辐射源指纹机理及识别方法研究
|
摘要
辐射源指纹识别,又称特定辐射源识别(Specific Emitter Identification;SEI),或辐射源个体识别,是指对接收的电磁信号进行特征测量,并根据已有的先验信息确定产生信号的辐射源个体的过程。随着雷达、通信系统参数捷变性能的提高,基于常规参数测量的信号识别技术已经很难满足电子侦察的作战要求,而基于信号无意调制等特征的辐射源指纹识别技术成为当前电子侦察的重要发展方向。
从目前辐射源指纹识别的研究来看,主要存在两个方面的问题:一是缺乏指纹的机理研究,使得所采用的指纹很容易受信号有意调制的影响而失效;二是指纹的“可测性”存在不足,往往由于背景噪声、接收机非线性、多径干扰等因素使得分类性能难以达到理想的精度。
本文针对上述两个问题展开了系统深入的研究,主要研究内容如下:
针对自激振荡类型的发射机,从典型射频振荡器(磁控管)的通用等效电路模型出发,提出了一种模型化的辐射源指纹分析方法。该模型化方法扩展了传统的特征定义,集成信号瞬时频率、瞬时带宽的相关信息,能反映器件的多方面的物理特性。在工作电压存在波纹(波纹可变)时,该方法能弥补传统上升沿、下降沿时延测量方法的局限性,可在脉冲中部提取出特征量。本章接着研究了一种基于粒子仿真的指纹分析方法,并将其用于对磁控管自由振荡和受控振荡的物理过程的模拟,验证了前文的结论。
针对主振放大式发射机,提出了一系列利用辐射源末级功放的非线性特性进行指纹识别的方法。(一)在功放激励信号为变功率的单音正弦信号时,提出了用近似的谐波约束特征作为指纹,或由吴文俊方法得到约简的指纹特征。(二)在激励信号为有意调制变化的窄带信号时,提出一种多通道相关指纹识别(Multi-Channel Correlation Fingerprinting;MCCF)方法。该方法首先由功率放大器的泰勒级数模型导出窄带输出信号的载频分量和倍频分量表达式,然后利用两分量的关联性,将载频分量作为放大器激励信号的近似,代入倍频分量的表达式中,用最小二乘方法估计出指纹特征量。在此基础上本文分析了MCCF的指纹特征的可观测条件和估计的CRLB。该方法定义的指纹特征与放大器的级数模型有关,与激励信号的形式无关,因此是发射机固有的。依据本方法对长沙地区的调频广播电台进行了指纹识别实测实验,在倍频分量功率比载频分量小60dB到80dB的典型条件下,对四个电台的发射机进行了有效的分类。(三)在激励信号为波形可变的宽带信号时,提出了一种基于子空间比较的指纹识别方法。给出了两种Volterra系统的MIMO建模方法,基于这两种MIMO模型可得到相应的参数子空间。为了避免Volterra模型存在的维数爆炸问题,直接对参数子空间进行比较来实现功放的指纹分类。对本方法的“独立性”和“可测性”进行了分析。数值仿真实验对本方法进行了验证。
针对连续波信号中的功放无意调制识别问题,提出了一种基于交叉关联积分的特定辐射源识别方法CCI-SEI。交叉关联积分算法用于对两个信号的重构矢量作高精度比较。在比较之前,采用重采样的方式使得重构矢量分布更连续,同时给出了一种统计测试方法和理论分析方法用于选择合适的相空间重构参数。该方法借鉴了美国海军研究室T.L.Carroll的工作,因此本章介绍了他提出的相空间差分方法,然后分析了相空间差分方法在低信噪比条件下的不足,并引出了本文的CCI-SEI方法。本文的实验结果验证了CCI-SEI的性能优势。
将核主元分析应用到辐射源指纹识别中,并提出了两种核函数。提出的瞬时频率RBF核函数及其高精度估计方法可用于对瞬时频率差别很小的调频信号进行特征分析。本文对估计方法的CRLB进行了分析,解释了性能改善的根本原因。提出的基于Frobenius内积的非对称子空间核函数AF-SKF可用于对脉冲组的特征提取,该核函数的定义、性质和计算方法的推导和证明都在文中给出。同时推导结果也指出:Wolf等人提出的对称子空间核函数WS-SKF实际上是本文AF-SKF的一个特例。在这之后,为了解决KPCA在数据量较大、特征发生漂移等情况下的实用性问题,给出了一种核主元更新的SVDU-KPCA算法,并用数值仿真对该算法进行了验证。
提出了一种适用于多径环境下SEI的频域广义观测模型。结合信号分类的假设检验理论,研究了三种鲁棒检验方法:信号相依污染度模型法、随机信号鲁棒检验方法和弱随机信号的渐近鲁棒检验法。其中,前两种方法都是基于Huber的混合模型,适用于多径效应影响高于加性噪声的情况。弱随机信号的渐近鲁棒检验器的推导实际上证明了在多径影响明显小于零均值加性高斯白噪声的情况下,渐近鲁棒检验器退化为直接似然比检验器。
Specific Emitter Identification (SEI) refers to designating the unique transmitter of a given signal, using only external feature measurements, by comparing those features with a library. With the improving ability of the parameter agileness in radar and communication systems, signal identification utilizing conventional parameter measurement techniques is difficult to satisfy the demand of ELectronic INTelligence (ELINT) systems. Specific Emitter Identification utilizing the signal's unintentional modulation then become an important trend of electronic intelligence development.
The existing research on SEI has two main problems. One is the absent of research on the mechanism of fingerprints. As a result, the obtained features are easy to fail when the intentional modulation changes. The other problem is the insufficient of the feature measurability. The background noise, nonlinearity of the receiver, multipath effect may all decrease the accuracy of classification.
This paper is aiming to solve the above two problems. The content of the dissertation is as follows.
Based on the equivalent circuit model of RF self-excitation device, such as magnetron, a SEI method for self-oscillatory transmitter is proposed. This model-based method defines a set of fingerprints, which expand the traditional features, and integrate the relationship of the signal's instantaneous frequency and instantaneous band. The method can extract features from the middle of the pulse,so it can cover the shortage of the traditional feature extraction methods only using the rising and trailing edge of the pulse. A Particle In Cell (PIC) simulation method for fingerprints analysis is also studied. The fingerprints of the previous model is proved by the simulation of the free and the controlled excitation process.
A series of SEI method utilizing the power amplifier(PA)'s nonlinear property are developed. (1)When the driven signal is sine signal with changeable power (in different observation), an approximate harmonic constraint feature is obtained. A more general method based on Wu method is also presented. (2) When the driven signal is narrow band with changeable intentional modulation, a Multi-Channel Correlation Fingerprinting(MCCF) method is proposed. From amplifier's Taylor series model, the expressions of the Carrier Component (CC) and the Harmonic Component (HC) of the output signal is derived. Then a least square algorithm is deduced by substituting the CC into the HC as an approximation of the driven signal. The observation condition and estimation CRLB of MCCF are provided. The fingerprints of MCCF depend on the Taylor series model, and are independent of the input signals, so they are inherent. The experiment of four FM broadcast emitters in Changsha area shows that, the MCCF method works well, even when the HC is 60dB to 80dB smaller than the CC.(3) When the driven signal is wide band with changeable intentional modulation, a subspace comparison based fingerprinting method is proposed. Two MIMO models of PA's Volterra series model are given, based on which the parameter subspace can be derived. To avoid the dimension explosion of the Volterra series model, different PA systems is identified by comparing the subspace directly. The independent and measurable properties of the method are given, and the principle of the method is verified by the simulation experiments.
