基于脉内特征的雷达辐射源信号识别研究
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摘要
雷达辐射源信号(RES)识别是雷达对抗信号处理中的关键技术,其识别水平已成为衡量电子对抗装备先进程度的重要标志。长期以来,人们主要依靠常规五参数实现RES的识别处理,这只有在常规雷达信号和信号密集度较低的情况下才能获得满意的识别效果。随着现代电子和雷达技术的发展以及它们在现代战争中的广泛应用,新型RES调制式样更加灵活,参数日益多变,电磁信号越来越密集,致使传统的识别技术已无法满足现代电子战的实际需要。因此,迫切需要探索新的RES识别方法,以提高我国电子对抗装备的技术水平。
近年来,国内外学者对RES识别进行了大量的研究,提出了许多新方法,以提高RES识别水平。但是,这些方法一般要求较高的信噪比、较为明显的信号类型差异,且大都基于常规参数相对稳定、辐射源数量较少等情况,很少涉及调制类型相同、参数变化和低信噪比下调制类型不同的信号识别问题。一般都只关注特征提取和识别算法的研究,很少涉及特征识别能力和高密集电磁环境下异常脉冲的处理等RES识别问题的分析。而这些实际问题,已严重制约RES识别技术的进一步提高。因此,本文从特征的可分性、脉内特征提取、识别模型和算法设计等方面,针对上述复杂体制RES识别中需要解决的关键理论问题展开了系统、深入的研究,主要贡献有以下5个方面。
1.基于概率论与统计理论,构建了一维特征参数的可分性模型,以分析提取特征的识别能力,为RES特征提取提供理论支持。在模型构建过程中,依据特征参数服从近似正态分布,研究并推导出了特征正确分类概率随相应特征统计参数变化的关系子模型。从而导出了特征参数要在理论上达到90%以上的正确分类概率,两类特征估计均值之差的绝对值与估计精度之比至少应大于3.3的结论。然后,定量地分析了常用特征参数的可分性度量。
2.提出了脊线-频率特征(脊频特征)及其级联特征提取方法,为在低信噪比条件下,实现不同调制类型的RES的有效识别补充新的特征参数。首先从脊线定义出发,基于信号时-频尺度原理导出了小波脊频特征(WRF)提取的条件约束模型。然后,提出一种新小波原子和脊线提取策略对脊线提取算法进行改进,并提取了典型RES的WRF。在此基础上,对WRF进行特征降维再挖掘,提取了一组能较好描述RES脉内调变统计规律的级联特征。新的增量模糊支持向量机被用于检验提取特征的有效性。
3.提出了小波包融合和融合熵特征提取方法,为实现类型相同、仅仅某些参数具有差异的近似雷达辐射源信号(ARES)识别构建有效的特征向量。首先使用小波包变换和主分量分析构建小波包融合算法。在此基础上,提取融合特征的融合香农熵、范数熵和概率熵,并对三种熵特征的抗噪性能进行详细分析。然后,进一步研究了LFM信号参数的识别问题。考虑到信号分解层数、特征维数和参数等多种影响因素,论文深入分析了不同识别算法与不同特征组合的识别性能。实验结果表明,使用融合熵特征向量不仅具有较好的ARES识别效果,而且算法复杂度远小于传统方法,基于实测和仿真数据给出了识别结果。
4.对雷达功率放大器的非线性特性进行详细分析,从而导出了反映非线性特性的谐波功率约束模型,并提出了相应的谐波功率约束特征(HPRF)提取算法,为实现辐射源个体识别提供新的无意调制参数。在估计谐波功率时,为削弱噪声影响、提高估计精度,基于二项展开式,导出了谐波功率谱的自相关估计模型。比较放大器输入功率固定和变化两种情况下HPRF的稳健性结果,发现在输入功率变化时,HPRF的二维分布成近似线性关系。最后的测试结果表明HPRF具有良好的识别性能,并得出测试信号需要不小于400个脉冲的能量积累,才能达到论文中识别结果的结论。
5.提出了一种增量模糊支持向量机识别算法用于雷达辐射源信号识别,以提高信号的识别率,并解决当前识别算法难以处理非库属目标、训练时间较长等问题,深入研究了算法设计过程中所涉及的相关理论问题和解决方案。其中,构建了训练样本的类隶属度模型,提出了确定最小超球体半径的支持向量模糊数据描述方法,并引入了平凡训练数据的概念。在此基础上,提出增量模糊学习算法。然后,基于属性理论构建了处理未知雷达信号的拒判规则,以控制虚警率。最后,考虑多种影响因素,通过若干实验,深入研究了不同参数和样本数量条件下算法的识别性能。
本论文的研究工作得到国家自然科学基金(No.60572143, No.60702026)和国防科技重点实验室基金的共同资助。
The recognition of radar emitter signals(RES) is a key technology in signal processing of radar countermeasures, and the recognition level of RES has become an important symbol of the technical merit of the radar countermeasures equipment. For a long time, traditional methods of recognizing radar signals are generally based on five conventional parameters, which these methods are effective and can obtain satisfactory results in the low dense environment. However, with the rapid development of electronic technology and radar technology, the modulation manner of RES became more and more complex and various, and the circumstance of RES became increasing denseness. As a result, the performances of these traditional methods descend rapidly. Therefore, only some new and valid approaches are explored to improve the technical merit of electronic countermeasure equipments.
In recent years, though many scholars have helpfully explored a great of new methods to improve the recognition level of RES, these proposed approaches only analyze those signals in high SNR, obvious difference, fixed signal parameters, and fewer emitters. The existing methods are difficult to identify same modulation signals, parameter-changes signals and different modulation signals in low SNR. The feature-separability of radar signals and the singularity pulse processing in dense circumstance of RES are not also studied. In fact, the recognition technology of RES has been restricted by these problems. Thereout, from the view of four important facets, which are feature-separability, intra-pulse feature extraction, novel model and algorithm of signal recognition, recognition methods of advanced RES have been studied. The research fruits are as follows.
1. In order to explore the problem of the radar signal feature-separability, the one-dimensional feature-separability model of the signal feature is built based on the probability and statistics theory. According to parameters obey approximate normal distribution, and the relationship sub-model of the correct classification probability and correspondence feature statistics parameters is proposed. As a result, the correct classification probability is more than 90% when the ratio value of the measure precision and the absolute value of the difference of two feature mean values is not less than 3.3. Afterwards, the separability measurement of convention parameters in signal recognition is gained.
2. The feature extraction algorithms of ridge-frequency features and Cscade Cnnection features of the ridge-frequency feature are proposed. Via these algorithms, some new parameters can be extracted, so that different modulation signals can be recognized. According to the time-frequency principle and the definition of the ridge-line, the condition restriction model of the ridge-frequency feature is constructed, and then the improved ridge-line feature extraction algorithm is proposed based on a new wavelet atom and extraction strategy of ridge-line. After extracted the ridge-frequency of radar emitter signals, the Cscade-Cnnection features of the feature are extracted to describe the modulation characteristics of the signal. The results of classification experiments based on increment fuzzy support vector machine demonstrate that Cscade-Cnnection features of the ridge-frequency featrure can reflect the difference of different modulation signals, and have a good ability to resist noise.
3. The wavelet packet fusion algorithm and the feature extraction algorithms of fusion entropy features are proposed to construct the effective recognition feature vector for approximately radar emitter signals(i.e., the modulation manner of the signal is identical, but some parameters of the signal are different). In this method, the choice rule of the wavelet is presented, and the fusion algorithm is reconstructed based on the wavelet packet decomposition and the principle component analysis. Similarly, the fusion Shannon entropy, fusion Norm entropy and fusion Probability entropy are extracted to describe the energy structure of the signal, and analyzing the resisting noise capability of three entropy features. Afterward, the parameters of LFM are estimated. Considering the number of wavelet decomposition layers, dimension numbers of the feature and various parameters, it is been researched detailedly that the recognition performances of the different features based on different recognition algorithms in this paper. The experiment results show that the proposed approach not only achieves good in recognition effect, but also suffers less computational burden than traditional methods.
4. According to analyses of the nonlinearity of the radar power amplifier, the harmonic power restriction model is constructed to describe the nonlinear characteristics of the amplifier, and the correspondence feature extraction algorithm of harmonic power restriction (HPR) is proposed. Via this algorithm, some unintentional-modulation features are extracted to recognize the emitter. In this algorithm, the correlation estimation model of the harmonic power of the signal is proposed based on two-term formula. Comparing with the solidity of the HPR feature in different power conditions, the linear relationship of HPR features is obtained when input power of the amplifier is various. The experiment results have shown that these conclusions can be drawn in this paper, if energy accumulation of the pulse signal is enough.
5. Aiming at unknown radar signal processing, lower signal recognition rate and longer training time of the existence signal recognition algorithm, an increment fuzzy support vector machine algorithm is proposed to improve the signal recognition rate, and the correspondence theory and solving-scheme are studied detailedly in the design of the algorithm. In this algorithm, the combine membership function of every training example and the support vector fuzzy data description method for confirming the radius of the hypersphere are proposed, the conception of the common training data set is introduced and the increment fuzzy training algorithm is also presented. Then, in order to control the loss alert ratio, the rejection strategy of unknown radar emitter signals is proposed based on the attribute theory. Lastly, considering all kinds of effect factors, it is been studied deeply that the recognition performances of the recognition algorithm based on different parameters and training example numbers.
This work is supported by the National Natural Science Foundation of China (No.60572143, No.60702026) and the National Electronic Warfare Laboratory Foundation.
