E. Wahl, P. H. Eichel, D. C. Chiglia. Phase gradient autofocus robust tool for high resolution SAR phase correction. IEEE Trans on AES, 1994,30(3):827～834
[211]Z. D Zhu, Z. R. Ye, X. Q. Wu. Super-resolution range-doppler imaging. IEE Proc. -F, 1995,142(1):25～32
[212]HaiQing Wu, Gilles Y. Dellsle. Precision tracking algorithms for ISAR imaging. IEEE Trans. on AES, 1996,32(1):243～254
[213]Victor C. C, Shie Qian. Joint time-frequency transform for radar range-doppler imaging. IEEE Trans. on AES, 1998,34(2):486～499
[214]Jian Li. Renbiao, Wu. Robust autofocus algorithm for ISAR imaging of moving targets. IEEE Trans. on AES, 2001,37(3):1056～1068
[215]王洋,陈建文,刘中.导弹目标ISAR成像仿真分析.现代雷达,2003,25(10):18～21
[216]Byron M. Welsh, Robet W. Hawley. Linear FM radar effects on moving target signatures. Proceedings of SPIE, 2001,4382:164～173
[217]Lee A. Centrohermitian and skew-centrohermitiam matrices. Linear Algebra and its applications, 1980,29:205～210
[218]Martin Haardt, Josef A. Nossek. Unitary ESPRIT: How to obtain increased estimation accuracy with a reduced computational burden. IEEE Trans. on signal processing, 1995,43(5):1232～1242
[219]王国林.逆合成孔径雷达运动补偿和系统补偿研究.博士学位论文,哈尔滨,哈尔滨工业大学,1996.1
[220]李金宗.模式识别导论.北京:高等教育出版社,1994
[221]Breimin L. Stacked regressions. Machine Learing, 1996,24:49～64
[222]C. T. Zahn, R. Z. Roskies. Fourier decripotors for plane closed curves. IEEE Trans. on Computer, 1972, C-21:269-281
[223]E. Persoon, K. S. Fu. Shape discrimination using Fourier descriptors. IEEE Trans. Systerms. Man and Cybernetics, 1977, SMC-7:170～179
[224]D. R. Wehner. High resolution radar. London, Artech House, 1987
[225]刘延柱.空间飞行器姿态控动力学.北京:国防工业出版社,1995
[226]Thompson W T. Introduction to space dynamics. New York, Wiley, 1961
[227]屠善澄.卫星姿态动力学与控制.北京:宇航出版社,2001
[228]Zhu Z D, et al. Super-resolution range-doppler imaging. IEE Proceeding—-F, 1995,142(1):25～32
[229]R M Nuthalapati. High resolution reconstruction of ISAR images. IEEE Trans. on AES, 1992,28(2):462～472
[230]Odendall J W, Barnard E, et al. Two-dimensional super resolution radar imaging using the MUSIC algorithm.IEEE Trans. on AP 1994,42(10):1386～1391.
[231]朱兆达,叶蓁如,邬小青.一种超分辨距离多普勒成像方法.电子学报,1992,20(7):1～6
[232]Zhu Z D, Ye Z R, Wu X Q. An approach to superresolution range-doppler imaging. Acta Electronica Sinica, 1992,20(7):1～6.(Chinese Source).
[233]吴强,王国林,许荣庆.ESPRIT超分辨ISAR成像.系统工程与电子技术,1999,21(7):33～36
[234]Kim K, Choi IS, Kim H. Efficient radar target identification using adaptive joint time-frequency processing. IEEE Trans. on AP, 2000,48(12):1789～1801
[235]Ueda N. Optimallinear combination of neural networks for improving classification peromance. IEEE Trans. on PAMI, 2000,22(2):207～215
[236]Woods K. Combination of multiple classifiers using local accuracy estimates. IEEE Trans. on PAMI, 1997,19(4):405～410
[237]Ruggieri S. Efficeent C4.5. IEEE Trans. on Knowledeg and Data Engineering. 2002,24(2):438～444
[238]Breiman L., Friedman J. H., Olshen R. A., et al. Classfication and regression trees. New Ybrk, Chapmen & Hall, 1984.
[239]Shafer J, Aggrawal R, Mehta M. SPRINT: a scalable parallel classifier for data mining. IBM Almaden Research Center, San Jose, Calofomia, 1996
[240]Quinlan J R. Introduction decision trees. Machine Learing, 1986,1(1):81～1-6
[241]Hand D. J. Construction and assessment of classification rules. Chichester: Wiley, 1997
[242]McLachlan, G. J. Discriminant analysis and statistical pattern recognition. New York, Wiley, 1992
[243]Xiaofan Lin, Xiaoqing Ding, Ming Chen, et al. Adaptive confidence transform based classifier combination for Chinese character recognition. Pattern Recognition Letters, 1998,19(10):975～988
[244]Bouchaffra D. A methodology for mapping scores to probabilities. IEEE Trans. on PAMI, 1999,21(9):923～927
[245]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
[246]Liu C L. Nakagawa M. Precise candidate selection for large character set recognition by confidence evaluation. IEEE Trans. on PAMI, 2000,22(6):636～642
[247]Gondail F, Lange E, Iwamoto T, et al. Face recognition system using local autocorrelations and multiscale integration. IEEE Trans. on PAMI, 1996,18(10):1024～1028
[248]Smith S J. Handwritten character classification using nearest ngighbor in large dataset. IEEE Trans. on PAMI, 1994,16(9):915～919