A Cross-Correlation Integral SEI method(CCI-SEI) is developed to identify the amplifier's unintentional modulation of the continuous signal. The CCI algorithm is used to compare the reconstructed vectors of the two signal with high degree of accuracy in the phase space. Before the comparison, a re-sample step is taken to make the reconstructed vectors' distribution more smoothly. Also a statistical test method and a theoretical analysis method are developed to choose appropriate reconstruct parameters. CCI-SEI borrows the ideas from the work of T.L.Carroll, so Carroll's Phase Space Difference (PSD) method is introduced, the shortage of PSD method in low SNR is analyzed too.The experiment results show that CCI-SEI has better performance than the PSD method.
Studies the utility of Kernel Principle Component Analysis (KPCA) method in SEI application. Two kernel functions are developed to extract fingerprints. The Instantaneous Frequency RBF kernel(IF-RBF) and its estimator can be used in the feature extraction of frequency modulation signals with Tiny Instantaneous Frequency Difference (TIFD). This kernel uses the IFD of the two signals, so the IFD estimator's CRLB is derived, which can explain the reason of the improvement. Another proposed kernel function is Asymmetric Frobenius Subspace Kernel Function (AF-SKF), which can be used in the feature extraction of pulse groups. The rationality of the definition, the property and the calculation method are given. The derivation also shows that, the symmetric subspace kernel function WS-SKF is just a special case of AF-SKF. After the discussion of kernel function, a SVD Updating based KPCA algorithm (SVDU-KPCA) is derived. The simulation experiment proofs it.
A Frequency Domain Generalized Observation Model (FDGOM) for SEI in multipath environment is proposed. Combined with the hypothesis testing theory of signal classification, three robust hypothesis testing methods are studied: Signal Dependent Contamination Parameter (SDCP) method, random signal robust hypothesis testing method and the weak random signal asymptotic robust testing method. The first two methods base on the Huber's contamination model, and are suit for the case that the interference of multipath is bigger than the interference of noise. The derivation of asymptotic robust testing shows that asymptotic testing method will degenerate into a simple likelihood ratio testing method when the interference of noise is obviously bigger than the multipath.
从目前辐射源指纹识别的研究来看,主要存在两个方面的问题:一是缺乏指纹的机理研究,使得所采用的指纹很容易受信号有意调制的影响而失效;二是指纹的“可测性”存在不足,往往由于背景噪声、接收机非线性、多径干扰等因素使得分类性能难以达到理想的精度。
本文针对上述两个问题展开了系统深入的研究,主要研究内容如下:
针对自激振荡类型的发射机,从典型射频振荡器(磁控管)的通用等效电路模型出发,提出了一种模型化的辐射源指纹分析方法。该模型化方法扩展了传统的特征定义,集成信号瞬时频率、瞬时带宽的相关信息,能反映器件的多方面的物理特性。在工作电压存在波纹(波纹可变)时,该方法能弥补传统上升沿、下降沿时延测量方法的局限性,可在脉冲中部提取出特征量。本章接着研究了一种基于粒子仿真的指纹分析方法,并将其用于对磁控管自由振荡和受控振荡的物理过程的模拟,验证了前文的结论。
针对主振放大式发射机,提出了一系列利用辐射源末级功放的非线性特性进行指纹识别的方法。(一)在功放激励信号为变功率的单音正弦信号时,提出了用近似的谐波约束特征作为指纹,或由吴文俊方法得到约简的指纹特征。(二)在激励信号为有意调制变化的窄带信号时,提出一种多通道相关指纹识别(Multi-Channel Correlation Fingerprinting;MCCF)方法。该方法首先由功率放大器的泰勒级数模型导出窄带输出信号的载频分量和倍频分量表达式,然后利用两分量的关联性,将载频分量作为放大器激励信号的近似,代入倍频分量的表达式中,用最小二乘方法估计出指纹特征量。在此基础上本文分析了MCCF的指纹特征的可观测条件和估计的CRLB。该方法定义的指纹特征与放大器的级数模型有关,与激励信号的形式无关,因此是发射机固有的。依据本方法对长沙地区的调频广播电台进行了指纹识别实测实验,在倍频分量功率比载频分量小60dB到80dB的典型条件下,对四个电台的发射机进行了有效的分类。(三)在激励信号为波形可变的宽带信号时,提出了一种基于子空间比较的指纹识别方法。给出了两种Volterra系统的MIMO建模方法,基于这两种MIMO模型可得到相应的参数子空间。为了避免Volterra模型存在的维数爆炸问题,直接对参数子空间进行比较来实现功放的指纹分类。对本方法的“独立性”和“可测性”进行了分析。数值仿真实验对本方法进行了验证。
针对连续波信号中的功放无意调制识别问题,提出了一种基于交叉关联积分的特定辐射源识别方法CCI-SEI。交叉关联积分算法用于对两个信号的重构矢量作高精度比较。在比较之前,采用重采样的方式使得重构矢量分布更连续,同时给出了一种统计测试方法和理论分析方法用于选择合适的相空间重构参数。该方法借鉴了美国海军研究室T.L.Carroll的工作,因此本章介绍了他提出的相空间差分方法,然后分析了相空间差分方法在低信噪比条件下的不足,并引出了本文的CCI-SEI方法。本文的实验结果验证了CCI-SEI的性能优势。
将核主元分析应用到辐射源指纹识别中,并提出了两种核函数。提出的瞬时频率RBF核函数及其高精度估计方法可用于对瞬时频率差别很小的调频信号进行特征分析。本文对估计方法的CRLB进行了分析,解释了性能改善的根本原因。提出的基于Frobenius内积的非对称子空间核函数AF-SKF可用于对脉冲组的特征提取,该核函数的定义、性质和计算方法的推导和证明都在文中给出。同时推导结果也指出:Wolf等人提出的对称子空间核函数WS-SKF实际上是本文AF-SKF的一个特例。在这之后,为了解决KPCA在数据量较大、特征发生漂移等情况下的实用性问题,给出了一种核主元更新的SVDU-KPCA算法,并用数值仿真对该算法进行了验证。
提出了一种适用于多径环境下SEI的频域广义观测模型。结合信号分类的假设检验理论,研究了三种鲁棒检验方法:信号相依污染度模型法、随机信号鲁棒检验方法和弱随机信号的渐近鲁棒检验法。其中,前两种方法都是基于Huber的混合模型,适用于多径效应影响高于加性噪声的情况。弱随机信号的渐近鲁棒检验器的推导实际上证明了在多径影响明显小于零均值加性高斯白噪声的情况下,渐近鲁棒检验器退化为直接似然比检验器。
Specific Emitter Identification (SEI) refers to designating the unique transmitter of a given signal, using only external feature measurements, by comparing those features with a library. With the improving ability of the parameter agileness in radar and communication systems, signal identification utilizing conventional parameter measurement techniques is difficult to satisfy the demand of ELectronic INTelligence (ELINT) systems. Specific Emitter Identification utilizing the signal's unintentional modulation then become an important trend of electronic intelligence development.
The existing research on SEI has two main problems. One is the absent of research on the mechanism of fingerprints. As a result, the obtained features are easy to fail when the intentional modulation changes. The other problem is the insufficient of the feature measurability. The background noise, nonlinearity of the receiver, multipath effect may all decrease the accuracy of classification.