近年来,国内外学者对RES识别进行了大量的研究,提出了许多新方法,以提高RES识别水平。但是,这些方法一般要求较高的信噪比、较为明显的信号类型差异,且大都基于常规参数相对稳定、辐射源数量较少等情况,很少涉及调制类型相同、参数变化和低信噪比下调制类型不同的信号识别问题。一般都只关注特征提取和识别算法的研究,很少涉及特征识别能力和高密集电磁环境下异常脉冲的处理等RES识别问题的分析。而这些实际问题,已严重制约RES识别技术的进一步提高。因此,本文从特征的可分性、脉内特征提取、识别模型和算法设计等方面,针对上述复杂体制RES识别中需要解决的关键理论问题展开了系统、深入的研究,主要贡献有以下5个方面。
1.基于概率论与统计理论,构建了一维特征参数的可分性模型,以分析提取特征的识别能力,为RES特征提取提供理论支持。在模型构建过程中,依据特征参数服从近似正态分布,研究并推导出了特征正确分类概率随相应特征统计参数变化的关系子模型。从而导出了特征参数要在理论上达到90%以上的正确分类概率,两类特征估计均值之差的绝对值与估计精度之比至少应大于3.3的结论。然后,定量地分析了常用特征参数的可分性度量。
2.提出了脊线-频率特征(脊频特征)及其级联特征提取方法,为在低信噪比条件下,实现不同调制类型的RES的有效识别补充新的特征参数。首先从脊线定义出发,基于信号时-频尺度原理导出了小波脊频特征(WRF)提取的条件约束模型。然后,提出一种新小波原子和脊线提取策略对脊线提取算法进行改进,并提取了典型RES的WRF。在此基础上,对WRF进行特征降维再挖掘,提取了一组能较好描述RES脉内调变统计规律的级联特征。新的增量模糊支持向量机被用于检验提取特征的有效性。
3.提出了小波包融合和融合熵特征提取方法,为实现类型相同、仅仅某些参数具有差异的近似雷达辐射源信号(ARES)识别构建有效的特征向量。首先使用小波包变换和主分量分析构建小波包融合算法。在此基础上,提取融合特征的融合香农熵、范数熵和概率熵,并对三种熵特征的抗噪性能进行详细分析。然后,进一步研究了LFM信号参数的识别问题。考虑到信号分解层数、特征维数和参数等多种影响因素,论文深入分析了不同识别算法与不同特征组合的识别性能。实验结果表明,使用融合熵特征向量不仅具有较好的ARES识别效果,而且算法复杂度远小于传统方法,基于实测和仿真数据给出了识别结果。
4.对雷达功率放大器的非线性特性进行详细分析,从而导出了反映非线性特性的谐波功率约束模型,并提出了相应的谐波功率约束特征(HPRF)提取算法,为实现辐射源个体识别提供新的无意调制参数。在估计谐波功率时,为削弱噪声影响、提高估计精度,基于二项展开式,导出了谐波功率谱的自相关估计模型。比较放大器输入功率固定和变化两种情况下HPRF的稳健性结果,发现在输入功率变化时,HPRF的二维分布成近似线性关系。最后的测试结果表明HPRF具有良好的识别性能,并得出测试信号需要不小于400个脉冲的能量积累,才能达到论文中识别结果的结论。
5.提出了一种增量模糊支持向量机识别算法用于雷达辐射源信号识别,以提高信号的识别率,并解决当前识别算法难以处理非库属目标、训练时间较长等问题,深入研究了算法设计过程中所涉及的相关理论问题和解决方案。其中,构建了训练样本的类隶属度模型,提出了确定最小超球体半径的支持向量模糊数据描述方法,并引入了平凡训练数据的概念。在此基础上,提出增量模糊学习算法。然后,基于属性理论构建了处理未知雷达信号的拒判规则,以控制虚警率。最后,考虑多种影响因素,通过若干实验,深入研究了不同参数和样本数量条件下算法的识别性能。
本论文的研究工作得到国家自然科学基金(No.60572143, No.60702026)和国防科技重点实验室基金的共同资助。
The recognition of radar emitter signals(RES) is a key technology in signal processing of radar countermeasures, and the recognition level of RES has become an important symbol of the technical merit of the radar countermeasures equipment. For a long time, traditional methods of recognizing radar signals are generally based on five conventional parameters, which these methods are effective and can obtain satisfactory results in the low dense environment. However, with the rapid development of electronic technology and radar technology, the modulation manner of RES became more and more complex and various, and the circumstance of RES became increasing denseness. As a result, the performances of these traditional methods descend rapidly. Therefore, only some new and valid approaches are explored to improve the technical merit of electronic countermeasure equipments.
In recent years, though many scholars have helpfully explored a great of new methods to improve the recognition level of RES, these proposed approaches only analyze those signals in high SNR, obvious difference, fixed signal parameters, and fewer emitters. The existing methods are difficult to identify same modulation signals, parameter-changes signals and different modulation signals in low SNR. The feature-separability of radar signals and the singularity pulse processing in dense circumstance of RES are not also studied. In fact, the recognition technology of RES has been restricted by these problems. Thereout, from the view of four important facets, which are feature-separability, intra-pulse feature extraction, novel model and algorithm of signal recognition, recognition methods of advanced RES have been studied. The research fruits are as follows.
1. In order to explore the problem of the radar signal feature-separability, the one-dimensional feature-separability model of the signal feature is built based on the probability and statistics theory. According to parameters obey approximate normal distribution, and the relationship sub-model of the correct classification probability and correspondence feature statistics parameters is proposed. As a result, the correct classification probability is more than 90% when the ratio value of the measure precision and the absolute value of the difference of two feature mean values is not less than 3.3. Afterwards, the separability measurement of convention parameters in signal recognition is gained.
2. The feature extraction algorithms of ridge-frequency features and Cscade Cnnection features of the ridge-frequency feature are proposed. Via these algorithms, some new parameters can be extracted, so that different modulation signals can be recognized. According to the time-frequency principle and the definition of the ridge-line, the condition restriction model of the ridge-frequency feature is constructed, and then the improved ridge-line feature extraction algorithm is proposed based on a new wavelet atom and extraction strategy of ridge-line. After extracted the ridge-frequency of radar emitter signals, the Cscade-Cnnection features of the feature are extracted to describe the modulation characteristics of the signal. The results of classification experiments based on increment fuzzy support vector machine demonstrate that Cscade-Cnnection features of the ridge-frequency featrure can reflect the difference of different modulation signals, and have a good ability to resist noise.
3. The wavelet packet fusion algorithm and the feature extraction algorithms of fusion entropy features are proposed to construct the effective recognition feature vector for approximately radar emitter signals(i.e., the modulation manner of the signal is identical, but some parameters of the signal are different). In this method, the choice rule of the wavelet is presented, and the fusion algorithm is reconstructed based on the wavelet packet decomposition and the principle component analysis. Similarly, the fusion Shannon entropy, fusion Norm entropy and fusion Probability entropy are extracted to describe the energy structure of the signal, and analyzing the resisting noise capability of three entropy features. Afterward, the parameters of LFM are estimated. Considering the number of wavelet decomposition layers, dimension numbers of the feature and various parameters, it is been researched detailedly that the recognition performances of the different features based on different recognition algorithms in this paper. The experiment results show that the proposed approach not only achieves good in recognition effect, but also suffers less computational burden than traditional methods.
4. According to analyses of the nonlinearity of the radar power amplifier, the harmonic power restriction model is constructed to describe the nonlinear characteristics of the amplifier, and the correspondence feature extraction algorithm of harmonic power restriction (HPR) is proposed. Via this algorithm, some unintentional-modulation features are extracted to recognize the emitter. In this algorithm, the correlation estimation model of the harmonic power of the signal is proposed based on two-term formula. Comparing with the solidity of the HPR feature in different power conditions, the linear relationship of HPR features is obtained when input power of the amplifier is various. The experiment results have shown that these conclusions can be drawn in this paper, if energy accumulation of the pulse signal is enough.
5. Aiming at unknown radar signal processing, lower signal recognition rate and longer training time of the existence signal recognition algorithm, an increment fuzzy support vector machine algorithm is proposed to improve the signal recognition rate, and the correspondence theory and solving-scheme are studied detailedly in the design of the algorithm. In this algorithm, the combine membership function of every training example and the support vector fuzzy data description method for confirming the radius of the hypersphere are proposed, the conception of the common training data set is introduced and the increment fuzzy training algorithm is also presented. Then, in order to control the loss alert ratio, the rejection strategy of unknown radar emitter signals is proposed based on the attribute theory. Lastly, considering all kinds of effect factors, it is been studied deeply that the recognition performances of the recognition algorithm based on different parameters and training example numbers.
This work is supported by the National Natural Science Foundation of China (No.60572143, No.60702026) and the National Electronic Warfare Laboratory Foundation.
引文
[1]胡来招.雷达侦察接收机设计.北京：国防工业出版社,2000
[2]Schroer R. Electronic warefare. IEEE Trans Aerospace Electronic Systems Magazine.2003,18(7):49-54.
[3]Collins J H, Grant P M. A review of current and future components for electronic warfare receivers. IEEE Transcations on Sonics and Ultrasonics. 1981,28(3):117-125.
[4]林象平.雷达对抗原理.西安：西北电讯工程学院出版社,1985.
[5]Lighart V A, Logvin A I.A survey of radar ECM and ECCM. IEEE Transactions on Aerospace and Electronic Systems.1995,31(3): 1110-1120.
[6]林锋.电子对抗.北京：科学出版社,1987.
[7]赵国庆.雷达对抗原理.西安：西安电子科技大学出版社,1999.
[8]Richard G W. Electronic intelligence:the analysis of radar signals. The 2nd ed., Norwood, MA:Artech House Inc.,1993.
[9]戚世权,姜秋喜.电子战数字接收机及其信号处理.航天电子对抗.1994,(4)：47-50.
[10]Granger E, Rubin M A, Grossberg, et al. A what-and where fusion neural network for recognition and tracking of multiple radar emitters. Neural Networks.2001,14(3):325-344.
[11]Lee J P Y. A multi-channel digital receiver for intrapulse analysis and direction-finding. Proceedings of IEEE Pacific Rim Conference on Communication, Computers and Signal Processing, Canada. 1999:589-592.
[12]Elbirt A J. Information warfare:are you at risk. IEEE Technology and Society Magazine.2003,22(4):13-19.
[13]李祖新.雷达对抗面临严重挑战.舰船电子对抗.1999,1：5-8.
[14]斯科尼克主编.雷达手册.王军强等译.第2版.北京：电子工业出版社,2003.
[15]谭显裕.21世纪海空电子战特征及发展趋势.雷达与对抗.1994,2：25-29.
[16]Stove A G, Hume A L and Baker C J. Low probability of intercept radar strategies. IEEE Proc. Radar Sonar Navig.2004,151(5):249-260.
[17]王乃和.关于ESM中雷达信号分选识别问题的探讨.电子对抗.1991,25(3)：44-51.
[18]解文斌,姜文利,黄勇杰等.脉冲编码信号的辐射源分类.电子对抗技术.2004,1 9(3)：23-29.
[19]胡波.脉内特征提取在信号调制形式识别中的应用.雷达与对抗.2005,2：35-38.
[20]王勇,张欣.雷达信号脉内特征参数提取技术.中国雷达.2006,1：13-21.
[21]桑炜森,顾耀平.综合电子战新技术新方法.北京：国防工业出版社,1996.
[22]Therrien C W. Application of feature extraction to radar signature classification. Proceedings of the 2th Interational Pattern Recognition Symposium.1974,125-132.
[23]束坤,盛九朝.ESM系统与低截获概率雷达之争.舰船电子对抗.2005,28(3)：11-13.