[249]J. A. Hanley, B. J. McNeil. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 1982,143:29～36
[250]Andrew P. Bradley. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 1997,30(7):1145～1159
[251]David J. Hand, Robert J. Till. A simple generalization of the area under the ROC curve for multiple class classification problems. Machine learning, 2001,45:171～186
[252]Niall M. Adams, David J. Hand. An improved meaure for comparing diagnostic tests. Computers in Biology and Medcine, 2000,30:89～96
[253]Duin, R. P. W. A note on comparing classifiers. Pattern Recognition Letters, 17:529～536
[254]Dieterich, T. G. Approximate statistical tests for comparing supervised classification learning algorithms. Netural Computation, 1998,10:1895～1923
[255]Adams N M., Hand D. J. Comparing classifiers when the misallocation costs are uncertain. Pattern Recognition, 1999,32:1139～1147
[256]Provost F., Fawcett T. Robust classification for imprecise environments. Machine Lerning, 2001,42:203～231
[257]Kittler J., Hatef M., Duin R. P. W., et al. On combining classifiers. IEEE Trans. on PAMI, 1998,20(3):226～239
[258]Wolpert D. H. Stacked generalization. Neural Networks, 1992,5(2):241～260
[259]Jacobs, R. A., Jordan M. I., Nowlan S. J., et al. Adaptive mixtures of local experts. Neural Computation. 1991,3:79～87
[260]郭桂蓉.模糊模式识别.长沙:国防科技大学出版社,1992
[261]Carrara W G, et al. Majewski R. M. Spotlight synthetic aperture radar: signal processing algorithms. London, Artech House, 1995
[262]黄培康.雷达目标特征信号.北京:宇航出版社,1993
[263]王国雄.弹头技术.北京:宇航出版社,1993
[264]王保义,时振栋.电磁场在目标识别中的应用.北京:电子工业出版社,1995
[265]戴征坚.空间目标的雷达特性预估与识别.博士学位论文,长沙,国防科技大学研究生院,2000
[266]B. Kang. Emproved adaptive-beamforming targets for self-caibrating a distorteed phased array. IEEE Trans. on AP, 1990,38(2):186～194
[267]Min-Chin Lin, Yean-Woei Klang. Performance analysis of a radar target discrimination technique. IEEE Trans. on AES, 1991,28(2):363～368
[268]Isaac Backman.Model of laser radar Signatures of ballistic missile warhead. SPIE, 1999,3699:133-137
[269]Bao Z, Sun C Y, Xing M D. Time-frequency approaches to ISAR imaging of maneuvering targets and their limitations. IEEE Trans. on AES, 2001,37(3):1091～1099
[270]Paul F. Hemler. Radar imaging understanding for complex space object. SPIE, 1381,55～65
[271]Cheryl L. Resch. Neural networks for exo-atmospheric target discrimination. SPIE, 1998,3371:119～128
[272]Olivier D. M., Robert H. Bishop. Tracking and identification of a maneuvering reentry vehicle. AIAA 2003.5446,1993,1～11
[273]Xiao Zhang, J. Zhang, G. G. Walter. Shape space object recognition. SPIE, 2001,4379:432～443
[274]宋枫溪,高林.文本分类器性能评估指标.计算机工程,2004,30(3):107～109
[275]K. Schultz, S. Davidson, A. Stein, et al. Range doppler laser radar for midcourse discrimination: the firefly experiments. AIAA, 1994.2653,1～12
[2]V G Nebabin, Methods and techniques of radar recognition. Boston, London, Artech House, 1995
[3]黄培康.反导系统中的目标识别技术.战略防御,1981(5):1～14
[4]郭桂蓉,庄钊文,陈曾平.电磁特征抽取与目标识别.长沙:国防科技大学出版社,1995
[5]王树生,李学焦,吴常信,等.前苏联反导系统的技术发展历程.飞航导弹,2001,31(12):34～42
[6]杨华,陈昌明,高桂清,等.美国反导系统的发展历程和技术现状.飞航导弹,2001,31(7):11～15
[7]David R, Tanks. National missile defense: policy issues and technological capabilities. Washington, SvecConway Priting Inc, 2000
[8]蒋艳清.TMD战略对东亚及我国安全的影响.硕士学位论文,武汉:华中师范大学,2002.03
[9]甘斯勒.美国国家导弹防御计划与拦截飞行试验.863先进防御技术(A类),2000(5):13～33
[10]马骏声.目标识别与GBR地基成像雷达.航天电子对抗,1996,12(4):32～35
[11]史别.识别真假目标是NMD的最大技术难题.863先进防御技术通讯(A类),2001(8):30～32
[12]863先进防御技术通讯(A类)编辑部.美国国防部声称NMD能够识别真假目标.863先进防御技术通讯简讯,2000(14):1～2
[13]薛雷达译.美国国家导弹防御系统地基雷达测量与识别能力.863先进防御技术通讯(A类),2000(6):1～17
[14]刘延柱.陀螺力学.北京:科学出版社,1986
[15]D. J Fouts, P. E. Pace, C. Karow, et al. A single-chip false target radarimage generator for countering wideband image radar. IEEE Journal of Solid State Circuits, 2001,37(6):751～758
[16]P. E. Pace, D. J. Fouts, S. Ekestrm, et al. Digital false-target image synthesizer for countering ISAR. IEE Proceedings-Radar, Sonar and Navigation, 2002,149(5):248～257
[17]Femando A.L. D. Performance analysis of digital image synthesizer as a counter-measure against inverse svntheticradar. Master's Thesis, Naval Postgraduate School, Monterey CA, 2002
[18]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
[19]黄培康,闫锦.弹道导弹(BM)对抗中的识别与反识别技术.航天电子对抗,2005,21(3):1～3
[20]裴云天.红外技术在美国国家导弹防御计划中的应用.红外,2002(1):1～6.
[21]I J.凯斯(美).光探测器与红外探测器.北京:科学出版社,1984
[22]徐南荣,卞南华.红外幅射与制导.北京:国防工业出版社,1997.