This paper is aiming to solve the above two problems. The content of the dissertation is as follows.
Based on the equivalent circuit model of RF self-excitation device, such as magnetron, a SEI method for self-oscillatory transmitter is proposed. This model-based method defines a set of fingerprints, which expand the traditional features, and integrate the relationship of the signal's instantaneous frequency and instantaneous band. The method can extract features from the middle of the pulse,so it can cover the shortage of the traditional feature extraction methods only using the rising and trailing edge of the pulse. A Particle In Cell (PIC) simulation method for fingerprints analysis is also studied. The fingerprints of the previous model is proved by the simulation of the free and the controlled excitation process.
A series of SEI method utilizing the power amplifier(PA)'s nonlinear property are developed. (1)When the driven signal is sine signal with changeable power (in different observation), an approximate harmonic constraint feature is obtained. A more general method based on Wu method is also presented. (2) When the driven signal is narrow band with changeable intentional modulation, a Multi-Channel Correlation Fingerprinting(MCCF) method is proposed. From amplifier's Taylor series model, the expressions of the Carrier Component (CC) and the Harmonic Component (HC) of the output signal is derived. Then a least square algorithm is deduced by substituting the CC into the HC as an approximation of the driven signal. The observation condition and estimation CRLB of MCCF are provided. The fingerprints of MCCF depend on the Taylor series model, and are independent of the input signals, so they are inherent. The experiment of four FM broadcast emitters in Changsha area shows that, the MCCF method works well, even when the HC is 60dB to 80dB smaller than the CC.(3) When the driven signal is wide band with changeable intentional modulation, a subspace comparison based fingerprinting method is proposed. Two MIMO models of PA's Volterra series model are given, based on which the parameter subspace can be derived. To avoid the dimension explosion of the Volterra series model, different PA systems is identified by comparing the subspace directly. The independent and measurable properties of the method are given, and the principle of the method is verified by the simulation experiments.
A Cross-Correlation Integral SEI method(CCI-SEI) is developed to identify the amplifier's unintentional modulation of the continuous signal. The CCI algorithm is used to compare the reconstructed vectors of the two signal with high degree of accuracy in the phase space. Before the comparison, a re-sample step is taken to make the reconstructed vectors' distribution more smoothly. Also a statistical test method and a theoretical analysis method are developed to choose appropriate reconstruct parameters. CCI-SEI borrows the ideas from the work of T.L.Carroll, so Carroll's Phase Space Difference (PSD) method is introduced, the shortage of PSD method in low SNR is analyzed too.The experiment results show that CCI-SEI has better performance than the PSD method.
Studies the utility of Kernel Principle Component Analysis (KPCA) method in SEI application. Two kernel functions are developed to extract fingerprints. The Instantaneous Frequency RBF kernel(IF-RBF) and its estimator can be used in the feature extraction of frequency modulation signals with Tiny Instantaneous Frequency Difference (TIFD). This kernel uses the IFD of the two signals, so the IFD estimator's CRLB is derived, which can explain the reason of the improvement. Another proposed kernel function is Asymmetric Frobenius Subspace Kernel Function (AF-SKF), which can be used in the feature extraction of pulse groups. The rationality of the definition, the property and the calculation method are given. The derivation also shows that, the symmetric subspace kernel function WS-SKF is just a special case of AF-SKF. After the discussion of kernel function, a SVD Updating based KPCA algorithm (SVDU-KPCA) is derived. The simulation experiment proofs it.
A Frequency Domain Generalized Observation Model (FDGOM) for SEI in multipath environment is proposed. Combined with the hypothesis testing theory of signal classification, three robust hypothesis testing methods are studied: Signal Dependent Contamination Parameter (SDCP) method, random signal robust hypothesis testing method and the weak random signal asymptotic robust testing method. The first two methods base on the Huber's contamination model, and are suit for the case that the interference of multipath is bigger than the interference of noise. The derivation of asymptotic robust testing shows that asymptotic testing method will degenerate into a simple likelihood ratio testing method when the interference of noise is obviously bigger than the multipath.
引文
[1]柳征.辐射源调制分类与个体识别技术研究[D].长沙:国防科学技术大学,2005.
[2]Langley L E.Specific emitter identification(SEI) and classical parameter fusion technology.377-381.1993
[3]任春辉.通信电台个体特征分析[D].成都:电子科技大学,2006.
[4]Dudczyk Janusz,Matuszewski Jan,Wnuk Marian.Applying the radiated emission to the specific emitter identification,vol.2,pp.431-434.2004
[5]Talbot Kenneth I,Duley Paul R.Specific Emitter Identification and Verification.[Z]2003.
[6]Stottler Richard,Bail Ben,Richards Robert.Intelligent Surface Threat Identification System(ISTIS).pp.4161465.2007
[7]测量与特征情报[J].国际电子战.pp.46-47.2005
[8]穆世强.雷达信号细微特性分析[J].电子对抗.pp.1-11.1992
[9]范金平,刘文林.雷达信号细微特征提取算法研究[J].电子对抗.pp.28-34.1992
[10]何自强.雷达信号脉内特征提取技术评述[J].电子对抗.Pp.1-7.1992
[11]沙祥.脉内分析综述[J].现代电子.pp.1-8.1998
[12]魏东升,徐东晖,林象平.雷达信号脉内细微特征的时频分析[J].电子对抗.pp.7-13.1993
[13]Kawalec Adam,Owczarek Robert.Radar emitter recognition using intrapulse data.vol.2,pp.435-438.2004
[14]Wisell David,Oberg Tommy.Analysis and Identification of Transmitter Non-linearities[Z].
[15]Tekbas O H,Ureten O,Serinken N.Improvement of transmitter identification system for low SNR transients[J].Electronics Letters.vol.40,pp.182-183.2004
[16]Tekbas O H,Serinken N,Ureten O.An experimental performance evaluation of a novel radio-transmitter identification system under diverse environmental conditions[J].Canadian Journal of Electrical and Computer Engineering.vol.29,pp.203-209.2004
[17]Sun L,Kinsner W,Serinken N.Characterization and Feature Extraction of Transient Signals using Multifractal Measures[C].Edmonton,Alberta,Canada:Procs.IEEE Canadian Conference on Electrical and Computer Engineering,1999.
[18]Serinken N O Ureten.Generalised dimension characterisation of radio transmitter turn-on transients[J].Electronics Letters.vol.36,pp.1064-1066.2000
[19]Kawalec Adam,Owczarek Robert.Specific emitter identification using intrapulse data.pp.249-252.2004
[20]张国柱,黄可生,姜文利,周一宇.基于信号包络的辐射源细微特征提取方法[J].系统工程与电子技术.vol.28,pp.795-797.2006
[21]胡国兵,刘渝.基于最大似然准则的特定辐射源识别[J].电子与信息学报.2008
[22]Sugiyama T,Shibuki M,Iwasaki K,Hirano T.Transmitter identification experimental techniques and results[J].Review of the Communications Research Laboratory.vol.48,pp.131-147.2002
[23]张伯涵,魏智.现代雷达发射机的理论设计与实践北京:国防工业出版社,1985.
[24]王振耀.雷达发射设备北京:电子工业出版社,1997.
[25]Skolnik M I.Radar Handbook:Mc Graw Hill,2002.
[26]张国柱.辐射源识别技术研究[D].长沙:国防科技大学,2005.