[24]Davies C L, Hollands H. Automatic processing for ESM. IEE Proceedings, Part F:Radar& Signal Process.1982,129(3):146-171.
[25]Andrews R S. ESM processing using 3D memory mapping and adaptive pattern formation algorithms. Military Microwaves Transactions.1984, 27-36.
[26]Mardia H K. Adaptive multi-dimensional clustering for ESM. Proceedings of IEE Colloquium on Signal Processing for ESM Systems.1988,5/1-5/4.
[27]Langley L E. Specific emitter identification (SEI) and classical parameter fusion technology. Proceedings of the WESCON.1993,377-381.
[28]徐欣,周一宇,卢启中.雷达截获系统实时信号分选处理技术研究.系统工程与电子技术.2001,23(3)：12-15.
[29]王海.电子战ESM系统技术发展综述.飞航导弹.2006,1：60-62.
[30]Danielsen P L, Agg D A, Burke N R. The application of pattern recognition techniques to ESM data processing. IEE Colloquium on Signal Processing for ESM Systems.1988,6/1-6/4.
[31]Agg D A. The development of an ESM data processing scheme based on pattern recognition techniques. Proc. of IEE Colloquium on Electronic Warfare Systems.1991,5/1-5/4.
[32]Perdriau B. Modulation domain offers a new view of radar performance. MSN.1990, (4):27-43.
[33]穆世强.雷达信号脉内细微特性分析.电子对抗.1991,2：28-37.
[34]张葛祥.雷达辐射源信号智能识别方法研究[PHD].成都：西南交通大学,2005.
[35]Delpart N. Asymptotic wavelet and Gabor analysis:extraction of instantaneous frequencies. IEEE Trans Information Theory.1992, 38(3):644-664.
[36]Liu W K, Zhu D J, Zhang C H. The extraction of modulation characteristics of radar signal using wavelet transform. Proceedings of the 4th International Conference on Signal Processing, Bei'jin.1998:288-291.
[37]梁百川.利用小波变换识别脉内调制信号.电子对抗.1998,3：1-12.
[38]郁春来,何明浩,斐立志.基于改进小波脊线法的LFM信号脉内特征提取.航天电子对抗.2004,4：38-42.
[39]曲长文,乔治国.雷达信号脉内特征的小波分析.上海航天.1996,5：15-19.
[40]魏东升,徐东晖,林象平.雷达信号脉内细微特征的时频分析.电子对抗.1993,4：7-13.
[41]赵拥军,黄洁.雷达信号细微特征时频分析法.现代雷达.2003,25(12)：26-28.
[42]Moraitakis I, Fargues M P. Feature extraction of intra-pulse modulated signals using time-frequency analysis. Proceedings of 21st Century Military Communications Conference, Los Angeles.2000:737-741.
[43]柳征,姜文利,周一宇.基于小波包变换的辐射源信号识别.信号处理.2005,21(5)：460-464.
[44]张葛祥,荣海娜,金炜东.基于小波包变换和特征选择的雷达辐射源信号识别.电路与系统学报.2006,6(11)：45-49.
[45]朱明,普运伟,金炜东,胡来招.基于时频原子方法的雷达辐源信号特征提取.电波科学学报.2007,22(3)：458-462.
[46]Jarmo Lunden, Visa Koivunen. Automatic radar waveform recognition. IEEE Journal of Selected Topics in Signal Processing.2007,1(1):124-136.
[47]T.Q.Gulum, P.E.Pace and R.Cristi. Extraction of polyphase radar modulation parameters using a wigner-ville distribution-radon transform. U.S.Government Work not Protected by U.S.Copyritht.2008,1505-1508.
[48]Ray P S. Radar waveform modulation recognition by neural processing. Proceedings of International Symposium on Signal Processing and Its Applications.1996,121-124.
[49]毕大平,董晖,姜秋喜.基于瞬时频率的脉内调制识别技术.电子对抗技术.2005,20(2)：6-9.
[50]普运伟,金炜东,胡来招.基于瞬时频率二次特征提取的辐射源信号分类.西南交通大学学报.2007,42(3),373-379.
[51]Roome S J. Classification of radar signals in modulation domain. Electronics Letters.1992,28(8):704-705.
[52]黄知涛,周一宇,姜文利.基于相对无模糊相位重构的自动脉内调制特性分析.通信学报.2003,24(4)：153-160.
[53]Trent McConaghy, Henry Leung, Eloi Bosse, Vinay Varadan. Classification of audio radar signals using radial basis function neural networks. IEEE Transactions on Instrumentation and Measurement.2003, 52(6):1771-1779.
[54]Zhang G X, Hu L Z, Jin W D. Resemblance coefficient and a quantum genetic algorithm for feature selection. Lecture Notes in Artificial Intelligence.2004,3245:155-168.
[55]Zhang G X, Jin W D, Hu L Z. Radar emitter signal recognition based on complexity feature. J. of Southwest Jiaotong Univ.2004,12(2):116-122.
[56]张葛祥,胡来招,金炜东.雷达辐射源信号脉内特征分析.红外与毫米波学报.2004,23(6)：478-480.
[57]张葛祥,胡来招,金炜东.基于熵特征的雷达辐射源信号识别.电波科学学报.2005,20(4)：440-445.
[58]张国柱.雷达辐射源识别技术研究[PHD].长沙：国防科技大学,2005.
[59]陈东锋,雷英杰,潘寒尽.基于模糊加权法的雷达辐射源识别.现代防御技术.2005,33(6)：57-59.
[60]Kenneth I. Talbot, Paul R. Duley, Martin H. Hyatt. Specific emitter identification and verification. Technology Review Journal Spring/Summer.2003:113-133.
[61]Kawalec Adam, Owczarek Robert. Specific emitter identification using intrapulse data. European Radar Conference, Amsterdam.2004:249-252.
[62]Kawalec A, Owczarek R. Radar emitter recognition using intrapulse data. Microwave& Radar Week in Poland, MICON 2004. Warsaw,2004,2: 435-438.
[63]J. Dudczyk, M. Wnuk, The utilization of unintentional radiation for identification of the radiation sources. The 34thEuropean Radar Conference, Amsterdam,2004:777-780.
[64]Carroll T L. A nonlinear dynamics method for signal identification. Chaos. 2007,17:023109.
[65]Matuszewski J, Paradowski L. The knowledge based approach for emitter dentification. In Microwaves and Radar, MIKON'98,12th International Conference.1998,3(5):810-814.
[66]Matuszewski J, Kawalec A. Knowledge-based signal processing for radar identification. The 9th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science TCSET'2008. Ukraine, Lviv-Slavsko,2008,302-305.
[67]L.Anjaneyulu, N.S.Murthy, N.V.S.N.Sarma. Radar emitter classification using self-organising neural network models. Proceedings of International Conference on Microwave.2008,431-433.
[68]董晖,姜秋喜.基于多脉冲的雷达个体识别技术.电子对抗.2006,6：12-18.
[69]姜秋喜,董晖基于单脉冲的雷达个体识别技术.舰船电子对抗.2006,29(4)：60-66.
[70]陈昌孝,何明浩,王志斌,高峰.基于双谱分析的雷达辐射源个体特征提取.航天电子对抗.2007,23(5)：46-49.
[71]Tao-wei Chen, Wei-dong Jin, Jie LI. Feature extraction using surrounding-line integral bispectrum for radar emitter signal. International Joint Conference on Neural Networks, Hong Kong.2008:294-298.
[72]潘继飞,姜秋喜,毕大平.雷达“指纹”参数选取.现代防御技术.2007,35(1),71-75.
[73]刘庆云,陆飞飞,朱伟强,王根弟.辐射源细微特征用于个体识别的可行性分析.航天电子对抗.2008,24(2)：40-42.
[74]许丹,姜文利,周一宇等.雷达功放正弦激励下的无意调制特征分析.系统工程与电子技术.2008,30(3)：399-403.
[75]张国柱,周一宇,姜文利.基于贝叶斯理论的辐射源分类识别方法研究.信号处理.2004,20(4)：350-352.
[76]张国柱,姜文利,周一宇.基于神经网络的辐射源识别系统设计.系统工程与电子技术.2004,26(2)：268-272.
[77]Dudczyk J, Matuszewski J, Wnuk M. Applying the radiated emission to the specific emitter identification. Proceedings of 15th International Conference on Microwaves, Radar and Wireless Communications, Skierniewice, Poland.2004,2:431-434.
[78]肖先赐.电子侦察中的关键技术.电子对抗.1991,(4)：1-6.
[79]Roe J. A review applications of artificial intelligence techniques to navel ESM signal processing. Proceedings of IEE Colloquium on the Applications of Artificial Intelligence Techniques to Signal Processing. 1989,5/1-5/5.
[80]Hopfield JJ. Neural networks and physical systems'with emergent collective computational abilities. Proc. Natl. A-cad. Sci. USA.1982, (79):2254-2258.
[81]Hinton GE, Sejuowski TJ, AckleyDH. Boltzmann machines:cotraint satisfaction networks that learn. Carnegie-Mellon University, Tech, Report CMU-CS-84-119.1984:1-44.
[82]Hinton GE, Sejuowski TJ. Learning and relearning in boltzmann machines. In Parallel Distributed Processing:Explorations in the Microstructure of Cognition. MIT Press, MA.1986,1:282-317.
[83]Werbos P. Backpropagation:past and future. The Proceedings of the IEEE International Conferenceon Neural Networks.1988,343-353.
[84]Parker D. Learning logic invention report S81-64, File 1. Office of Technology Licensing, Stanford University,1982.
[85]Roe A L. Artificial neural networks for ESM emitter identification-aninitial study. Proceedings of IEE Colloquium on Neural Networks for Systems:Principles and Applications.1991,4/1-4/3.
[86]Roe J. Pudner A. The real-time implementation of emitter identification for ESM. Proceedings of IEE Colloquium on Signal Processing in Electronic Warfare.1994,7/1-7/6.
[87]梁百川.神经元网络用于辐射源分选、识别.航天电子对抗.1994,(2)：34-40.
[88]王建华,赵莉萍,虞平良等.模糊神经网络的舰载雷达辐射源识别方法.哈尔滨理工大学学报.1999,14(2)：67-69.
[89]唐斌,胡光锐.基于免疫RBF网络的雷达信号分类识别.数据采集与理.2002,17(4)：371-375.
[90]Shieh C S, Lin C T. A vector neural network for emitter identification. IEEE Trans, on Antennas and Propagation.2002,50(8):1120-1127.