[23]王军.弹道目标空间反识别措施发展态势浅析.系统工程与电子技术,2002,24(7):53～57
[24]杨华,凌永顺,陈昌明,等.美国反导系统红外探测、跟踪和识别技术分析.红外技术,2001,23(4):1～3
[25]李群章.弹道导弹弹道中段和再入段弹头红外光学识别方法研究.红外与激光工程,1999,28(5):1～5
[26]赵锋伟,沈振康,李吉成.红外诱饵辨识的仿真研究(一)灰度时间序列分析.红外与激光工程,2002,31(4):286～289
[27]赵锋伟,沈振康,李吉成.红外诱饵辨识的仿真研究(一)运动特征分析.红外与激光工程,2002,31(5):395～398
[28]J. D. Mill, B. D. Guilmain. The MSX mission objectives. John Hopkins APL technical digest, 1996,17(1):4～10
[29]王伟,张汉华,姜卫东,等.低分辨雷达的目标特征提取方法.国防科技大学学报,2002,24(2):31～35
[30]Van Blaricum, Mittra R. A technique for extracting the poles and residues of a system directly from its transient response. IEEE Trans. on AES, 1975,23(6):1046～1051
[31]Bennett C. L. Toomey J. P. Target classification with multiple frenquency illumination. IEEE Trans. on AP, 1981,29(2):352～358
[32]Kennaugh E. M. The K-pulse concept. IEEE Trans. on AP, 1981,29(2):327～331
[33]Chen K M. Radar wave synthesis method-a new radar detection schem. IEEE Trans. on AP, 1981,29(4):553～565
[34]Keller, J. B. Geometrical theory of diffraction. J. Opt. Soc. Am. 1962,52(2):116～118
[35]Borden B. High frequency statistical classification of complex targets using severely aspectlimited data. IEEE Trans. on AP, 1986,34(12):1455～1459
[36]Mr. Thomas Foster. Application of pattern recognition techniques for early warning radar (EWR) discrimination. ADA298895,27 Jan 1995
[37]Peter Swerling. Radar probability of detection for additional fluctuaing target case. IEEE Trans. on AES, 1997,33(2):698～709
[38]Xu Xiaojian, Huang Peikang. A new RCS statiscal model of radar target. IEEE Trans. on AES, 1997,33(2):710～714
[39]许小剑,黄培康.利用RCS幅度信息进行雷达目标识别.系统工程与电子技术.1992,14(6):1～9
[40]NanZhi Jiang, Jian Li. Super resolution feature extraction of moving targets. IEEE Tans. on AES, 2001,37(3):781～792
[41]Zhaoqiang Bi, Rebao Wu, Jian Li. Simultaneous HRR feature extraction and doppler shift estimation of moving target with rigid bodies. SPIE Conference, 1999,3721:425～435
[42]Li H, Yang S H. Using rangeprofiles as feature vectors to identify aerospace objects. IEEE Trans. on AP. 1993,41(3):261～268
[43]Hudson S, Psaltis D. Correlation filters for aircraft identification from radar range profiles. IEEE Trans. on AES, 1993,29(3):741～748
[44]E. J. Rothwell, D. P. Nyquist, K. M Chen. A radar target discrimination scheme using the discrete wavelet transform for reduced data storage. IEEE Trans. on AP, 1994,42(7):1033～1037
[45]Yu Shi, Xianda Zhang. Local bispectra based high-resolution radar classification. IEEE Proceedings of ICSE, 2000,1968～1971
[46]K. Wong. Non-cooperative target recognition in the frequency domain. IEE Proc-Radar, Sonar and Navigation, 2004,151(2):77～84
[47]Junshui Ma, Stanley Ahalt. Analysis of complex HRR range signature. Proceeding of SPIE, 2000,4029:130～135
[48]Zwike Pe, Kiss I. A new implementation of the Mellin transforma and its application to radar classification of ships. IEEE Trans. on PAMI, 1989,5(2):191～199
[49]Hu R, Zhu Z D. Researches on radar target classification based on high resolution range profiles. IEEE Conf., 1997:951～955
[50]V. Gouaillier, L. Gagnon. Ship silhouette recognition using principal components analysis. SPIE, 1997,3164:59-69
[51]Steedly Wm. Moses RL. High resolution exponential modeling of fully polarized radar returns. IEEE Trans. on AES, 1991,27(5):459～469
[52]Jiang Yicheng. A HRR radar aircraff identification method based statistical feature. Proceeding of SPIE, 2001,4554:77～81
[53]Steven P. Jacobs. Automatic target recognition using sequences of high resolution radar range-profiles. IEEE Trans. on AES, 2000,36(2):364-381
[54]Keitth Copsey, Andres Webb. Bayesian Gamma mixture model approach to radar target recognition. IEEE Trans. on AES, 2003,39(4):1201～1216
[55]Kim K, Choi IS, Kim H. Efficient radar target identification using adaptive joint time-frequency processing. IEEE Trans. on AP, 2000,48(12):1789～1801
[56]Cheryl. Resch. Neural network for exo-atmospheric target discrimination. SPIE, 1998, 3371:119～128
[57]马骏声.NMD-GBR雷达的测量能力及其性能参数.航天电子对抗,2002,18(5):1～8
[58]Andrew M. S, John M. Cl, Bob Dietz, et al. Countermeasures-A technical evaluation of the operational effectiveness of the planned US national missile defense system. Union of Concerned Scienst, Cambridge MA, 2000
[59]David wright, Libeth Gronlund. Decoys and discrimination in tercept test IFT-8. Union of Concerned Scienst, Cambridge MA. 2002
[60]Libeth Gronlund, David wright, Stephen Young. An assessment of the intercept test program of the ground-based midcourse national missile defencse system. Union of Concerned Scienst, Cambridge MA, 2001