[27]Kay S.Statistically/computationally efficient frequency estimation,pp.2292-2295.1988
[28]张贤达.现代信号处理北京:清华出版社,2002.
[29]Slater J C.Microwave electronics[Z].1946,489-495.
[30]Slater J C.The Phasing of Magnetrons[Z].Cambridge:MIT.1947.
[31]Chen Shien Chi.Growth and frequency pushing effects in relativistic magnetron phase-locking[J].IEEE Transactions on Plasma Science.vol.18,pp.570-576.1990
[32]Pengvanich P,Neculaes V B,Lau Y Y,Gilgenbach R M,Jones M C.Modeling and experimental studies of magnetron injection locking[J].Journal of Applied Physics 98.2005
[33]David E E.RF phase Control in Pulsed Magnetrons[J].Proc.IRE.vol.22,pp.669-685.1952
[34]Loughlin Patrick J Keith L Davidson.Modified Cohen-Lee Time-Frequency Distributions and Instantaneous Bandwidth of Multicomponent Signals[J].IEEE Transactions on Signal Processing.vol.49,pp.1153-1165.2001
[35]Cohen L,Lee C.Instantaneous frequency,its standard deviation and multicomponent signals[J].Proc.SPIE Adv.Signal Process.Ⅲ.vol.975,pp.186-208.1988
[36]Cohen L,Lee C.Local bandwidth and optimal windows for the short-time Fourier transform[J].Proc.SPIE Adv.Signal Process.Ⅳ.vol.1152,pp.401-425.1989
[37]Pol B Van der.The nonlinear theory of electrical oscillations[J].Proc.IRE.vol.22,pp.1051-1086.1934
[38]Mandelstam L I.Lectures on Oscillation Theory(In Russian)[M].Moscow,U.S.S.R.:Nauka,1972.
[39]BogoluboffN N,Y A Mitropolskii.Asymptotic Method in the Theory of Nonlinear Oscillations[M].New York:Gordon and Breach,1961.
[40]Andronov A A,Khaikin S E,Vitt A A.Theory of Oscillations[M].Oxford,U.K.:Pergamon,1966.
[41]Yee K S.Numerical solution of initial boundary value problems involving Maxwell equations in isotropic media[J].IEEE Trans.Anatennas Propagat.vol.14,pp.302-307.1966
[42]Jones M C,Neculaes V B,White W M.Projection ablation lithograph cathode for high-current,relativistic magnetron[J].Review of Scientific Instruments.vol.75,pp.2976-2980.2004
[43]Carroll T L.A nonlinear dynamics method for signal identification[J].Chaos 17.2007
[44]Pedro Jose C,Maas Stephen A.A comparative overview of microwave and wireless power-amplifier behavioral modeling approaches[J].IEEE Transactions on Microwave Theory and Techniques.vol.53,pp.1150-1163.2005
[45]Saleh A A.Frequency-independent and frequency-dependent nonlinear models of TWT amplifiers[J].IEEE Transactions on Communications.vol.CM-29,pp.1715-1720.1981
[46]Poza H B,Sarkozy Z A,Berger H L.WIDEBAND DATA LINK COMPUTER SIMULATION MODEL[J].IEEE Proc.the National Aerospace and Electronics Conference.pp.71-78.1975
[47]Abuelma M T.Frequency-dependent nonlinear quadrature model for TWT amplifiers[J].IEEE Transactions on Communications.vol.CM-32,pp.982-986.1984
[48]Asbeck P M,Kobayashi H,Iwamoto M,Hanington G,Nam S,Larson L E.Augmented behavioral characterization for modeling the nonlinear response of power amplifiers[J],vol.1,pp.135-138.2002
[49]Draxler P,Langmore I,Hung T P,Asbeck P M.Time domain characterization of power amplifiers with memory effects[J],vol.2,pp.803-806.2003
[50]Bosch W,Gatti G.Measurement and simulation of memory effects in predistortion linearizers[J].IEEE Transactions on Microwave Theory and Techniques.vol.37,pp.3606153.1989
[51]Vuolevi J,Rahkonen T,Manninen J.Measurement technique for characterizing memory effects in RF power amplifiers[C].Denver,CO,USA:RAWCON,2000.
[52]Abed-MerA M K,Wan-Zhi Qiu,Ying-bo Hua.Blind system identification[J].Proc.the IEEE.vol.8,pp.1310-1322.1998
[53]Yang-wang Fang,Li-cheng Jiao.Blind Identification of nonlinear FIR Volterra Channel[C].Beijing:ICSP2000,2000.
[54]E Moulnes,P Duhamel,J Cardoso.Subspace Methods for the Blind Identification of Multichannel FIR Filters[J].IEEE Trans.Signal Processing.vol.43,pp.516-525.1995
[55]魏瑞轩,左东广,等.非线性系统广义脉冲响应函数的盲辨识[J].控制与决策.vol.17,pp.278-281.2002
[56]Hua Y B,Meraim K A,Waxm.Blind system identification using minimum noise subspace[J].IEEE Transactions on Signal Processing,.vol.45.pp.770-773.1997
[57]Meraim K A,Hua Y B,A Belouchran I.Minimum noise subspace concepts and applications Information[J].Communications and Signal Processing.vol.1,pp.118-121.1997
[58]Hong-zhou Tan,S CHOW T W.Blind and totla identification of ARMA mode in higher order cumulants domain[J].IEEE Transactions on Industrial Electronics.vol.46,pp.1233-1240.1999
[59]Hong-zhou Tan,S CHOW T W.Blind identification of quadratic nonlinear models using neural networks with higher order cumulants[J].IEEE Transactions on Industrial Electronics.vol.47,pp.687-696.2000
[60]Kalouptsidis Nicholas,Koukoulas Panos,Mathews V John.Blind Identification of Bilinear Systems[J].IEEE Transactions on Signal Processing.vol.51,pp.484-487.2003
[61]Prakdya Shankar,Hatzinakos Dimitrios.Blind Identification of Linear Subsystems of LTI-ZMNL-LTI Models with Cyclostationary Inputs[J].IEEE Transactions on Signal Processing.vol.45,pp.2023-2013.1997
[62]Picard G,Cappe O.Blind identification of Hammerstein nonlinear distortion models[C].New Paltz,NY:IEEE Workshop Applications Signal Processing Audio and Acoustics,2003.
[63]林强,张祖荫,郭伟.微波功率放大器非线性失真分析[J].微波学报.vol.20,pp.79-82.2004
[64]Chunming Liu,Heng Xiao,Qiang Wu,Fu Li.Linear RF Power Amplifier Design For Wireless Signals:A Spectrum Analysis Approach[C].ICASSP,2003.
[65]Han Hong,Hao Yue,Feng Hui,Li De.LDMOS linear microwave power amplifier design[J].Chinese Journal of Electron Devices.vol.30,pp.444-449.2007
[66]W Leon Couch Ⅱ.Digital and Analog Communication Systems[M].US New Jersey:Prentice-Hall Inc.,1996:179-180.
[67]Rappaport.Wireless Communication Principle and Practice[M].US New Jersey:Prentice-Hall,Inc.,1996:200-202.
[68]Chunming Liu,Heng Xiao,Qiang Wu,Fu Li.Linear RF power amplifier design for wireless signals:a spectrum analysis approach[J],vol.4,pp.568-571.2003
[69]曹建福,韩崇昭,方洋旺.非线性系统理论[M].西安:西安交通大学出版社,2001.
[70]吴文俊.几何定理机器证明的基本原理(初等几何部分)[M].北京:科学出版社,1984.