[91]Chen Ting, Luo Jingqing, Shen Bing. Research on rough set-neural network and its application in radar signal recognition. The 8th International Conference on Electronic Measurement and Instruments, Xi'an.2007:3-764-3-768.
[92]Ford B P, Middlebrook V S. Using a knowledge based system for emitter classification and ambiguity resolution. Proceedings of the IEEE National Aerospace and Electronics Conference, NAECON.1989:1739-1746.
[93]Matuszewski J, Paradowski L. The knowledge based approach for emitter identification. Proceedings of 12th International Conference on Microwaves and Radar, Krakow, Poland.1998:810-814.
[94]陈锡明,祝正威,卢显良.新型雷达辐射源识别专家系统的研究与实现.系统工程与电子技术.2000,22(8)：58-62.
[95]孙寒星,黄洁,赵拥军.雷达辐射源识别专家系统中的推理机设计现代雷达.2005,27(1)：10-13.
[96]S.A.Hassan, A.I.Bhatti, A.Latif. Emitter recognition using fuzzy inference system. IEEE International Conference on Emerging Technologies, Islamabad.2005:204-208.
[97]姜宁,胡维礼,孙翱.辐射源威胁等级判定的模糊多属性方法.兵工 学报.2004,25(1)：56-59.
[98]郭小宾,王壮,胡卫东.基于贝叶斯网络分类器的雷达辐射源识别方法.火力与指挥控制.2006,31(2)：36-39.
[99]郑孝勇,姚景顺.一种雷达信号模糊模式识别方法.舰船电子对抗.2001,6：12-13.
[100]周旭,姜双章.雷达辐射源识别的算法研究.指挥控制与仿真.2007,29(5)：36-40.
[101]黄小毛,郑孝勇,章新华.基于D-S推理的雷达信号模糊识别方法.现代雷达.2002,4：7-9.
[102]西奥多里德斯等著.模式识别.李晶皎等译.第3版.北京：电子工业出版社,2006.
[103]余志斌,金炜东,张葛祥.基于峰度的盲源分离算法研究与应用.电波科学学报.2008,23(1)：146-152.
[104]边肇祺,张学工等编著.模式识别.第2版.北京：清华大学出版社,2000.
[105]马晓岩,向家彬,朱裕生等编著.雷达信号处理.湖南科学技术出版社,1999.
[106]L.Sun, W.Kinsner. Fractal segmentation of signal from noise for radio transmitter fingerprinting. Digital Object Identifier.1998,2:561-564.
[107]Jeyanthi Hall, Michel Barbeau, Evangelos Kranakis. Detection of transient in radio frequency fingerprinting using signal phase. Pro-ceedings Wireless and Optical Communications.2003,13-18.
[108]Richard G. Wiley著.电子情报雷达信号分析.胡来招译.第2版.成都：电子集团第二十九研究所,2006.
[109]Kawalec A, Owczarek R. Specific emitter identification using intrapulse data. European Radar Conference, Amsterdam.2004,249-252.
[110]Kawalec A, Owczarek R. Radar emitter recognition using intrapulse data. Microwave& Radar Week In Poland, MICON 2004. Warsaw,2004,2: 435-438.
[111]Young Wan Kim and Jae Du Yu. Phase noise model of single loop frequency synthesizer. IEEE Transactions on Broadcasting.2008,54(1): 112-118.
[112]Venceslav F. Kroupa. Noise properties of PLL systems. IEEE Transactions on Communications.1982,30(10):2244-2252.
[113]李峥.情报综合中相关门限的统计分析.电子对抗技术.2001,16(6)：31-35.
[114]宋力平.一种码分、频分和时分雷达信号.上海航天.1997,4：20-25.
[11 5]靳凯,王卫东,王东进.一种脉内相位编码脉间步进频雷达信号的研究.中国科学技术大学学报.2006,36(2)：137-142.
[116]电子情报研究报告——信息战及其装备技术发展研究.电子工业部科学技术情报研究所,1 998.
[117]Elbirt A J. Information warfare:are you at risk. IEEE Technology and Society Magazine.2003,22(4):13-19.
[118]姜秋喜,祁建清,黄建冲.雷达信号细微特征分析器研究.中国电子学会电子对抗分会第十一届学术年会论文集.1999：521-523.
[119]Zhang G X, Hu L Z, Jin W D. Resemblance coefficient based intrapulse feature extraction approach for radar emitter signals. Chinese Journal of Electronics.2005,14(2):337-341.
[120]S. Mallat, A wavelet tour of signal processing. San Diego, CA:Academic, 1998.
[121]郁春来,万建伟,徐如海,韩彦明等.改进小波脊线法算法分析和仿真.现代雷达.2005,27(8)：46-48.
[122]F.J.Owens, M.S.Murray. A short-time Fourier transform. Signal Processing,1988,14(1):3-10.
[123]Morlet, J. Sampling theory and wave jprogagation. In NATO ASI Series, Issues in Acoustic Signal/Image Processing and Recognition,1983, 1:233-261.
[124]P. Goupillaud, A. Grossman, and J. Morlet. Cycle-octave and related transform in seismic signal analysis. Geoexploration,1984,23:85-102.
[125]Namias V. The fractional Fourier transform and its application in quantum mechanics. J Inst ApplMath,1980,25(1):241-265.
[126]T. Claasen, W.Mecklenbruker. The Wigner distribution-a tool for time-frequency signal analysis; Part 2:Discrete-Time Signals. Philips J. Res.,1980,35,4/5:276-300.
[127]BOUALEM B Estimating and interpreting the instantaneous frequency of a signal-part Ⅱ algorithms and appkication. Proceedings of the IEEE. 1992,80(4):540-568.
[128]Ivanovic V. N., Dakovic M., Stankovic L. Performance of quadratic time-frequency distributions as instantaneous frequency estimators. IEEE Trans. Signal Processing.2003,51(1):77-89.
[129]GUSTAVO L R, JESUS G, ALVORA SO. Digital channelized receiver based on time-frequency analysis for signal interception. IEEE Trans Aerospace and Electronic Systems.2005,41(3):879-898.
[130]Carmona R. Wen L H. Torrdsani B. Multi-ridge detection and time-frequency reconstruction. California:University of California at Irvine,1995.
[131]Helene L, Catherine M. Ridge extraction from the scalogram of the uterine electromyogram. Proceedings of the IEEE-SP International Symposium. Pittsburgh,1998:245-248.
[132]Heng L, Alexander N. Moire interferogram phase extraction:a ridge detection algorithm for continuous wavelet transform. Applied Optics. 2004,43(4):850-857.
[133]Haase M, Widjajakusuma J. Damage identification based on ridges and maxima lines of the wavelet transform. International Journal of Engineering Science.2003,41:1423-1443.
[134]K.C.Ho, W.Prokopiw and Y.T.Chan. Modulation identification of digital signals by the wavelet transform. IEEE Proc-Radar Navig.2000,147(4), pp:169-176.
[135]Nengheng Z, Ching, P.C., Lee, T. Time-frequency analysis of vocal source signal for speaker recognition. The 8th International Conference on Spoken Language Processing, Jeju.2007,12(3):273-290.
[136]C. Ioana, A. Quinquis, Y.Stephan. Feature extraction from underwater signals using time-frequency warping operators. IEEE Journal of Oceanic Engineering.2006,26,1-25.
[137]张贤达,保铮.非平稳信号分析与处理.北京：国防工业出版社,1998.
[138]Ibars, C. Bar-Ness, Y Analysis of time-frequency duality of MC and DS CDMA for multiantenna systems on highly time-varying and wide-band channels. IEEE Transactions on Wireless Communications.2005,4(6): 2661-2667.
[139]J.Kilby, H.Gholam Hosseini. Wavelet analysis of surface electromyography signals. Proceedings of the 26th Annual International Conference of the IEEE EMBS San Francisco,2004. CA, USA.2004: 384-387.
[140]R.R.Coifman, Y. Meyer. Orthonormal wavelet packet bases. Preprint Yale Univ.,1989.
[141]R.R.Coifman, Y. Meyer, and M. V. Wickerhauser. Wavelet analysis and signal processing. Boston,1992.
[142]E. Avci, Z. H. Akpolat. Speech recognition using a wavelet packet adaptive network based fuzzy inference system. Expert Systems with Applications.2006,31:495-503.
[143]S. Ekici, S. Yildirim, M. Poyraz. Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition. Expert Systems with Applications. 34(2008):2937-2944.
[144]Jian-Da Wu, Bing-Fu Lin. Speaker identification using discrete wavelet packet transform technique with irregular decomposition. Expert Systems with Applications.2008,1-8.
[145]Ye Z G, Wu B, Sadeghian A.Current signature analysis of induction motor mechanical fault by wavelet packet decomposition. IEEE Transaction on Industrial Electronics.2003,50(6):1217-1228.
[146]静远,张冰,蒋兴舟.基于小波变换的特征提取方法分析.信号处理.2000,16(2)：156-162.
[147]李世玲,李治,李合生.基于小波包能量特征的滚动轴承故障监测方法.系统仿真学报.2003,15(1)：76-83.
[148]HuangNE, ShenZ, LongSR etal. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non stationary time series analysis. ProcRSocLondA,1998,454(1971):899-995.
[149]ChuJan, GaiQiang, ZhangHaiyong. The EMD Method and Its Use in Ship Diagnosis. Proceeding of the International Symposium on Test and Measurement. China,2003,3:2039-2042.
[150]AP phillipssc, Gledhill R J, Essex J W, etal. Application of the Hilert Huang transform to the analysis of molecular dynamics simulations. Journal of Physical Chemistry, A(2003),107(24):4869-4876.
[151]王首勇,朱光喜,唐远炎.应用最优小波包变换的特征提取方法.电子学报.2003,31(7)：1035-1038.
[152]Hu X G, Wang F, Zhao H L, etal.The mechanical fault diagnosis for HV breakers on the wavelet packet analysis. Proceedings of Instrumentation and Measurement Technology Conference, Colorado.2003:415-419.
[153]彭启琮,邵怀宗,李明奇.信号分析.北京：电子工业出版社,2006.
[154]潘继飞,姜秋喜,毕大平,莫翠琼,章根龙.数字自相关技术在LPI雷达信号检测中的应用.电子对抗技术.2003,1 8(6)：29-32.
[155]潘继飞,姜秋喜,毕大平.基于细节分量提取的脉压雷达信号检测技术.电子信息对抗技术.2006,3(21)：16-20.
[156]飞思科技产品研发中心编著.小波分析理论与MATLAB7实现.北京：电子工业出版社,2005.
[157]LIN Jing. Feature extraction based on morlet wavelet and its application for mechanical fault diagnosis. Journal of Sound and Vibration.2000, 234(1):135-148.