[61]J. Katechis, J. Caldwell. Exoatmospheric intercept-a gold mine for signature and impact data. ADA345814. Alabama, 1992
[62]United States General Accounting Office. Missile defense: review of result and limtitations of an early national defense flight test. ADA399464. Washinton. 2002
[63]United States General Accounting Office. Missile defense: review of result and limtitations of an early national defense flight test. ADA399465, Washington, 2002
[64]James L. Rasmussen, Randy L. Haupt, Michael L. Walker. RCS feature extraction from simple targets using time-frequency analysis, SPIE 2845
[65]M. A. Hussian. HRR, length and velocity decision regions for rapid target identification. SPIE conference, 1999,3810:40～52
[66]M. E. Clark. High range resolution techniques for ballistic missile targets. British Aerospace PIC, Cowes United Kingdom, 1999
[67]李永祯,王雪松等.GBR有源假目标极化鉴别能力分析.第九届全国雷达学术会议,烟台,2003
[68]William R. Chadwick. Augmentation of high altidude maneuver performance of a tail controlled missile using lateral thrust. ADA328973, Dahlgren, Va., 1997
[69]Bassem Mahafza, Stephen Welstead, Dale Champagne, et al. Real time radar signal simulation for the ground based radar for national missile defense. IEEE radar conference, New Ybrk, 1998
[70]D. Mehrholz. Radar oservation in low earth orbit. Adv. Space Res. 1997,19(2):203～212
[71]T. J. Settecerri. An introduction to processing orbital debris radar data. 38th Aerospace Sciences Meeting & Exhibit, 2000, AIAA-2000-0761:1～8
[72]Tour Sato. Shape estimation of space debris using single-range doppler interferometry. IEEE Trans. Geoscience and Remote Sensing, 1999,37(2):1000～1005
[73]Su May Hsu. Extracting target features from angle-angle and range-doppler Image. The Lincoln Laboratory Journal, 1993.6
[74]C C Chen, Harry C. Andrews. Target-motion-induced radar imaging. IEEE Trans. on AES, 1980,16(1):2～14
[75]S. Musman, D. Kerr, C. Bachmnn. Automatic recognition of ISAR ship images. IEEE Trans. on AES, 1996,32(4):1392～1403
[76]张剑云,张庆文.轨道飞行目标的雷达回波模拟及成像.电子学报,1995,23(9):28～31
[77]VC. Chen, W. J. Miceli. Simulation of ISAR imaging of moving target. IEE Proc. Radar Sonar and navigation, 2001,148(3):160～166
[78]V. C. Chen, W. J. Miceli. Time-varying spectral analysis for radar imaging of maneuvering targets. IEE Proc. Sonar and navigation, 1998,145(5):262～268
[79]863-308技术专家组.逆合成孔径雷达论文集.1996.03
[80]B. D. Steinberg. Radar imaging from a distorted array: the radip camera algorithm and experiment. IEEE Trans. on AP, 1981,29(5):740～748
[81]Zebker H A. Vanzyl J J. Imaging radar polarimetry: areview. Proceeding IEEE, 1991,79(1):1583～1606
[82]HaiQing Wu, Gilles Y. Dellsle. Precision Tracking Algorithms for ISAR Imaging. IEEE Trans. on AES, 1996,32(1):243～254
[83]Rao M Nuthalapati. High Resolution Reconstruction of ISAR Images. IEEE Trans. on AES, 1992,28(2):462～472
[84]D. A. Ausherman, A. Kozma, J. L. Walker, et a. Development in radar imaging. IEEE Trans. on AES 1984,20(4):363～399
[85]Xu X, Narayanan R M. Three～dimensional interferometric ISAR imaging for target scattering diagnosis and modeling. IEEE Trans. on image processing, 2001,10(7):1094～1102
[86]许小剑,黄培康.防空雷达中的目标识别技术.系统工程与电子技术, 1996,18(5):48～61
[87]王涛,王雪松.基于目标全极信息的弹头及诱饵识别方法研究.第九届全国雷达学术年会论文集,烟台,2003
[88]Brickel S. H. Some invariant properties of the polarization scattering matrix. Proc. IEEE, 1965,53(8):100～1072
[89]Lowenschuss O. Scattering matrix applications. Proc. IEEE, 1965,53(8):988～992
[90]Huynen J. R. Phenomenologycal theory of radar target, electromagnetic scattering. Academic Press, 1978
[91]Kostinski A. B., Boerner W. M. On foundaion of radar polarimetry. IEEE Trans. on AP, 1986,34(12):1395～1403
[92]Krogager E. New decoposition of the radar target scattering matrix. Electrics letters, 1990,26(18):1525～1527
[93]何松华.高距离分辨率毫米波雷达目标识别的理论与应用.博士学位论文,长沙:国防科技大学电子技术系,1993
[94]Smith C R, Goggans P M. Radar target identification. IEEE AP Magazine, 1993,35(2):27～38
[95]Xing M, Wu R, Lan J, et al. Migration through resolution cell compensation in ISAR imaging. IEEE GRS. Letters, 2004,1(2):141～144
[96]Giuli D. Polarixation diversity in radars. Proc. IEEE, 1986,74(2):245～269
[97]Arnold C. W. Improve ATR performance evaluation via mode seeking. Signal processing, sensor fusion, and target recognition IV Conference. Orlando, 1995
[98]KH. Rosenbach, J. Schiler. Non-coperative air target identification using radar identification rate as a function of signal bandwidth. 2000 international radar conference, 2000, Virginia.