[71]吴文俊.吴文俊论数学机械化[M].济南:山东教育出版社,1996.
[72]聂一鸣.吴方法在模式识别问题中的应用研究[D].湖南长沙:国防科学技术大学.2006.
[73]Zhu A,Brazil T J.Behavioral modeling of RF power amplifiers based on pruned volterra series[J].IEEE Microwave and Wireless Components Letters.vol.14,pp.563-565.2004
[74]方洋旺,刘静,等.多输入多输出Volterra滤波均衡[J].西安交通大学学报.vol.35,pp.600-603.2001
[75]方洋旺,党映农,等.基于Volterra级数的离散MIMO非线性闭环系统的稳定性[J].控制理论与应用.vol.18,pp.741-744.2001
[76]Giannakis G B,Serpedin E.Linear multichannel blind equalizers of nonlinear FIR Volterra channels[J].IEEE Transactions on Signal Processing.vol.45,pp.67-81.1997
[77]魏瑞轩.基于Volterra级数模型的非线性系统辨识及故障诊断方法研究[D].西安:西安交通大学,2001.
[78]Wolf L,Shashua A.Learning over sets using kernel principal angles[J].Journal of Machine Learning Research.vol.4,pp.913-931.2004
[79]H Golub G,F Van Loan C.Matrix Computation[M].2ed.Baltimore:The John Hopkins University Press,1989.
[80]Liwei Wang,Xiao Wang,Jufu Feng.Subspace distance analysis with application to adaptive Bayesian algorithm for face recognition[J].Pattern Recognition.vol.39,pp.456-464.2006
[81]Wang Liwei,Wang Xiao,Feng Jufu.Intrapersonal subspace analysis with application to adaptive Bayesian face recognition[J].Pattern Recognition.vol.38,pp.617-621.2005
[82]Wang Liwei,Wang Xiao,Feng Jufu.Subspace distance analysis with application to adaptive Bayesian algorithm for face recognition[J].Pattern Recognition.vol.39,pp.456-464.2006
[83]胡茑庆.转子碰摩非线性行为与故障辨识的研究[D].长沙:国防科学技术大学,2001.
[84]Takens F.Detecting strange attractors in turbulence[J].Proc.Dynam.Syst.Turbulence.pp.366-381.1980
[85]Povinelli R J,Johnson M T,Lindgren A C,Roberts F M,Jinjin Ye.Statistical models of reconstructed phase spaces for signal classification[J].IEEE Transactions on Signal Processing.vol.54,pp.2178-2186.2006
[86]Su Yang.Nonlinear signal classification in the framework of high-dimensional shape analysis in reconstructed state space[J].IEEE Transactions on Circuits and Systems Ⅱ:Analog and Digital Signal Processing.vol.52,pp.512-516.2005
[87]Yang S.Nonlinear signal classification using geometric statistical features in state space[J].Electronics Letters.vol.40,pp.780-781.2004
[88]Schreiber Thomas.Detecting and Analyzing Nonstationarity in a Time Series Using Nonlinear Cross Predictions[J].Physical Review Letters.vol.78,pp.843-846.1997
[89]Grassberger P,Procaccia I.Characterisation of strange attractors[J].Physical Review Letters.vol.50,pp.346-349.1983
[90]Grassberger P,Procaccia I.Measuring the strangeness of strange attractors[J].Physica D.vol.9D,pp.189-208.1983
[91]H Kantz Thomas S.Nonlinear time series analysis[M].Cambridge:Cambridge University Press,1997.
[92]靳伍银,徐健学.基于关联积分的非线性降噪和故障诊断技术[J].西安交通大学学报.vol.37,pp.1167-1170.2003
[93]Scholkopf B,Mika S,Burges C J,Knirsch P,Muller K R,Ratsch G,Smola A J.Input space versus feature space in kernel-based methods[J].IEEE Transactions on Neural Networks.vol.10,pp.1000-1017.1999
[94]Scholkopf B,Smola A J,Muller K R.Nonlinear component analysis as a kernel eigenvalue problem[J].Neural Comput..vol.10,pp.1299-1319.1998
[95]Mika S,Scholkopf B,Smola A J,Muller K R,Scholz M,Batsch G.Kernel PCA and de-nosing in feature spaces[M].Cambridge,MA:MIT Press,1999:536-542.
[96]Muller K R,Mika S,Ratsch G,Tsuda K,Scholkopf B.An introduction to kernel-based learning algorithms[J].IEEE Transactions on Neural Networks.vol.12,pp.181-201.2001
[97]Boser Bernhard E,Guyon Isabelle M,Vapnik Vladimir N.Training algorithm for optimal margin classifiers[J],pp.144-152.1992
[98]Mercer J.Functions of positive and negative type and their connection with the theory of integral equations[J].Philos.Trans.Roy.Soc.London.vol.A 209,pp.415-446.1909
[99]王新峰.机电系统BIT特征层降虚警技术研究[D].长沙:国防科学技术大学,2005.
[100]边肇祺,张学工.模式识别[M].北京:清华大学出版社,2000.
[101]Shawe-Taylor John,Cristianini Nello.Kemel Methods for Pattern Analysis.Cambridge:Cambridge University Press,2004.
[102]邓新蒲,祁颖松,等.相位差变化率的测量方法及其测量精度分析[J].系统工程与电子技术.vol.23,PP.20-23.2001
[103]邓新蒲,卢启中,等.数字式相位差测量方法及精度分析[J].国防科技大学学报.vol.24,pp.70-74.2002
[104]郑世林,余佑财.频率差计基本原理的探讨[J].电子测量技术.vol.29,pp.7-8.2006
[105]Boashash Boualem.Estimating and interpreting the instantaneous frequency of a signal-part 2:Fundamentals[J].Proc.The IEEE.vol.80,pp.540-568.1992
[106]Boudra Andel Ouahab.IF estimation using empirical mode decomposition[C].IEEE International Symposium on Control,Communications and sigal Processing,2004.
[107]Maragos P,Kaiser J F.Energy separation in signal modulation to speech analysis[J].IEEE Transactions on Signal Process.vol.41,pp.3024-3051.1993
[108]Sharman K,Friedlander B.Time-varying auto-regressive modeling of a class of non-stationary signals[C].Proc.ICASSP84,1984.
[109]Wang Chenshu,Amin Moness G.Zero-tracking time-frequency distributions[J].IEEE Signal Processing.vol.3,pp.21-24.1997
[110]Wof L,Shashua A.Kernel principal angles for classification machines with applications to image sequence interpretation[J],vol.1,pp.635-640.2003
[111]李维伦.Binet-Cauchy 公式及其应用[J].工科数学 vol.18,pp.94-97.2002
[112]Kimura Shosuke,Ozawa Seiichi,Abe Shigeo.Incremental Kernel PCA for Online Learning of Feature Space[C].CIMCA-IAWTIC'05,2005.
[113]Haitao Zhao,Pong Chi Yuen,Kwok J T.A novel incremental principal component analysis and its application for face recognition[J].IEEE Transactions on Systems,Man and Cybernetics,Part B(Cybernetics).vol.36,pp.873-886.2006
[114]Zha Hongyuan,Simon Horst D.On updating problems in latent semantic indexing[J].SIAM Journal of Scientific Computing.vol.21,pp.782-791.1999
[115]Kay S M.统计信号处理基础:检测与估计理论[M].北京:电子工业出版社,2000.