[158]郑治真,沈萍等.小波变换及基matlab工具的应用.北京：地震出版社,2000.
[159]A.Jamin, P.Mahonen. Wavelet packet modulation for wireless communication. Wireless Communications and Mobile Computing Journal.2005,5(2):1-18.
[160]R. Sarikaya, B. L. Pellom, H. L. Hansen. Wavelet packet transform features with application to speaker identification. In Proc. IEEE Nordic Signal processing Symposium, Visgo, Denmark.1998:81-84.
[161]I.T.Jolliffe, Principal component analysis. The 2ndEd., Springer Press, 2002.
[162]K.-T. Kim, I.-S. Choi, and H.-T.Kim. Efficient radar target classification using adaptive joint time-frequency processing. IEEE Trans. Antennas Propagat.2000,48(12):1789-1801.
[163]Jollife I T. Principal components aanalysis. Springer-Verlag,1986.
[164]Daw CS, Finney CEA, Kennel MB. Symbolic approach for measuring temporal'irreversibility'. Physical Review E.2000,62(2):1912-1921.
[165]黄知涛,周一宇,姜文利.一种相位编码序列恢复方法.信号处理,2002,1 8(2)：141-146.
[166]魏跃敏,黄知涛,王丰华等.一种PSK信号相位编码调制规律分析方法.电子对抗技术,2005,20(4)：23-27.
[167]余志斌,金炜东,张葛祥.基于局域波分解的雷达辐射源信号时频分析.计算机应用与研究,2008,25(10)：312-316.
[168]Langley L E. Specific emitter identification (SEI) and classical parameter fusion technology. W ESCON.1993,93:377-381.
[169]Kawalec Adam, Owczarek Robert. Specific emitter identification using intrapulse data. European Radar Conference, Amsterdam,2004:249-252.
[170]J. Dudczyk, M.Wnuk. The utilization of unintentional radiation for identification of the radiation sources. The 34th European Radar Conference, Amsterdam,2004:777-780.
[171](美)杰里.L.伊伏斯,爱德华.K.里迪编.现代雷达原理.卓荣邦等译.北京：工业出版社,1991.
[172]林强,张祖荫,郭伟.微波功率放大器非线性失真分析.微波学报.2004,20(4)：79-82.
[173]Pozar,D.M.著.微波工程.张肇仪等译.北京：电子工业出版社,2006.
[174]Ming-Wei L, John F. D. Specific emitter identification using nonlinear device estimation. IEEE Sarnoff Symposium, Princeton.2008:1-5.
[175]William H. Tranter K. Sam Shanmugan Theodore S.Rappaport Kurt L. Kosbar principles of communication system simulation with wireless applications. Prentice Hall Professional Technical Reference Upper Saddle River, New Jersey,3003.
[176]Arif Ahmed, Syed S Islam, Anwar A F. A temperature dependent nonlinear analysis of GaN/AlGaN HEMTs using Volterra series. IEEE Trans Microwave Theory Tech.2001,49 (9):1518-1523.
[177]M. B. Steer and P. J. Khan, An algebraic formula for the output of a system with large-signal, multifrequency excitation. Proc. IEEE.1983, 71(1):177-179.
[178]Kevin G. Gard, Hector M. Gutierrez, Michael B. Steer. Characterization of spectral regrowth in microwave amplifiers based on the complex Gaussian process. IEEE Transactions on Microwave Theory and Techniques 1999. 47(7):1059-1067.
[179]R.E.Ziemer and W.H. Tranter, Principles of Communications. New York: Wiley,1995.
[180]Corinna Cortes, V.Vapnik. Support-Vector Network. Machine Learning. 1995,20:273-297.
[181]Nandi A.K., Azzouz E.E. Automatic analogue modulation recognition. Signal Processing.1995,46(2):211-222.
[182]Leonid I., Perlovsky, William H. Schoendorf, Bernard J. Burdick, David M. Tye. Model-based neural network for target detection in SAR imagery. IEEE Trans. Image Processing.1997,6(1):203-215.
[183]N.Merhav, Y. Ephraim. A Bayesian classification approach with application to speech recognition. IEEE Trans. Signal Process.1991, 39(10):2157-2166.
[184]Rueda L, Oommen B J. On optimal pairwise linear classifiers for normal distributions:the d-dimensional case. Pattern Recognition.2003, 36:13-23.
[185]Rueda L. Selecting the best hyperplane in the framework of optimal pairwise linear classifiers. Pattern Recognition Letter.2004,25:49-62.
[186]Pernkopf F. Bayesian network classifiers versus selective k-NN classifier. Pattern Recognition.2005,38(1):1-10.
[187]Domeniconi C, Peng J, Gunopulos D. Locally adaptive metric nearnest-neighbour classification. IEEE Transactions on Pattern Analysis and Machine Intelligence.2002,24(9):1281-1285.
[188]Gonzalez-Barrios J M, Quiroz A J. A clustering procedure based on the comparison between the k nearest neighbors graph and the minimal spanning tree. Statistics & Probability Letters.2003,62(1):23-34.
[189]Pedrycz W, Sosnowski Z A. Genetically optimized fuzzy decision trees. IEEE Transactions on System, Man, and Cybernetics-Part B:Cybernetics. 2005,35(3):633-641.
[190]Vapnik, V. The nature of statistical learning theory. New York: Springer-Verlag,1995,5-13.
[191]Trent M C, Henry L, Eloi B, Vinay V. Classification of audio radar signals using radial basis function neural networks. IEEE Transactions on Instrumentation and Measurement.2003,52(6):1771-1779.
[192]Granger E, Rubin M A, Grossbert S, Lavoie P. A what-and-where fusion neural network for recognition and tracking of multiple radar emitters. Neural Networks.2001,14:325-344.
[193]Simon H.著.神经网络原理.叶世伟,史忠植译.第2版.北京：机械工业出版社,2004.
[194]靳蕃.神经计算智能基础原理方法.成都：西南交通大学出版社,2000.
[195]Chakrabarti S, Bindal N, Theagharajan K. Robust radar target classifier using artificial neural networks. IEEE Trans. on Neural Networks.1995, 6(3):760-766.
[196]Trent McConaghy, Henry Leung, Eloi Bosse, Vinay Varadan. Classification of audio radar signals using radial basis function neural networks. IEEE Transactions on Instrumentation and Measurement.2003, 52(6):1771-1779.
[197]N.A. Syed, H. Liu and K.K. Sung. Incremental learning with support vector machines. In Proc. Int. Joint Conf. on Artificial Intelligence (IJCAI-99),1999.
[198]C.P.Diehl, GCauwenberghs. SVM incremental learning adaptation and optimization. Proceedings of the International Joint Conference on Neural Networks, Portland.2003,4:2685-2690.
[199]Shinya Katagiri, Shigeo Abe. Incremental training of support vector machines using hyperspheres. Pattern Recognition Letters.2006,27, 1495-1507.
[200]Mulier F. Vapnik-Chervonenkis(VC). Learning theory and its applications. IEEE Trans, on Neural Networks.1999,10(5):985-987.
[201]VladimirN. Vapnik著.统计学习理论的本质.张学工译.北京：清华大学出版社,2000.
[202]Ovidiu Ivanciuc. Applications of support vector machines in chemistry. Reviews in Computational Chemistry.2007,23,291-400.
[203]白鹏,李彦,张斌,刘君华等.基于SVM的混合气体红外光谱分析关键技术研究.光子学报.2008,37(3)：566-572.
[204]S Tong, D Koller. Support vector machine active learning with applications to text classification. Journal of Machine Learning Research. 2001,45-66.
[205]Ratsaby, J. Incremental learning with sample queries. IEEE Transactions on Pattern Analysis and Machine Intelligence.1998,20(8):883-888.
[206]Yamauchi, K., Yamaguchi, N., Ishii, N. Incremental learning methods with retrieving of interfered patterns. IEEE Transactions on Neural Networks.1999,10 (11):1351-1365.
[207]ZengW H,Ma J. A novel approach to incremental SVM learning algorithm. Journal of Xiamen University:NaturalScience.2002,41(6):687-691.
[208]孔锐,张冰.一种快速支持向量机增量学习算法.控制与决策.2005,(10)：1129-1130.
[209]Chun-Fu Lin and Sheng-De Wang. Fuzzy support vector machines. IEEE Transactions on Neural Networks.2002, (13):464-471.
[210]D.M.J. Tax, and R.P.W. Duin. Outliers and data descriptions. Proc. ASCI 2001,7th Annual Conf. of the Advanced School for Computing and Imaging (Heijen,NL,May 30-June 1), ASCI, Delft,2001:234-241.
[211]Xiufeng Jiang Zhang Yi Jian Cheng Lv. Fuzzy SVM with a new fuzzy membership function. Neural Comput & Applic.2006,15:268-276.
[212]UduPa J K, Samarasekera S. Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. Graphical Model and Image Processing.1995,58(3):246-261.
[213]张翔,肖小玲,徐光祜.模糊支持向量机中隶属度的确定与分析.中国图象图形学报.2006,11(8)：1188-1192.
[214]刁鸣.雷达对抗技术.哈尔滨：哈尔滨工程大学出版社,2005.
[2]Schroer R. Electronic warefare. IEEE Trans Aerospace Electronic Systems Magazine.2003,18(7):49-54.
[3]Collins J H, Grant P M. A review of current and future components for electronic warfare receivers. IEEE Transcations on Sonics and Ultrasonics. 1981,28(3):117-125.
[4]林象平.雷达对抗原理.西安：西北电讯工程学院出版社,1985.
[5]Lighart V A, Logvin A I.A survey of radar ECM and ECCM. IEEE Transactions on Aerospace and Electronic Systems.1995,31(3): 1110-1120.
[6]林锋.电子对抗.北京：科学出版社,1987.
[7]赵国庆.雷达对抗原理.西安：西安电子科技大学出版社,1999.
[8]Richard G W. Electronic intelligence:the analysis of radar signals. The 2nd ed., Norwood, MA:Artech House Inc.,1993.
[9]戚世权,姜秋喜.电子战数字接收机及其信号处理.航天电子对抗.1994,(4)：47-50.
[10]Granger E, Rubin M A, Grossberg, et al. A what-and where fusion neural network for recognition and tracking of multiple radar emitters. Neural Networks.2001,14(3):325-344.
[11]Lee J P Y. A multi-channel digital receiver for intrapulse analysis and direction-finding. Proceedings of IEEE Pacific Rim Conference on Communication, Computers and Signal Processing, Canada. 1999:589-592.