[99]王国玉.基于雷达对抗的战区导弹突防仿真研究.博士学位论文,长沙:国防科学技术大学研究生院,1999
[100]阮颖铮.雷达截面与隐身技术.北京:国防工业出版社,1998
[101]彭望泽.防空导弹武器系统电子对抗技术.北京:宇航出版社,1999
[102]边肇祺,张学工.模式识别.北京:清华大学出版社,1999.9
[103]K. Fukunage. Introduction to statiscal pattern recognition. New York, Acadecmic Press. 1990
[104]Richard A. Citchell, John J. Westerkamp. Robust statistical feature based aircraft identification. IEEE Trans. on AES, 1999,35(3):1077～1094
[105]Yakov D. Shirman. Computer simulation of aerial target radar scattering, recognition, detection, and tracking. London: Artech House, 2002
[106]Jerome J. Sheehy. Optimum detection of signals in non-Gaussian noise. Journal acoustic society America, 1978,63(1):81～90
[107]Ron Mahler, Constantino Rago, Tim Zajic, et al. Joint tracking, pose estimation and identification using HRRR data. SPIE Proceedings, 2000,4052:195～206
[108]T. Zajic, C. Rago, R. Mahler. Joint tracking, pose estimation and target recognition using HRRR and track data: new result, SPIE Proceedings, 2001,4380:196～206
[109]B. D. Baker. TMR processing procddures for GSRS and attitude measurements. ADA074947,1979
[110]罗宏.动态雷达目标的建模与识别研究.博士学位论文.北京:航天科工集团公司第二研究院,2000.4
[111]Frank Hanson, Guy Beaghler. Discriminating interceptor technology program (DITP) laser radar. Conference on laser radar technology and application IV, Oriando, Florida, 1999
[112]刘永祥.导弹防御系统中的雷达目标综合识别研究.博士学位论文,长沙:国防科技大学研究生院,2004.3
[113]D. Giuli. Polarization diversity in radars. Proceedding of IEEE, 1986,74(2):245～269
[114]肖顺平.宽带极化雷达目标识别的理论与应用.博士学位论文,长沙,国防科技大学研究生院,1995.12
[115]Gene Greneker, David Asbell. Extraction of micro-doppler from vehicle targets of X-band frenquences. Proceeding of SPIE, 2001,4374:1～9
[116]V. C. Chen, F. Li, S. -S. Ho, et al. Analysis of micro-doppler signatures. IEE Proc. Radar Sonar Navig., 2003,150(4):271～276
[117]Burges C J. A tutorial on support vector machines for pattern recognition. Data Miming and Knowledeg Discovery, 1998,2(2):121～167
[118]Q. Zhao, J. C. Principle. Support vector machines for SAR automatic target recognition. IEEE Trans. on AES, 2001,37(2):643～654
[119]Tipping M E.. Sparse Bayesian learning and relvenance vector maching. Journal of Machine Learning Research, 2001(6):211～244
[120]P. Bharadwaj, P. Runkle, L. Carin, et al. Multiaspect classification of airborne targets via physics-based HMMs and maching pursuits. IEEE Trans. on AES, 2001,37(2):595～606
[121]J. G. Teti, W. A.Berger. A multifeature decision space approach to radar target identification. IEEE Trans on AES, 1996,32(1):480～487
[122]Richard T. Ivey, Allen M. Waxman. Learning-while-tracking, feature discovery and fusion of high-resolution radar range profile. ISIF, 2003, 741 -748
[123] Michael Boshra, Bir Bhanu. Predicting an upper bound on SAR ATR performance. IEEE Trans. on AES, 2001, 37(3): 876-888
[124] John A. Richards, Wallace J.Bow. An informative confidence metric for ATR. Proceeding of SPIE, 2003, 5095, 336-347
[125] David Blacknell. A comparison of SAR ATR performance with information theoretic predictions. Proceeding of SPIE, 2003, 5095, 385-395
[126] Scott D. Briles. Information-theoretic performance bounding of Bayesian identifiers. Proceeding of SPIE, 1993,1960,255-266
[127] Fred Garber, Ed Zelnio. On some simple estimates of ATR performance, and initial comparisons for a small data set. Proceeding of SPIE, 1997, 3070, 150-161
[128] Ed Zelnio, Fred Garber . A characterization of ATR performance evaluation. Proceeding of SPIE, 1996,2755,2-13
[129] Curtis E. Martin, Steven K. Rogers. Nonparametric Bayes error estimation for HRR identification. Proceedings of SPIE, 1994,2243:1-10
[130] Arnold Williams. A comparative study of the Gaussian form of the Bhattacharyya metric for ATR performance evaluation. SPIE, 1997, 3070, 258-266
[131] Joseph A. O'sullivan, Michael D. Devore. SAR ATR performance using a conditionally Gaussian model . IEEE Trans. on AES, 2001, 37(1):91-106
[132] F. D. Garber, A. Djouadi. Bounds on the Bayes classification error based on pairwise risk functions. IEEE Trans. on PAMI, 1988, 10(2): 281-288
[133] Luc Devroye, Laszlo Gyorfi. A probabilistic theory of recognition. New York, Springer, 1996
[134] Rob Williams, D. Gross.1D HRR data analysis and ATR assessment. Proceeding of SPIE, 1998,3020,54-59
[135] Kh. Rosenbach, J. Schiller. Non co-operative air target identification using radar imagery: identification rate as a function of signal bandwidth. IEEE international radar conference, Alexandria VA., 2000
[136] Duy H. Nguyen, John H. Kay, et al. Improving HRR ATR performance at low SNR by multi-look adaptive weighting. Proceeding of SPIE, 2001,4379, 216-228
[137] D. E. Dugeon. ATR performance modeling and estimation. Massachusetts: Lincoln laboratory, 1998
[138] Andrew P. Bradley. ROC curvesand x~2 test. Pattern Recognition Letters, 1996, (17), 287-294
[139] C. Brian Bassham. Automatic target recognition classification system evaluation methology. Doctor dissertation, Ohio, Air ForceInstitute of Technology, 2002
[140]Robert C. Holte. Very simple classification rulesperform well on most commonly used data sets. Machine Learning, 1993,11(1):63～91
[141]Anil K. Jain, Richard C. Dubes, Chaur-Chin Chen. Boostrap techniques for error estimation. IEEE Trans on PAMI, 1998,20(5):628～633
[142]David H. Wolpert. The mathematics of generalization. MA: Addison-Wesley, Reading, 1995
[143]Satoshi Watanabe. Pattern recognition: human and mechanical. New York, Wiley, 1985
[144]Richard O. Duba, Peter E. Hart, David G. Stork. Pattern classification (second edition). New York: John Wiley & Sons Inc., 2003
[145]Xing M D, Zheng B. The properties of range profile of aircraft. IEEE International Radar Conference, 2001,1050～1054.