[116]Iickno Song,Kassam S A.Locally optimum detection of signals in a generalized observation model:the known signal case[J].IEEE Transactions on Information Theory.vol.36,pp.502-515.1990.
[117]Iickho Song,Kassam S A.Locally optimum detection of signals in a generalized observation model:the random signal case[J].IEEE Transactions on Information Theory.vol.36,pp.516-530.1990
[118]Blum R S.Asymptotically robust detection of known signals in nonadditive noise[J].IEEE Transactions on Information Theory.vol.40,pp.1612-1619.1994
[119]Blum R S.Asymptotically robust detection for known signals in contaminated multiplicative noise[J].Signal Processing.vol.38,pp.197-210.1994
[120]Durgin Gregory D.Space-Time Wireless Channels[M].Pearson Education,Inc,2003.
[121]J Huber P.Robust version of the probability ratio test[J].Ann Math Statist.pp.1753-1758.1965
[122]刘庆云,李志舜.高斯白噪声序列谱的统计特性及应用研究[J].声学与电子工程.pp.9-11.2003
[123]沈永欢,梁在中等.实用数学手册[M].北京:科学出版社,1992.
[124]张贤达.矩阵分析与应用.北京:清华出版社,2004.
[125]蔡忠伟,李建东.基于双谱的通信辐射源个体识别[J].通信学报,2007,28(2):75-76
[126]J McLaughlin,J Droppo,L Atlas.Class-dependent time-frequency distributions via operator theory.IEEE International Conference on Acoustics,Speech,and Signal Processing.ICASSP-97,3:2045-2048
[129]Bradford W Gillespie,L Atlas.Optimizing time-frequency kernels for classification.IEEE Trans.On Signal Processing.2001.49(3):485-495
[130]T Hirano,T Sugiyama,M Shibuki.Study on time-frequency spectrum pattern for radio transmitter identification.Spring conf of ieice.Mar,2000,B-4-24:121- 127
[131]D Ralph,Hippenstiel and Ycin Paya.Wavelet based Transmitter Identification.ISSPA.Gold Coast,Australia.1996,740-743.
[2]Langley L E.Specific emitter identification(SEI) and classical parameter fusion technology.377-381.1993
[3]任春辉.通信电台个体特征分析[D].成都:电子科技大学,2006.
[4]Dudczyk Janusz,Matuszewski Jan,Wnuk Marian.Applying the radiated emission to the specific emitter identification,vol.2,pp.431-434.2004
[5]Talbot Kenneth I,Duley Paul R.Specific Emitter Identification and Verification.[Z]2003.
[6]Stottler Richard,Bail Ben,Richards Robert.Intelligent Surface Threat Identification System(ISTIS).pp.4161465.2007
[7]测量与特征情报[J].国际电子战.pp.46-47.2005
[8]穆世强.雷达信号细微特性分析[J].电子对抗.pp.1-11.1992
[9]范金平,刘文林.雷达信号细微特征提取算法研究[J].电子对抗.pp.28-34.1992
[10]何自强.雷达信号脉内特征提取技术评述[J].电子对抗.Pp.1-7.1992
[11]沙祥.脉内分析综述[J].现代电子.pp.1-8.1998
[12]魏东升,徐东晖,林象平.雷达信号脉内细微特征的时频分析[J].电子对抗.pp.7-13.1993
[13]Kawalec Adam,Owczarek Robert.Radar emitter recognition using intrapulse data.vol.2,pp.435-438.2004
[14]Wisell David,Oberg Tommy.Analysis and Identification of Transmitter Non-linearities[Z].
[15]Tekbas O H,Ureten O,Serinken N.Improvement of transmitter identification system for low SNR transients[J].Electronics Letters.vol.40,pp.182-183.2004
[16]Tekbas O H,Serinken N,Ureten O.An experimental performance evaluation of a novel radio-transmitter identification system under diverse environmental conditions[J].Canadian Journal of Electrical and Computer Engineering.vol.29,pp.203-209.2004
[17]Sun L,Kinsner W,Serinken N.Characterization and Feature Extraction of Transient Signals using Multifractal Measures[C].Edmonton,Alberta,Canada:Procs.IEEE Canadian Conference on Electrical and Computer Engineering,1999.
[18]Serinken N O Ureten.Generalised dimension characterisation of radio transmitter turn-on transients[J].Electronics Letters.vol.36,pp.1064-1066.2000
[19]Kawalec Adam,Owczarek Robert.Specific emitter identification using intrapulse data.pp.249-252.2004
[20]张国柱,黄可生,姜文利,周一宇.基于信号包络的辐射源细微特征提取方法[J].系统工程与电子技术.vol.28,pp.795-797.2006
[21]胡国兵,刘渝.基于最大似然准则的特定辐射源识别[J].电子与信息学报.2008
[22]Sugiyama T,Shibuki M,Iwasaki K,Hirano T.Transmitter identification experimental techniques and results[J].Review of the Communications Research Laboratory.vol.48,pp.131-147.2002
[23]张伯涵,魏智.现代雷达发射机的理论设计与实践北京:国防工业出版社,1985.
[24]王振耀.雷达发射设备北京:电子工业出版社,1997.
[25]Skolnik M I.Radar Handbook:Mc Graw Hill,2002.
[26]张国柱.辐射源识别技术研究[D].长沙:国防科技大学,2005.
[27]Kay S.Statistically/computationally efficient frequency estimation,pp.2292-2295.1988
[28]张贤达.现代信号处理北京:清华出版社,2002.
[29]Slater J C.Microwave electronics[Z].1946,489-495.
[30]Slater J C.The Phasing of Magnetrons[Z].Cambridge:MIT.1947.
[31]Chen Shien Chi.Growth and frequency pushing effects in relativistic magnetron phase-locking[J].IEEE Transactions on Plasma Science.vol.18,pp.570-576.1990
[32]Pengvanich P,Neculaes V B,Lau Y Y,Gilgenbach R M,Jones M C.Modeling and experimental studies of magnetron injection locking[J].Journal of Applied Physics 98.2005
[33]David E E.RF phase Control in Pulsed Magnetrons[J].Proc.IRE.vol.22,pp.669-685.1952
[34]Loughlin Patrick J Keith L Davidson.Modified Cohen-Lee Time-Frequency Distributions and Instantaneous Bandwidth of Multicomponent Signals[J].IEEE Transactions on Signal Processing.vol.49,pp.1153-1165.2001
[35]Cohen L,Lee C.Instantaneous frequency,its standard deviation and multicomponent signals[J].Proc.SPIE Adv.Signal Process.Ⅲ.vol.975,pp.186-208.1988
[36]Cohen L,Lee C.Local bandwidth and optimal windows for the short-time Fourier transform[J].Proc.SPIE Adv.Signal Process.Ⅳ.vol.1152,pp.401-425.1989
[37]Pol B Van der.The nonlinear theory of electrical oscillations[J].Proc.IRE.vol.22,pp.1051-1086.1934
[38]Mandelstam L I.Lectures on Oscillation Theory(In Russian)[M].Moscow,U.S.S.R.:Nauka,1972.
[39]BogoluboffN N,Y A Mitropolskii.Asymptotic Method in the Theory of Nonlinear Oscillations[M].New York:Gordon and Breach,1961.
[40]Andronov A A,Khaikin S E,Vitt A A.Theory of Oscillations[M].Oxford,U.K.:Pergamon,1966.