[12]Elbirt A J. Information warfare:are you at risk. IEEE Technology and Society Magazine.2003,22(4):13-19.
[13]李祖新.雷达对抗面临严重挑战.舰船电子对抗.1999,1：5-8.
[14]斯科尼克主编.雷达手册.王军强等译.第2版.北京：电子工业出版社,2003.
[15]谭显裕.21世纪海空电子战特征及发展趋势.雷达与对抗.1994,2：25-29.
[16]Stove A G, Hume A L and Baker C J. Low probability of intercept radar strategies. IEEE Proc. Radar Sonar Navig.2004,151(5):249-260.
[17]王乃和.关于ESM中雷达信号分选识别问题的探讨.电子对抗.1991,25(3)：44-51.
[18]解文斌,姜文利,黄勇杰等.脉冲编码信号的辐射源分类.电子对抗技术.2004,1 9(3)：23-29.
[19]胡波.脉内特征提取在信号调制形式识别中的应用.雷达与对抗.2005,2：35-38.
[20]王勇,张欣.雷达信号脉内特征参数提取技术.中国雷达.2006,1：13-21.
[21]桑炜森,顾耀平.综合电子战新技术新方法.北京：国防工业出版社,1996.
[22]Therrien C W. Application of feature extraction to radar signature classification. Proceedings of the 2th Interational Pattern Recognition Symposium.1974,125-132.
[23]束坤,盛九朝.ESM系统与低截获概率雷达之争.舰船电子对抗.2005,28(3)：11-13.
[24]Davies C L, Hollands H. Automatic processing for ESM. IEE Proceedings, Part F:Radar& Signal Process.1982,129(3):146-171.
[25]Andrews R S. ESM processing using 3D memory mapping and adaptive pattern formation algorithms. Military Microwaves Transactions.1984, 27-36.
[26]Mardia H K. Adaptive multi-dimensional clustering for ESM. Proceedings of IEE Colloquium on Signal Processing for ESM Systems.1988,5/1-5/4.
[27]Langley L E. Specific emitter identification (SEI) and classical parameter fusion technology. Proceedings of the WESCON.1993,377-381.
[28]徐欣,周一宇,卢启中.雷达截获系统实时信号分选处理技术研究.系统工程与电子技术.2001,23(3)：12-15.
[29]王海.电子战ESM系统技术发展综述.飞航导弹.2006,1：60-62.
[30]Danielsen P L, Agg D A, Burke N R. The application of pattern recognition techniques to ESM data processing. IEE Colloquium on Signal Processing for ESM Systems.1988,6/1-6/4.
[31]Agg D A. The development of an ESM data processing scheme based on pattern recognition techniques. Proc. of IEE Colloquium on Electronic Warfare Systems.1991,5/1-5/4.
[32]Perdriau B. Modulation domain offers a new view of radar performance. MSN.1990, (4):27-43.
[33]穆世强.雷达信号脉内细微特性分析.电子对抗.1991,2：28-37.
[34]张葛祥.雷达辐射源信号智能识别方法研究[PHD].成都：西南交通大学,2005.
[35]Delpart N. Asymptotic wavelet and Gabor analysis:extraction of instantaneous frequencies. IEEE Trans Information Theory.1992, 38(3):644-664.
[36]Liu W K, Zhu D J, Zhang C H. The extraction of modulation characteristics of radar signal using wavelet transform. Proceedings of the 4th International Conference on Signal Processing, Bei'jin.1998:288-291.
[37]梁百川.利用小波变换识别脉内调制信号.电子对抗.1998,3：1-12.
[38]郁春来,何明浩,斐立志.基于改进小波脊线法的LFM信号脉内特征提取.航天电子对抗.2004,4：38-42.
[39]曲长文,乔治国.雷达信号脉内特征的小波分析.上海航天.1996,5：15-19.
[40]魏东升,徐东晖,林象平.雷达信号脉内细微特征的时频分析.电子对抗.1993,4：7-13.
[41]赵拥军,黄洁.雷达信号细微特征时频分析法.现代雷达.2003,25(12)：26-28.
[42]Moraitakis I, Fargues M P. Feature extraction of intra-pulse modulated signals using time-frequency analysis. Proceedings of 21st Century Military Communications Conference, Los Angeles.2000:737-741.
[43]柳征,姜文利,周一宇.基于小波包变换的辐射源信号识别.信号处理.2005,21(5)：460-464.
[44]张葛祥,荣海娜,金炜东.基于小波包变换和特征选择的雷达辐射源信号识别.电路与系统学报.2006,6(11)：45-49.
[45]朱明,普运伟,金炜东,胡来招.基于时频原子方法的雷达辐源信号特征提取.电波科学学报.2007,22(3)：458-462.
[46]Jarmo Lunden, Visa Koivunen. Automatic radar waveform recognition. IEEE Journal of Selected Topics in Signal Processing.2007,1(1):124-136.
[47]T.Q.Gulum, P.E.Pace and R.Cristi. Extraction of polyphase radar modulation parameters using a wigner-ville distribution-radon transform. U.S.Government Work not Protected by U.S.Copyritht.2008,1505-1508.
[48]Ray P S. Radar waveform modulation recognition by neural processing. Proceedings of International Symposium on Signal Processing and Its Applications.1996,121-124.
[49]毕大平,董晖,姜秋喜.基于瞬时频率的脉内调制识别技术.电子对抗技术.2005,20(2)：6-9.
[50]普运伟,金炜东,胡来招.基于瞬时频率二次特征提取的辐射源信号分类.西南交通大学学报.2007,42(3),373-379.
[51]Roome S J. Classification of radar signals in modulation domain. Electronics Letters.1992,28(8):704-705.
[52]黄知涛,周一宇,姜文利.基于相对无模糊相位重构的自动脉内调制特性分析.通信学报.2003,24(4)：153-160.
[53]Trent McConaghy, Henry Leung, Eloi Bosse, Vinay Varadan. Classification of audio radar signals using radial basis function neural networks. IEEE Transactions on Instrumentation and Measurement.2003, 52(6):1771-1779.
[54]Zhang G X, Hu L Z, Jin W D. Resemblance coefficient and a quantum genetic algorithm for feature selection. Lecture Notes in Artificial Intelligence.2004,3245:155-168.
[55]Zhang G X, Jin W D, Hu L Z. Radar emitter signal recognition based on complexity feature. J. of Southwest Jiaotong Univ.2004,12(2):116-122.
[56]张葛祥,胡来招,金炜东.雷达辐射源信号脉内特征分析.红外与毫米波学报.2004,23(6)：478-480.
[57]张葛祥,胡来招,金炜东.基于熵特征的雷达辐射源信号识别.电波科学学报.2005,20(4)：440-445.
[58]张国柱.雷达辐射源识别技术研究[PHD].长沙：国防科技大学,2005.
[59]陈东锋,雷英杰,潘寒尽.基于模糊加权法的雷达辐射源识别.现代防御技术.2005,33(6)：57-59.
[60]Kenneth I. Talbot, Paul R. Duley, Martin H. Hyatt. Specific emitter identification and verification. Technology Review Journal Spring/Summer.2003:113-133.
[61]Kawalec Adam, Owczarek Robert. Specific emitter identification using intrapulse data. European Radar Conference, Amsterdam.2004:249-252.
[62]Kawalec A, Owczarek R. Radar emitter recognition using intrapulse data. Microwave& Radar Week in Poland, MICON 2004. Warsaw,2004,2: 435-438.
[63]J. Dudczyk, M. Wnuk, The utilization of unintentional radiation for identification of the radiation sources. The 34thEuropean Radar Conference, Amsterdam,2004:777-780.
[64]Carroll T L. A nonlinear dynamics method for signal identification. Chaos. 2007,17:023109.
[65]Matuszewski J, Paradowski L. The knowledge based approach for emitter dentification. In Microwaves and Radar, MIKON'98,12th International Conference.1998,3(5):810-814.
[66]Matuszewski J, Kawalec A. Knowledge-based signal processing for radar identification. The 9th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science TCSET'2008. Ukraine, Lviv-Slavsko,2008,302-305.
[67]L.Anjaneyulu, N.S.Murthy, N.V.S.N.Sarma. Radar emitter classification using self-organising neural network models. Proceedings of International Conference on Microwave.2008,431-433.
[68]董晖,姜秋喜.基于多脉冲的雷达个体识别技术.电子对抗.2006,6：12-18.
[69]姜秋喜,董晖基于单脉冲的雷达个体识别技术.舰船电子对抗.2006,29(4)：60-66.
[70]陈昌孝,何明浩,王志斌,高峰.基于双谱分析的雷达辐射源个体特征提取.航天电子对抗.2007,23(5)：46-49.
[71]Tao-wei Chen, Wei-dong Jin, Jie LI. Feature extraction using surrounding-line integral bispectrum for radar emitter signal. International Joint Conference on Neural Networks, Hong Kong.2008:294-298.
[72]潘继飞,姜秋喜,毕大平.雷达“指纹”参数选取.现代防御技术.2007,35(1),71-75.
[73]刘庆云,陆飞飞,朱伟强,王根弟.辐射源细微特征用于个体识别的可行性分析.航天电子对抗.2008,24(2)：40-42.
[74]许丹,姜文利,周一宇等.雷达功放正弦激励下的无意调制特征分析.系统工程与电子技术.2008,30(3)：399-403.
[75]张国柱,周一宇,姜文利.基于贝叶斯理论的辐射源分类识别方法研究.信号处理.2004,20(4)：350-352.
[76]张国柱,姜文利,周一宇.基于神经网络的辐射源识别系统设计.系统工程与电子技术.2004,26(2)：268-272.
[77]Dudczyk J, Matuszewski J, Wnuk M. Applying the radiated emission to the specific emitter identification. Proceedings of 15th International Conference on Microwaves, Radar and Wireless Communications, Skierniewice, Poland.2004,2:431-434.
[78]肖先赐.电子侦察中的关键技术.电子对抗.1991,(4)：1-6.
[79]Roe J. A review applications of artificial intelligence techniques to navel ESM signal processing. Proceedings of IEE Colloquium on the Applications of Artificial Intelligence Techniques to Signal Processing. 1989,5/1-5/5.
[80]Hopfield JJ. Neural networks and physical systems'with emergent collective computational abilities. Proc. Natl. A-cad. Sci. USA.1982, (79):2254-2258.
[81]Hinton GE, Sejuowski TJ, AckleyDH. Boltzmann machines:cotraint satisfaction networks that learn. Carnegie-Mellon University, Tech, Report CMU-CS-84-119.1984:1-44.