[146]Li H J, Wang Y D. Matching score properties between range profiles of high-resolution radar targets. IEEE Trans. On AP, 1996,44(4):444～452
[147]章仁为.静止卫星的轨道和姿态控制.北京:科学出版社,1987
[148]张守信.外弹道测量与卫星轨道测量基础.北京:国防工业出版社,1992.10
[149]冯德军,陶华敏,刘中,等.对宽带高分辨雷达的转发式干扰方法.现代雷达,2005,27(11):19～23
[150]陈建文,倪晋麟.弹道导弹的雷达目标识别技术.第八届全国雷达学术年会论文集,合肥,2002
[151]M. E. Clark. High range resolution techniques for ballistic missile targets. British Aerospace PIC, 1999,6.1～6.13
[152]J. A. Richard, W. J. Bow, B. k. Bray. An informative confidence metric for ATR. SPIE Proceeding, 5095,336～348
[153]刘宏伟,保铮.基于复合特征及分层特征选择的雷达HRRP识别.系统工程与电子技术,2005,27(4):596～599
[154]R. Lambour, T. Morgan, etc. Assessment orbital debris size estimation from radar cross section measurements. 2001 Core technologies for space systems Conf, 2001
[155]庄钊文,肖顺平,王雪松.雷达极化信息处理及应用.北京:国防工业出版社,1999
[156]刘永祥,黎湘,庄钊文.空间目标进动特性及其在雷达目标识别中的应用.自然科学进展,2004,11(11):1329～1332
[157]金文彬,刘永祥,任双桥,等.锥体目标空间进动特性分析及其参数提取.宇 航学报,2004,25(4):408～410
[158]闫锦,黄培康.雷达目标识别中的维数压缩.系统工程与电子技术,2001,21(12):35～38
[159]李跃华,沈庆宏,等.小波神经网络的毫米波雷达目标一维像识别.南京理工大学学报,2002,26(1):20～23
[160]了解国家导弹防御技术.863先进防御技术通讯(A类),2000(10):1～42
[161]陈建文,李士国.基于目标运动分辨技术提取导弹目标运动特征.现代雷达,2003,25(6):5～7
[162]章仁为.卫星姿态动力学与控制.北京:北京航空航天大学出版社,1997.4
[163]张居凤,冯德军,等.雷达目标动态RCS仿真研究.系统仿真学报,2005,17(4):834～837
[164]杨行峻,迟惠生.语音信号数字处理.北京:电子工业出版社,1995
[165]M. Ross, H. Shaffer, et al. Average magnitude difference function pitch extractor. IEEE Trans. on ASSP, 1974,22(5):353～362
[166]Wolfgang Hess. Pitch determination of speech signals. New York: Springer-Verlag, 1983
[167]顾良,刘润生.高性能汉语语音基音周期估计.电子学报,1999,27(1):8～11
[168]张文耀,许刚,王裕国.循环AMDF及其语音周期估计算法.2003,31(6):886～890
[169]Caputi W J. Stretch: a time-transformation technique. IEEE Trans on AES, 1971,7(2):269～278.
[170]曹志道,刘永坦,许荣庆.实验ISAR若干参数的选择和实践.逆合成孔径雷达论文集,1996,20～26
[171]鹿国春,黄槐,王金奎,沈德泉.实验ISAR总体设计和实验.逆合成孔径雷达论文集,1996,27～46
[172]B. M. Welsh, R. W. Hawley. Linear FM radar effects on moving target signatures. Proceeding of SPIE, 2001,4382:161～173
[173]丁鹭飞,耿富录.雷达原理. 西安:西安电子科技大学出版社,1984
[174]Wood John C. Barry Daniel T. Radon Transformation of time-frequency distribution for analysis of multi-component signals. IEEE Trans. on Signal Processing, 1994,42(11):3166～3177.
[175]Wang Minsheng, Chan Andrew K, Chui Charles K. Linear frequency-modulated signal detection using radon-ambiguity transformation. IEEE Trans. Signal Processing, 1998,46(3):571～586.
[176]Abatzoglou T. J. Fast maximum likelihood joint estimation of frequency and frequency rate. IEEE Trans.on AES, 1986,22(6):708～715.
[177]刘渝.快速解线性调频技术.数据采集与处理.1999.6,14(2):175～178
[178]Peleg S, Porat B. Linear FM signal parameter estimation from discrete-time observations. IEEE Trans. on AES, 1991,27(4):607～615.
[179]冯德军,王雪松,削顺平,王国玉.基于单个宽带脉沖的至间目标测距和测速方法.信号处理,2006,22(1):73～77.
[180]张贤达.现代信号处理.北京:清华大学出版社,2002
[181]David Iny, Martin Monoi. Quanntative analysis of HRR NCTR performance drivers. SPIE, 1996,2747:144～152
[182]M. A. Hussain. HRR, length and velocity decision regions for rapid target identification. SPIE. 1999.3810:40～52
[183]孙文峰.宽带毫米波雷达精确制导信息处理方法研究.博士学位论文,长沙,国防科技大学研究生院,1998.07
[184]JianGuo Wang, Peikun He. Use of the radial velocity measurement in target tracking. IEEE Trans. on AES, 2003,39(2):401～413
[185]黄小红.目标高速运动对一维距离像的影响及补偿方法研究.信号处理,2001,18(6):487～490
[186]张毅.弹道导弹弹道学.长沙:国防科技大学出版社,1999
[187]Merrill I. Skolnik. Radar Handdbook, Second edition. McGraw-Hill Companies. Inc. 1990
[188]冯德军,王雪松,肖顺平,王国玉.弹道目标中段雷达成像仿真研究.系统仿真学报,2004,16(11):2511～2516
[189]H T Wu, J F Yang, F K Chen. Source number estimators using transformed Gerchgorin disks. Proc ICASSP, Adelaide: IEEE, 1994,261～264.