[41]Yee K S.Numerical solution of initial boundary value problems involving Maxwell equations in isotropic media[J].IEEE Trans.Anatennas Propagat.vol.14,pp.302-307.1966
[42]Jones M C,Neculaes V B,White W M.Projection ablation lithograph cathode for high-current,relativistic magnetron[J].Review of Scientific Instruments.vol.75,pp.2976-2980.2004
[43]Carroll T L.A nonlinear dynamics method for signal identification[J].Chaos 17.2007
[44]Pedro Jose C,Maas Stephen A.A comparative overview of microwave and wireless power-amplifier behavioral modeling approaches[J].IEEE Transactions on Microwave Theory and Techniques.vol.53,pp.1150-1163.2005
[45]Saleh A A.Frequency-independent and frequency-dependent nonlinear models of TWT amplifiers[J].IEEE Transactions on Communications.vol.CM-29,pp.1715-1720.1981
[46]Poza H B,Sarkozy Z A,Berger H L.WIDEBAND DATA LINK COMPUTER SIMULATION MODEL[J].IEEE Proc.the National Aerospace and Electronics Conference.pp.71-78.1975
[47]Abuelma M T.Frequency-dependent nonlinear quadrature model for TWT amplifiers[J].IEEE Transactions on Communications.vol.CM-32,pp.982-986.1984
[48]Asbeck P M,Kobayashi H,Iwamoto M,Hanington G,Nam S,Larson L E.Augmented behavioral characterization for modeling the nonlinear response of power amplifiers[J],vol.1,pp.135-138.2002
[49]Draxler P,Langmore I,Hung T P,Asbeck P M.Time domain characterization of power amplifiers with memory effects[J],vol.2,pp.803-806.2003
[50]Bosch W,Gatti G.Measurement and simulation of memory effects in predistortion linearizers[J].IEEE Transactions on Microwave Theory and Techniques.vol.37,pp.3606153.1989
[51]Vuolevi J,Rahkonen T,Manninen J.Measurement technique for characterizing memory effects in RF power amplifiers[C].Denver,CO,USA:RAWCON,2000.
[52]Abed-MerA M K,Wan-Zhi Qiu,Ying-bo Hua.Blind system identification[J].Proc.the IEEE.vol.8,pp.1310-1322.1998
[53]Yang-wang Fang,Li-cheng Jiao.Blind Identification of nonlinear FIR Volterra Channel[C].Beijing:ICSP2000,2000.
[54]E Moulnes,P Duhamel,J Cardoso.Subspace Methods for the Blind Identification of Multichannel FIR Filters[J].IEEE Trans.Signal Processing.vol.43,pp.516-525.1995
[55]魏瑞轩,左东广,等.非线性系统广义脉冲响应函数的盲辨识[J].控制与决策.vol.17,pp.278-281.2002
[56]Hua Y B,Meraim K A,Waxm.Blind system identification using minimum noise subspace[J].IEEE Transactions on Signal Processing,.vol.45.pp.770-773.1997
[57]Meraim K A,Hua Y B,A Belouchran I.Minimum noise subspace concepts and applications Information[J].Communications and Signal Processing.vol.1,pp.118-121.1997
[58]Hong-zhou Tan,S CHOW T W.Blind and totla identification of ARMA mode in higher order cumulants domain[J].IEEE Transactions on Industrial Electronics.vol.46,pp.1233-1240.1999
[59]Hong-zhou Tan,S CHOW T W.Blind identification of quadratic nonlinear models using neural networks with higher order cumulants[J].IEEE Transactions on Industrial Electronics.vol.47,pp.687-696.2000
[60]Kalouptsidis Nicholas,Koukoulas Panos,Mathews V John.Blind Identification of Bilinear Systems[J].IEEE Transactions on Signal Processing.vol.51,pp.484-487.2003
[61]Prakdya Shankar,Hatzinakos Dimitrios.Blind Identification of Linear Subsystems of LTI-ZMNL-LTI Models with Cyclostationary Inputs[J].IEEE Transactions on Signal Processing.vol.45,pp.2023-2013.1997
[62]Picard G,Cappe O.Blind identification of Hammerstein nonlinear distortion models[C].New Paltz,NY:IEEE Workshop Applications Signal Processing Audio and Acoustics,2003.
[63]林强,张祖荫,郭伟.微波功率放大器非线性失真分析[J].微波学报.vol.20,pp.79-82.2004
[64]Chunming Liu,Heng Xiao,Qiang Wu,Fu Li.Linear RF Power Amplifier Design For Wireless Signals:A Spectrum Analysis Approach[C].ICASSP,2003.
[65]Han Hong,Hao Yue,Feng Hui,Li De.LDMOS linear microwave power amplifier design[J].Chinese Journal of Electron Devices.vol.30,pp.444-449.2007
[66]W Leon Couch Ⅱ.Digital and Analog Communication Systems[M].US New Jersey:Prentice-Hall Inc.,1996:179-180.
[67]Rappaport.Wireless Communication Principle and Practice[M].US New Jersey:Prentice-Hall,Inc.,1996:200-202.
[68]Chunming Liu,Heng Xiao,Qiang Wu,Fu Li.Linear RF power amplifier design for wireless signals:a spectrum analysis approach[J],vol.4,pp.568-571.2003
[69]曹建福,韩崇昭,方洋旺.非线性系统理论[M].西安:西安交通大学出版社,2001.
[70]吴文俊.几何定理机器证明的基本原理(初等几何部分)[M].北京:科学出版社,1984.
[71]吴文俊.吴文俊论数学机械化[M].济南:山东教育出版社,1996.
[72]聂一鸣.吴方法在模式识别问题中的应用研究[D].湖南长沙:国防科学技术大学.2006.
[73]Zhu A,Brazil T J.Behavioral modeling of RF power amplifiers based on pruned volterra series[J].IEEE Microwave and Wireless Components Letters.vol.14,pp.563-565.2004
[74]方洋旺,刘静,等.多输入多输出Volterra滤波均衡[J].西安交通大学学报.vol.35,pp.600-603.2001
[75]方洋旺,党映农,等.基于Volterra级数的离散MIMO非线性闭环系统的稳定性[J].控制理论与应用.vol.18,pp.741-744.2001
[76]Giannakis G B,Serpedin E.Linear multichannel blind equalizers of nonlinear FIR Volterra channels[J].IEEE Transactions on Signal Processing.vol.45,pp.67-81.1997
[77]魏瑞轩.基于Volterra级数模型的非线性系统辨识及故障诊断方法研究[D].西安:西安交通大学,2001.
[78]Wolf L,Shashua A.Learning over sets using kernel principal angles[J].Journal of Machine Learning Research.vol.4,pp.913-931.2004
[79]H Golub G,F Van Loan C.Matrix Computation[M].2ed.Baltimore:The John Hopkins University Press,1989.
[80]Liwei Wang,Xiao Wang,Jufu Feng.Subspace distance analysis with application to adaptive Bayesian algorithm for face recognition[J].Pattern Recognition.vol.39,pp.456-464.2006
[81]Wang Liwei,Wang Xiao,Feng Jufu.Intrapersonal subspace analysis with application to adaptive Bayesian face recognition[J].Pattern Recognition.vol.38,pp.617-621.2005
[82]Wang Liwei,Wang Xiao,Feng Jufu.Subspace distance analysis with application to adaptive Bayesian algorithm for face recognition[J].Pattern Recognition.vol.39,pp.456-464.2006
[83]胡茑庆.转子碰摩非线性行为与故障辨识的研究[D].长沙:国防科学技术大学,2001.