[82]Hinton GE, Sejuowski TJ. Learning and relearning in boltzmann machines. In Parallel Distributed Processing:Explorations in the Microstructure of Cognition. MIT Press, MA.1986,1:282-317.
[83]Werbos P. Backpropagation:past and future. The Proceedings of the IEEE International Conferenceon Neural Networks.1988,343-353.
[84]Parker D. Learning logic invention report S81-64, File 1. Office of Technology Licensing, Stanford University,1982.
[85]Roe A L. Artificial neural networks for ESM emitter identification-aninitial study. Proceedings of IEE Colloquium on Neural Networks for Systems:Principles and Applications.1991,4/1-4/3.
[86]Roe J. Pudner A. The real-time implementation of emitter identification for ESM. Proceedings of IEE Colloquium on Signal Processing in Electronic Warfare.1994,7/1-7/6.
[87]梁百川.神经元网络用于辐射源分选、识别.航天电子对抗.1994,(2)：34-40.
[88]王建华,赵莉萍,虞平良等.模糊神经网络的舰载雷达辐射源识别方法.哈尔滨理工大学学报.1999,14(2)：67-69.
[89]唐斌,胡光锐.基于免疫RBF网络的雷达信号分类识别.数据采集与理.2002,17(4)：371-375.
[90]Shieh C S, Lin C T. A vector neural network for emitter identification. IEEE Trans, on Antennas and Propagation.2002,50(8):1120-1127.
[91]Chen Ting, Luo Jingqing, Shen Bing. Research on rough set-neural network and its application in radar signal recognition. The 8th International Conference on Electronic Measurement and Instruments, Xi'an.2007:3-764-3-768.
[92]Ford B P, Middlebrook V S. Using a knowledge based system for emitter classification and ambiguity resolution. Proceedings of the IEEE National Aerospace and Electronics Conference, NAECON.1989:1739-1746.
[93]Matuszewski J, Paradowski L. The knowledge based approach for emitter identification. Proceedings of 12th International Conference on Microwaves and Radar, Krakow, Poland.1998:810-814.
[94]陈锡明,祝正威,卢显良.新型雷达辐射源识别专家系统的研究与实现.系统工程与电子技术.2000,22(8)：58-62.
[95]孙寒星,黄洁,赵拥军.雷达辐射源识别专家系统中的推理机设计现代雷达.2005,27(1)：10-13.
[96]S.A.Hassan, A.I.Bhatti, A.Latif. Emitter recognition using fuzzy inference system. IEEE International Conference on Emerging Technologies, Islamabad.2005:204-208.
[97]姜宁,胡维礼,孙翱.辐射源威胁等级判定的模糊多属性方法.兵工 学报.2004,25(1)：56-59.
[98]郭小宾,王壮,胡卫东.基于贝叶斯网络分类器的雷达辐射源识别方法.火力与指挥控制.2006,31(2)：36-39.
[99]郑孝勇,姚景顺.一种雷达信号模糊模式识别方法.舰船电子对抗.2001,6：12-13.
[100]周旭,姜双章.雷达辐射源识别的算法研究.指挥控制与仿真.2007,29(5)：36-40.
[101]黄小毛,郑孝勇,章新华.基于D-S推理的雷达信号模糊识别方法.现代雷达.2002,4：7-9.
[102]西奥多里德斯等著.模式识别.李晶皎等译.第3版.北京：电子工业出版社,2006.
[103]余志斌,金炜东,张葛祥.基于峰度的盲源分离算法研究与应用.电波科学学报.2008,23(1)：146-152.
[104]边肇祺,张学工等编著.模式识别.第2版.北京：清华大学出版社,2000.
[105]马晓岩,向家彬,朱裕生等编著.雷达信号处理.湖南科学技术出版社,1999.
[106]L.Sun, W.Kinsner. Fractal segmentation of signal from noise for radio transmitter fingerprinting. Digital Object Identifier.1998,2:561-564.
[107]Jeyanthi Hall, Michel Barbeau, Evangelos Kranakis. Detection of transient in radio frequency fingerprinting using signal phase. Pro-ceedings Wireless and Optical Communications.2003,13-18.
[108]Richard G. Wiley著.电子情报雷达信号分析.胡来招译.第2版.成都：电子集团第二十九研究所,2006.
[109]Kawalec A, Owczarek R. Specific emitter identification using intrapulse data. European Radar Conference, Amsterdam.2004,249-252.
[110]Kawalec A, Owczarek R. Radar emitter recognition using intrapulse data. Microwave& Radar Week In Poland, MICON 2004. Warsaw,2004,2: 435-438.
[111]Young Wan Kim and Jae Du Yu. Phase noise model of single loop frequency synthesizer. IEEE Transactions on Broadcasting.2008,54(1): 112-118.
[112]Venceslav F. Kroupa. Noise properties of PLL systems. IEEE Transactions on Communications.1982,30(10):2244-2252.
[113]李峥.情报综合中相关门限的统计分析.电子对抗技术.2001,16(6)：31-35.
[114]宋力平.一种码分、频分和时分雷达信号.上海航天.1997,4：20-25.
[11 5]靳凯,王卫东,王东进.一种脉内相位编码脉间步进频雷达信号的研究.中国科学技术大学学报.2006,36(2)：137-142.
[116]电子情报研究报告——信息战及其装备技术发展研究.电子工业部科学技术情报研究所,1 998.
[117]Elbirt A J. Information warfare:are you at risk. IEEE Technology and Society Magazine.2003,22(4):13-19.
[118]姜秋喜,祁建清,黄建冲.雷达信号细微特征分析器研究.中国电子学会电子对抗分会第十一届学术年会论文集.1999：521-523.
[119]Zhang G X, Hu L Z, Jin W D. Resemblance coefficient based intrapulse feature extraction approach for radar emitter signals. Chinese Journal of Electronics.2005,14(2):337-341.
[120]S. Mallat, A wavelet tour of signal processing. San Diego, CA:Academic, 1998.
[121]郁春来,万建伟,徐如海,韩彦明等.改进小波脊线法算法分析和仿真.现代雷达.2005,27(8)：46-48.
[122]F.J.Owens, M.S.Murray. A short-time Fourier transform. Signal Processing,1988,14(1):3-10.
[123]Morlet, J. Sampling theory and wave jprogagation. In NATO ASI Series, Issues in Acoustic Signal/Image Processing and Recognition,1983, 1:233-261.
[124]P. Goupillaud, A. Grossman, and J. Morlet. Cycle-octave and related transform in seismic signal analysis. Geoexploration,1984,23:85-102.
[125]Namias V. The fractional Fourier transform and its application in quantum mechanics. J Inst ApplMath,1980,25(1):241-265.
[126]T. Claasen, W.Mecklenbruker. The Wigner distribution-a tool for time-frequency signal analysis; Part 2:Discrete-Time Signals. Philips J. Res.,1980,35,4/5:276-300.
[127]BOUALEM B Estimating and interpreting the instantaneous frequency of a signal-part Ⅱ algorithms and appkication. Proceedings of the IEEE. 1992,80(4):540-568.
[128]Ivanovic V. N., Dakovic M., Stankovic L. Performance of quadratic time-frequency distributions as instantaneous frequency estimators. IEEE Trans. Signal Processing.2003,51(1):77-89.
[129]GUSTAVO L R, JESUS G, ALVORA SO. Digital channelized receiver based on time-frequency analysis for signal interception. IEEE Trans Aerospace and Electronic Systems.2005,41(3):879-898.
[130]Carmona R. Wen L H. Torrdsani B. Multi-ridge detection and time-frequency reconstruction. California:University of California at Irvine,1995.
[131]Helene L, Catherine M. Ridge extraction from the scalogram of the uterine electromyogram. Proceedings of the IEEE-SP International Symposium. Pittsburgh,1998:245-248.
[132]Heng L, Alexander N. Moire interferogram phase extraction:a ridge detection algorithm for continuous wavelet transform. Applied Optics. 2004,43(4):850-857.
[133]Haase M, Widjajakusuma J. Damage identification based on ridges and maxima lines of the wavelet transform. International Journal of Engineering Science.2003,41:1423-1443.
[134]K.C.Ho, W.Prokopiw and Y.T.Chan. Modulation identification of digital signals by the wavelet transform. IEEE Proc-Radar Navig.2000,147(4), pp:169-176.
[135]Nengheng Z, Ching, P.C., Lee, T. Time-frequency analysis of vocal source signal for speaker recognition. The 8th International Conference on Spoken Language Processing, Jeju.2007,12(3):273-290.
[136]C. Ioana, A. Quinquis, Y.Stephan. Feature extraction from underwater signals using time-frequency warping operators. IEEE Journal of Oceanic Engineering.2006,26,1-25.
[137]张贤达,保铮.非平稳信号分析与处理.北京：国防工业出版社,1998.
[138]Ibars, C. Bar-Ness, Y Analysis of time-frequency duality of MC and DS CDMA for multiantenna systems on highly time-varying and wide-band channels. IEEE Transactions on Wireless Communications.2005,4(6): 2661-2667.
[139]J.Kilby, H.Gholam Hosseini. Wavelet analysis of surface electromyography signals. Proceedings of the 26th Annual International Conference of the IEEE EMBS San Francisco,2004. CA, USA.2004: 384-387.
[140]R.R.Coifman, Y. Meyer. Orthonormal wavelet packet bases. Preprint Yale Univ.,1989.
[141]R.R.Coifman, Y. Meyer, and M. V. Wickerhauser. Wavelet analysis and signal processing. Boston,1992.
[142]E. Avci, Z. H. Akpolat. Speech recognition using a wavelet packet adaptive network based fuzzy inference system. Expert Systems with Applications.2006,31:495-503.
[143]S. Ekici, S. Yildirim, M. Poyraz. Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition. Expert Systems with Applications. 34(2008):2937-2944.
[144]Jian-Da Wu, Bing-Fu Lin. Speaker identification using discrete wavelet packet transform technique with irregular decomposition. Expert Systems with Applications.2008,1-8.
[145]Ye Z G, Wu B, Sadeghian A.Current signature analysis of induction motor mechanical fault by wavelet packet decomposition. IEEE Transaction on Industrial Electronics.2003,50(6):1217-1228.
[146]静远,张冰,蒋兴舟.基于小波变换的特征提取方法分析.信号处理.2000,16(2)：156-162.
[147]李世玲,李治,李合生.基于小波包能量特征的滚动轴承故障监测方法.系统仿真学报.2003,15(1)：76-83.
[148]HuangNE, ShenZ, LongSR etal. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non stationary time series analysis. ProcRSocLondA,1998,454(1971):899-995.
[149]ChuJan, GaiQiang, ZhangHaiyong. The EMD Method and Its Use in Ship Diagnosis. Proceeding of the International Symposium on Test and Measurement. China,2003,3:2039-2042.
[150]AP phillipssc, Gledhill R J, Essex J W, etal. Application of the Hilert Huang transform to the analysis of molecular dynamics simulations. Journal of Physical Chemistry, A(2003),107(24):4869-4876.
[151]王首勇,朱光喜,唐远炎.应用最优小波包变换的特征提取方法.电子学报.2003,31(7)：1035-1038.
[152]Hu X G, Wang F, Zhao H L, etal.The mechanical fault diagnosis for HV breakers on the wavelet packet analysis. Proceedings of Instrumentation and Measurement Technology Conference, Colorado.2003:415-419.
[153]彭启琮,邵怀宗,李明奇.信号分析.北京：电子工业出版社,2006.
[154]潘继飞,姜秋喜,毕大平,莫翠琼,章根龙.数字自相关技术在LPI雷达信号检测中的应用.电子对抗技术.2003,1 8(6)：29-32.
[155]潘继飞,姜秋喜,毕大平.基于细节分量提取的脉压雷达信号检测技术.电子信息对抗技术.2006,3(21)：16-20.
[156]飞思科技产品研发中心编著.小波分析理论与MATLAB7实现.北京：电子工业出版社,2005.
[157]LIN Jing. Feature extraction based on morlet wavelet and its application for mechanical fault diagnosis. Journal of Sound and Vibration.2000, 234(1):135-148.
[158]郑治真,沈萍等.小波变换及基matlab工具的应用.北京：地震出版社,2000.
[159]A.Jamin, P.Mahonen. Wavelet packet modulation for wireless communication. Wireless Communications and Mobile Computing Journal.2005,5(2):1-18.
[160]R. Sarikaya, B. L. Pellom, H. L. Hansen. Wavelet packet transform features with application to speaker identification. In Proc. IEEE Nordic Signal processing Symposium, Visgo, Denmark.1998:81-84.
[161]I.T.Jolliffe, Principal component analysis. The 2ndEd., Springer Press, 2002.
[162]K.-T. Kim, I.-S. Choi, and H.-T.Kim. Efficient radar target classification using adaptive joint time-frequency processing. IEEE Trans. Antennas Propagat.2000,48(12):1789-1801.
[163]Jollife I T. Principal components aanalysis. Springer-Verlag,1986.
[164]Daw CS, Finney CEA, Kennel MB. Symbolic approach for measuring temporal'irreversibility'. Physical Review E.2000,62(2):1912-1921.
[165]黄知涛,周一宇,姜文利.一种相位编码序列恢复方法.信号处理,2002,1 8(2)：141-146.
[166]魏跃敏,黄知涛,王丰华等.一种PSK信号相位编码调制规律分析方法.电子对抗技术,2005,20(4)：23-27.
[167]余志斌,金炜东,张葛祥.基于局域波分解的雷达辐射源信号时频分析.计算机应用与研究,2008,25(10)：312-316.
[168]Langley L E. Specific emitter identification (SEI) and classical parameter fusion technology. W ESCON.1993,93:377-381.
[169]Kawalec Adam, Owczarek Robert. Specific emitter identification using intrapulse data. European Radar Conference, Amsterdam,2004:249-252.
[170]J. Dudczyk, M.Wnuk. The utilization of unintentional radiation for identification of the radiation sources. The 34th European Radar Conference, Amsterdam,2004:777-780.
[171](美)杰里.L.伊伏斯,爱德华.K.里迪编.现代雷达原理.卓荣邦等译.北京：工业出版社,1991.
[172]林强,张祖荫,郭伟.微波功率放大器非线性失真分析.微波学报.2004,20(4)：79-82.
[173]Pozar,D.M.著.微波工程.张肇仪等译.北京：电子工业出版社,2006.
[174]Ming-Wei L, John F. D. Specific emitter identification using nonlinear device estimation. IEEE Sarnoff Symposium, Princeton.2008:1-5.
[175]William H. Tranter K. Sam Shanmugan Theodore S.Rappaport Kurt L. Kosbar principles of communication system simulation with wireless applications. Prentice Hall Professional Technical Reference Upper Saddle River, New Jersey,3003.
[176]Arif Ahmed, Syed S Islam, Anwar A F. A temperature dependent nonlinear analysis of GaN/AlGaN HEMTs using Volterra series. IEEE Trans Microwave Theory Tech.2001,49 (9):1518-1523.
[177]M. B. Steer and P. J. Khan, An algebraic formula for the output of a system with large-signal, multifrequency excitation. Proc. IEEE.1983, 71(1):177-179.
[178]Kevin G. Gard, Hector M. Gutierrez, Michael B. Steer. Characterization of spectral regrowth in microwave amplifiers based on the complex Gaussian process. IEEE Transactions on Microwave Theory and Techniques 1999. 47(7):1059-1067.
[179]R.E.Ziemer and W.H. Tranter, Principles of Communications. New York: Wiley,1995.
[180]Corinna Cortes, V.Vapnik. Support-Vector Network. Machine Learning. 1995,20:273-297.
[181]Nandi A.K., Azzouz E.E. Automatic analogue modulation recognition. Signal Processing.1995,46(2):211-222.
[182]Leonid I., Perlovsky, William H. Schoendorf, Bernard J. Burdick, David M. Tye. Model-based neural network for target detection in SAR imagery. IEEE Trans. Image Processing.1997,6(1):203-215.
[183]N.Merhav, Y. Ephraim. A Bayesian classification approach with application to speech recognition. IEEE Trans. Signal Process.1991, 39(10):2157-2166.
[184]Rueda L, Oommen B J. On optimal pairwise linear classifiers for normal distributions:the d-dimensional case. Pattern Recognition.2003, 36:13-23.
[185]Rueda L. Selecting the best hyperplane in the framework of optimal pairwise linear classifiers. Pattern Recognition Letter.2004,25:49-62.
[186]Pernkopf F. Bayesian network classifiers versus selective k-NN classifier. Pattern Recognition.2005,38(1):1-10.
[187]Domeniconi C, Peng J, Gunopulos D. Locally adaptive metric nearnest-neighbour classification. IEEE Transactions on Pattern Analysis and Machine Intelligence.2002,24(9):1281-1285.
[188]Gonzalez-Barrios J M, Quiroz A J. A clustering procedure based on the comparison between the k nearest neighbors graph and the minimal spanning tree. Statistics & Probability Letters.2003,62(1):23-34.
[189]Pedrycz W, Sosnowski Z A. Genetically optimized fuzzy decision trees. IEEE Transactions on System, Man, and Cybernetics-Part B:Cybernetics. 2005,35(3):633-641.
[190]Vapnik, V. The nature of statistical learning theory. New York: Springer-Verlag,1995,5-13.
[191]Trent M C, Henry L, Eloi B, Vinay V. Classification of audio radar signals using radial basis function neural networks. IEEE Transactions on Instrumentation and Measurement.2003,52(6):1771-1779.
[192]Granger E, Rubin M A, Grossbert S, Lavoie P. A what-and-where fusion neural network for recognition and tracking of multiple radar emitters. Neural Networks.2001,14:325-344.
[193]Simon H.著.神经网络原理.叶世伟,史忠植译.第2版.北京：机械工业出版社,2004.
[194]靳蕃.神经计算智能基础原理方法.成都：西南交通大学出版社,2000.
[195]Chakrabarti S, Bindal N, Theagharajan K. Robust radar target classifier using artificial neural networks. IEEE Trans. on Neural Networks.1995, 6(3):760-766.
[196]Trent McConaghy, Henry Leung, Eloi Bosse, Vinay Varadan. Classification of audio radar signals using radial basis function neural networks. IEEE Transactions on Instrumentation and Measurement.2003, 52(6):1771-1779.
[197]N.A. Syed, H. Liu and K.K. Sung. Incremental learning with support vector machines. In Proc. Int. Joint Conf. on Artificial Intelligence (IJCAI-99),1999.
[198]C.P.Diehl, GCauwenberghs. SVM incremental learning adaptation and optimization. Proceedings of the International Joint Conference on Neural Networks, Portland.2003,4:2685-2690.
[199]Shinya Katagiri, Shigeo Abe. Incremental training of support vector machines using hyperspheres. Pattern Recognition Letters.2006,27, 1495-1507.
[200]Mulier F. Vapnik-Chervonenkis(VC). Learning theory and its applications. IEEE Trans, on Neural Networks.1999,10(5):985-987.
[201]VladimirN. Vapnik著.统计学习理论的本质.张学工译.北京：清华大学出版社,2000.
[202]Ovidiu Ivanciuc. Applications of support vector machines in chemistry. Reviews in Computational Chemistry.2007,23,291-400.
[203]白鹏,李彦,张斌,刘君华等.基于SVM的混合气体红外光谱分析关键技术研究.光子学报.2008,37(3)：566-572.
[204]S Tong, D Koller. Support vector machine active learning with applications to text classification. Journal of Machine Learning Research. 2001,45-66.
[205]Ratsaby, J. Incremental learning with sample queries. IEEE Transactions on Pattern Analysis and Machine Intelligence.1998,20(8):883-888.
[206]Yamauchi, K., Yamaguchi, N., Ishii, N. Incremental learning methods with retrieving of interfered patterns. IEEE Transactions on Neural Networks.1999,10 (11):1351-1365.
[207]ZengW H,Ma J. A novel approach to incremental SVM learning algorithm. Journal of Xiamen University:NaturalScience.2002,41(6):687-691.
[208]孔锐,张冰.一种快速支持向量机增量学习算法.控制与决策.2005,(10)：1129-1130.
[209]Chun-Fu Lin and Sheng-De Wang. Fuzzy support vector machines. IEEE Transactions on Neural Networks.2002, (13):464-471.
[210]D.M.J. Tax, and R.P.W. Duin. Outliers and data descriptions. Proc. ASCI 2001,7th Annual Conf. of the Advanced School for Computing and Imaging (Heijen,NL,May 30-June 1), ASCI, Delft,2001:234-241.
[211]Xiufeng Jiang Zhang Yi Jian Cheng Lv. Fuzzy SVM with a new fuzzy membership function. Neural Comput & Applic.2006,15:268-276.
[212]UduPa J K, Samarasekera S. Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. Graphical Model and Image Processing.1995,58(3):246-261.
[213]张翔,肖小玲,徐光祜.模糊支持向量机中隶属度的确定与分析.中国图象图形学报.2006,11(8)：1188-1192.
[214]刁鸣.雷达对抗技术.哈尔滨：哈尔滨工程大学出版社,2005.
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