[190]H T Wu, J F Yang, F K Chen. Source number estimators using transformed Gerchgorin radii. IEEE Trans on SP, 1995,43(6):1325～1333
[191]H T Wu, C L Chen. A new Gerschgorin radii based method for source number detection. Proc 10th IEEE Workshop on Statistical Signal and Array Processing, Pennsylvania, IEEE Conf, 2000,104～107
[192]姜卫东,庄钊文,陈曾平.基于一维距离像的目标识别方法.现代雷达,1992,21(1):19～22
[193]E. J. Rothwell, D. P. Nyquist, K. M Chen. A radar target discrimination scheme using the discrete wavelet transform for reduced data storage. IEEE Trans. AP, 1994,42(7):1033～1037
[194]Hurst M P, Mittra R. Scattering center analysis via Prony'S method. IEEE Trans on AP, 1987,35(8):986～988
[195]Zyewck A., Bogner. Rader target classification of commercial aircraft. IEEE Trans. on AES, 1996,32(2):598～606
[196]Ricard A. Mitchell, John J. Westerkamp. Statistical feature based HRR radar classification. RTO SCI Symposium on non-cooperative air target identification using radar, Mannheim, Germany, 998
[197]Andrew R. Webb. Statistical pattern recognition. London, John Wiley & Sons, 2002
[198]Andrew R. Webb. Gamma mixture models for target recognition. RTO SCI Symposium on non-cooperative air target identification using radar, Mannheim, Germany, 998
[199]R. Heiden, F. Groen. The BOX-COX metric for nerest neighbor classification improvement. Pattern Recognition, 1997,30(2):273～279
[200]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.第九届全国雷达学术会议论文集,烟台,2004
[201]David Middleton. Statistical-Physical models of electromagnetic interference. IEEE Trans. on Electromagnet, 1977,19(3):106～126
[202]Leslile A. Berry. Understanding Middleton's canonical formula for Class A noise. IEEE Trans. on Electromagnet, 1981,23(4):337～344
[203]S. M. Zabin and H. V. Poor. Parameter estimation for Middleton Class A interference processes. IEEE Trans. on Communication, 1989,37(10):1042～1051
[204]Serena M. Zabin and H. Vincent Poor. Efficient estimation of Class A noise parameters via the EM algorithm. 1991, IEEE Trans. on Inform. Theory, 137(1):60～72
[205]Serena M. Zabin and H. Vincent Poor. Recursive algorithms for identification of impulsive noise channels. IEEE Trans. on Inform. Theory, 1990,36(3):559～578
[206]B. D. Steinberg. Microwave imaging with large antenna arrays: radio camera principles and techniques. New York: John wiley and Sons, 1983
[207]沈永欢,梁在中,等编.实用数学手册.北京:科学出版社,1992
[208]刘永坦.雷达成像技术,哈尔滨:哈尔滨工业大学出版社,1999
[209]保铮,邢孟道,王彤.雷达成像技术.北京:电子工业出版社,2005
[210]D. E. Wahl, P. H. Eichel, D. C. Chiglia. Phase gradient autofocus robust tool for high resolution SAR phase correction. IEEE Trans on AES, 1994,30(3):827～834
[211]Z. D Zhu, Z. R. Ye, X. Q. Wu. Super-resolution range-doppler imaging. IEE Proc. -F, 1995,142(1):25～32
[212]HaiQing Wu, Gilles Y. Dellsle. Precision tracking algorithms for ISAR imaging. IEEE Trans. on AES, 1996,32(1):243～254
[213]Victor C. C, Shie Qian. Joint time-frequency transform for radar range-doppler imaging. IEEE Trans. on AES, 1998,34(2):486～499
[214]Jian Li. Renbiao, Wu. Robust autofocus algorithm for ISAR imaging of moving targets. IEEE Trans. on AES, 2001,37(3):1056～1068
[215]王洋,陈建文,刘中.导弹目标ISAR成像仿真分析.现代雷达,2003,25(10):18～21
[216]Byron M. Welsh, Robet W. Hawley. Linear FM radar effects on moving target signatures. Proceedings of SPIE, 2001,4382:164～173
[217]Lee A. Centrohermitian and skew-centrohermitiam matrices. Linear Algebra and its applications, 1980,29:205～210
[218]Martin Haardt, Josef A. Nossek. Unitary ESPRIT: How to obtain increased estimation accuracy with a reduced computational burden. IEEE Trans. on signal processing, 1995,43(5):1232～1242
[219]王国林.逆合成孔径雷达运动补偿和系统补偿研究.博士学位论文,哈尔滨,哈尔滨工业大学,1996.1
[220]李金宗.模式识别导论.北京:高等教育出版社,1994
[221]Breimin L. Stacked regressions. Machine Learing, 1996,24:49～64
[222]C. T. Zahn, R. Z. Roskies. Fourier decripotors for plane closed curves. IEEE Trans. on Computer, 1972, C-21:269-281
[223]E. Persoon, K. S. Fu. Shape discrimination using Fourier descriptors. IEEE Trans. Systerms. Man and Cybernetics, 1977, SMC-7:170～179
[224]D. R. Wehner. High resolution radar. London, Artech House, 1987
[225]刘延柱.空间飞行器姿态控动力学.北京:国防工业出版社,1995
[226]Thompson W T. Introduction to space dynamics. New York, Wiley, 1961
[227]屠善澄.卫星姿态动力学与控制.北京:宇航出版社,2001
[228]Zhu Z D, et al. Super-resolution range-doppler imaging. IEE Proceeding—-F, 1995,142(1):25～32
[229]R M Nuthalapati. High resolution reconstruction of ISAR images. IEEE Trans. on AES, 1992,28(2):462～472
[230]Odendall J W, Barnard E, et al. Two-dimensional super resolution radar imaging using the MUSIC algorithm.IEEE Trans. on AP 1994,42(10):1386～1391.
[231]朱兆达,叶蓁如,邬小青.一种超分辨距离多普勒成像方法.电子学报,1992,20(7):1～6
[232]Zhu Z D, Ye Z R, Wu X Q. An approach to superresolution range-doppler imaging. Acta Electronica Sinica, 1992,20(7):1～6.(Chinese Source).
[233]吴强,王国林,许荣庆.ESPRIT超分辨ISAR成像.系统工程与电子技术,1999,21(7):33～36
[234]Kim K, Choi IS, Kim H. Efficient radar target identification using adaptive joint time-frequency processing. IEEE Trans. on AP, 2000,48(12):1789～1801
[235]Ueda N. Optimallinear combination of neural networks for improving classification peromance. IEEE Trans. on PAMI, 2000,22(2):207～215
[236]Woods K. Combination of multiple classifiers using local accuracy estimates. IEEE Trans. on PAMI, 1997,19(4):405～410
[237]Ruggieri S. Efficeent C4.5. IEEE Trans. on Knowledeg and Data Engineering. 2002,24(2):438～444
[238]Breiman L., Friedman J. H., Olshen R. A., et al. Classfication and regression trees. New Ybrk, Chapmen & Hall, 1984.
[239]Shafer J, Aggrawal R, Mehta M. SPRINT: a scalable parallel classifier for data mining. IBM Almaden Research Center, San Jose, Calofomia, 1996
[240]Quinlan J R. Introduction decision trees. Machine Learing, 1986,1(1):81～1-6
[241]Hand D. J. Construction and assessment of classification rules. Chichester: Wiley, 1997
[242]McLachlan, G. J. Discriminant analysis and statistical pattern recognition. New York, Wiley, 1992
[243]Xiaofan Lin, Xiaoqing Ding, Ming Chen, et al. Adaptive confidence transform based classifier combination for Chinese character recognition. Pattern Recognition Letters, 1998,19(10):975～988
[244]Bouchaffra D. A methodology for mapping scores to probabilities. IEEE Trans. on PAMI, 1999,21(9):923～927
[245]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
[246]Liu C L. Nakagawa M. Precise candidate selection for large character set recognition by confidence evaluation. IEEE Trans. on PAMI, 2000,22(6):636～642
[247]Gondail F, Lange E, Iwamoto T, et al. Face recognition system using local autocorrelations and multiscale integration. IEEE Trans. on PAMI, 1996,18(10):1024～1028
[248]Smith S J. Handwritten character classification using nearest ngighbor in large dataset. IEEE Trans. on PAMI, 1994,16(9):915～919
[249]J. A. Hanley, B. J. McNeil. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 1982,143:29～36
[250]Andrew P. Bradley. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 1997,30(7):1145～1159
[251]David J. Hand, Robert J. Till. A simple generalization of the area under the ROC curve for multiple class classification problems. Machine learning, 2001,45:171～186
[252]Niall M. Adams, David J. Hand. An improved meaure for comparing diagnostic tests. Computers in Biology and Medcine, 2000,30:89～96
[253]Duin, R. P. W. A note on comparing classifiers. Pattern Recognition Letters, 17:529～536
[254]Dieterich, T. G. Approximate statistical tests for comparing supervised classification learning algorithms. Netural Computation, 1998,10:1895～1923
[255]Adams N M., Hand D. J. Comparing classifiers when the misallocation costs are uncertain. Pattern Recognition, 1999,32:1139～1147
[256]Provost F., Fawcett T. Robust classification for imprecise environments. Machine Lerning, 2001,42:203～231
[257]Kittler J., Hatef M., Duin R. P. W., et al. On combining classifiers. IEEE Trans. on PAMI, 1998,20(3):226～239
[258]Wolpert D. H. Stacked generalization. Neural Networks, 1992,5(2):241～260
[259]Jacobs, R. A., Jordan M. I., Nowlan S. J., et al. Adaptive mixtures of local experts. Neural Computation. 1991,3:79～87
[260]郭桂蓉.模糊模式识别.长沙:国防科技大学出版社,1992
[261]Carrara W G, et al. Majewski R. M. Spotlight synthetic aperture radar: signal processing algorithms. London, Artech House, 1995
[262]黄培康.雷达目标特征信号.北京:宇航出版社,1993
[263]王国雄.弹头技术.北京:宇航出版社,1993
[264]王保义,时振栋.电磁场在目标识别中的应用.北京:电子工业出版社,1995
[265]戴征坚.空间目标的雷达特性预估与识别.博士学位论文,长沙,国防科技大学研究生院,2000
[266]B. Kang. Emproved adaptive-beamforming targets for self-caibrating a distorteed phased array. IEEE Trans. on AP, 1990,38(2):186～194
[267]Min-Chin Lin, Yean-Woei Klang. Performance analysis of a radar target discrimination technique. IEEE Trans. on AES, 1991,28(2):363～368
[268]Isaac Backman.Model of laser radar Signatures of ballistic missile warhead. SPIE, 1999,3699:133-137
[269]Bao Z, Sun C Y, Xing M D. Time-frequency approaches to ISAR imaging of maneuvering targets and their limitations. IEEE Trans. on AES, 2001,37(3):1091～1099
[270]Paul F. Hemler. Radar imaging understanding for complex space object. SPIE, 1381,55～65
[271]Cheryl L. Resch. Neural networks for exo-atmospheric target discrimination. SPIE, 1998,3371:119～128
[272]Olivier D. M., Robert H. Bishop. Tracking and identification of a maneuvering reentry vehicle. AIAA 2003.5446,1993,1～11
[273]Xiao Zhang, J. Zhang, G. G. Walter. Shape space object recognition. SPIE, 2001,4379:432～443
[274]宋枫溪,高林.文本分类器性能评估指标.计算机工程,2004,30(3):107～109
[275]K. Schultz, S. Davidson, A. Stein, et al. Range doppler laser radar for midcourse discrimination: the firefly experiments. AIAA, 1994.2653,1～12