[84]Takens F.Detecting strange attractors in turbulence[J].Proc.Dynam.Syst.Turbulence.pp.366-381.1980
[85]Povinelli R J,Johnson M T,Lindgren A C,Roberts F M,Jinjin Ye.Statistical models of reconstructed phase spaces for signal classification[J].IEEE Transactions on Signal Processing.vol.54,pp.2178-2186.2006
[86]Su Yang.Nonlinear signal classification in the framework of high-dimensional shape analysis in reconstructed state space[J].IEEE Transactions on Circuits and Systems Ⅱ:Analog and Digital Signal Processing.vol.52,pp.512-516.2005
[87]Yang S.Nonlinear signal classification using geometric statistical features in state space[J].Electronics Letters.vol.40,pp.780-781.2004
[88]Schreiber Thomas.Detecting and Analyzing Nonstationarity in a Time Series Using Nonlinear Cross Predictions[J].Physical Review Letters.vol.78,pp.843-846.1997
[89]Grassberger P,Procaccia I.Characterisation of strange attractors[J].Physical Review Letters.vol.50,pp.346-349.1983
[90]Grassberger P,Procaccia I.Measuring the strangeness of strange attractors[J].Physica D.vol.9D,pp.189-208.1983
[91]H Kantz Thomas S.Nonlinear time series analysis[M].Cambridge:Cambridge University Press,1997.
[92]靳伍银,徐健学.基于关联积分的非线性降噪和故障诊断技术[J].西安交通大学学报.vol.37,pp.1167-1170.2003
[93]Scholkopf B,Mika S,Burges C J,Knirsch P,Muller K R,Ratsch G,Smola A J.Input space versus feature space in kernel-based methods[J].IEEE Transactions on Neural Networks.vol.10,pp.1000-1017.1999
[94]Scholkopf B,Smola A J,Muller K R.Nonlinear component analysis as a kernel eigenvalue problem[J].Neural Comput..vol.10,pp.1299-1319.1998
[95]Mika S,Scholkopf B,Smola A J,Muller K R,Scholz M,Batsch G.Kernel PCA and de-nosing in feature spaces[M].Cambridge,MA:MIT Press,1999:536-542.
[96]Muller K R,Mika S,Ratsch G,Tsuda K,Scholkopf B.An introduction to kernel-based learning algorithms[J].IEEE Transactions on Neural Networks.vol.12,pp.181-201.2001
[97]Boser Bernhard E,Guyon Isabelle M,Vapnik Vladimir N.Training algorithm for optimal margin classifiers[J],pp.144-152.1992
[98]Mercer J.Functions of positive and negative type and their connection with the theory of integral equations[J].Philos.Trans.Roy.Soc.London.vol.A 209,pp.415-446.1909
[99]王新峰.机电系统BIT特征层降虚警技术研究[D].长沙:国防科学技术大学,2005.
[100]边肇祺,张学工.模式识别[M].北京:清华大学出版社,2000.
[101]Shawe-Taylor John,Cristianini Nello.Kemel Methods for Pattern Analysis.Cambridge:Cambridge University Press,2004.
[102]邓新蒲,祁颖松,等.相位差变化率的测量方法及其测量精度分析[J].系统工程与电子技术.vol.23,PP.20-23.2001
[103]邓新蒲,卢启中,等.数字式相位差测量方法及精度分析[J].国防科技大学学报.vol.24,pp.70-74.2002
[104]郑世林,余佑财.频率差计基本原理的探讨[J].电子测量技术.vol.29,pp.7-8.2006
[105]Boashash Boualem.Estimating and interpreting the instantaneous frequency of a signal-part 2:Fundamentals[J].Proc.The IEEE.vol.80,pp.540-568.1992
[106]Boudra Andel Ouahab.IF estimation using empirical mode decomposition[C].IEEE International Symposium on Control,Communications and sigal Processing,2004.
[107]Maragos P,Kaiser J F.Energy separation in signal modulation to speech analysis[J].IEEE Transactions on Signal Process.vol.41,pp.3024-3051.1993
[108]Sharman K,Friedlander B.Time-varying auto-regressive modeling of a class of non-stationary signals[C].Proc.ICASSP84,1984.
[109]Wang Chenshu,Amin Moness G.Zero-tracking time-frequency distributions[J].IEEE Signal Processing.vol.3,pp.21-24.1997
[110]Wof L,Shashua A.Kernel principal angles for classification machines with applications to image sequence interpretation[J],vol.1,pp.635-640.2003
[111]李维伦.Binet-Cauchy 公式及其应用[J].工科数学 vol.18,pp.94-97.2002
[112]Kimura Shosuke,Ozawa Seiichi,Abe Shigeo.Incremental Kernel PCA for Online Learning of Feature Space[C].CIMCA-IAWTIC'05,2005.
[113]Haitao Zhao,Pong Chi Yuen,Kwok J T.A novel incremental principal component analysis and its application for face recognition[J].IEEE Transactions on Systems,Man and Cybernetics,Part B(Cybernetics).vol.36,pp.873-886.2006
[114]Zha Hongyuan,Simon Horst D.On updating problems in latent semantic indexing[J].SIAM Journal of Scientific Computing.vol.21,pp.782-791.1999
[115]Kay S M.统计信号处理基础:检测与估计理论[M].北京:电子工业出版社,2000.
[116]Iickno Song,Kassam S A.Locally optimum detection of signals in a generalized observation model:the known signal case[J].IEEE Transactions on Information Theory.vol.36,pp.502-515.1990.
[117]Iickho Song,Kassam S A.Locally optimum detection of signals in a generalized observation model:the random signal case[J].IEEE Transactions on Information Theory.vol.36,pp.516-530.1990
[118]Blum R S.Asymptotically robust detection of known signals in nonadditive noise[J].IEEE Transactions on Information Theory.vol.40,pp.1612-1619.1994
[119]Blum R S.Asymptotically robust detection for known signals in contaminated multiplicative noise[J].Signal Processing.vol.38,pp.197-210.1994
[120]Durgin Gregory D.Space-Time Wireless Channels[M].Pearson Education,Inc,2003.
[121]J Huber P.Robust version of the probability ratio test[J].Ann Math Statist.pp.1753-1758.1965
[122]刘庆云,李志舜.高斯白噪声序列谱的统计特性及应用研究[J].声学与电子工程.pp.9-11.2003
[123]沈永欢,梁在中等.实用数学手册[M].北京:科学出版社,1992.
[124]张贤达.矩阵分析与应用.北京:清华出版社,2004.
[125]蔡忠伟,李建东.基于双谱的通信辐射源个体识别[J].通信学报,2007,28(2):75-76
[126]J McLaughlin,J Droppo,L Atlas.Class-dependent time-frequency distributions via operator theory.IEEE International Conference on Acoustics,Speech,and Signal Processing.ICASSP-97,3:2045-2048
[129]Bradford W Gillespie,L Atlas.Optimizing time-frequency kernels for classification.IEEE Trans.On Signal Processing.2001.49(3):485-495
[130]T Hirano,T Sugiyama,M Shibuki.Study on time-frequency spectrum pattern for radio transmitter identification.Spring conf of ieice.Mar,2000,B-4-24:121- 127
[131]D Ralph,Hippenstiel and Ycin Paya.Wavelet based Transmitter Identification.ISSPA.Gold Coast,Australia.1996,740-743.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |