认知雷达目标识别自适应波形设计技术研究
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摘要
认知雷达通过自适应、动态地调整发射信号使其与目标及环境相适应，因而可以大幅提升目标识别性能。在认知雷达研究的诸多问题中，自适应波形设计是认知雷达提高目标识别性能的关键问题。论文首先针对现有最优波形设计方法目标函数不完善、不统一的问题，分别研究将待识别目标冲激响应建模为确定性信号和随机信号模型的最优波形设计方法，建立不同准则目标函数之间的区别与联系；针对目前对最优波形特征随环境变化规律认识不足、难以对各波形性能分析与评估提供依据的问题，研究目标与杂波、噪声之间的关系对最优波形的影响。然后针对面向多目标、动目标的自适应波形设计技术空缺的现状，研究面向多目标和动目标场景的自适应波形设计方法。最后针对固定调制方式调制参数可变的宽带信号波形研究信号参数与目标识别性能之间的关系。论文主要内容概括如下：
第一章阐述了论文的研究背景及意义，总结了认知雷达系统的关键技术，论述了针对目标识别的自适应波形设计技术研究的意义，分析了该技术的研究现状及存在的问题，最后介绍了本文的主要工作与组织结构。
第二章研究基于确定性信号检测理论的最优波形设计方法。首先，分析了雷达目标识别问题与信号检测、最优波形设计之间的联系，建立了目标识别最优波形设计的确定性信号模型。然后，分别基于Neyman-Pearson准则和最小错误概率准则推导了噪声环境下最优波形设计的目标函数，提出了杂波环境下，以检测性能最优为准则的波形设计方法，克服了传统发射-接收联合最优化技术迭代寻优难以收敛到全局最优值的缺点；在上述研究基础上，将噪声和杂波环境下针对确定性目标识别的波形设计目标函数统一到确定性信号检测理论的框架内。最后，通过仿真实验分析不同环境下最优波形的特点，研究目标能量谱密度与噪声和杂波功率谱密度间的关系对最优波形的影响，并深入分析产生该影响的原因。
第三章研究基于随机信号估计方法的最优波形设计问题。首先，建立了随机目标的信号模型，将待识别目标冲激响应视为具有一定先验知识的随机信号。在此基础上，研究了基于线性贝叶斯理论和基于信息论的两类波形最优化设计方法，提出了基于局部SNR最大准则的目标函数，充实了基于线性贝叶斯理论的最优波形设计方法；然后，给出了杂波环境下基于线性最小均方误差估计（LMMSE）、局部SNR和互信息准则的目标函数，并针对噪声和杂波环境，建立了各准则下目标函数的相互关系，揭示了各目标函数的联系，并分析了不同准则最优波形特点及其与环境适应的能力。之后，通过仿真实验研究目标与环境的相互关系对最优波形的影响。论文第二章和第三章针对不同类型（确定、随机）目标识别的波形最优化技术进行了梳理和统一，分析讨论了不同环境下最优波形的特点，为后续最优波形设计问题中目标函数的选择和波形性能的评估奠定了理论基础。
第四章针对现有波形自适应方法仅面向单个目标、不适用于多目标识别的问题，研究针对多目标识别的波形自适应技术。首先，建立多目标识别的信号模型，定义了识别性能评判准则。在此基础上，提出了以各目标与观测信号间的互信息线性加权和（WLS-MI）为目标函数的最优波形设计方法，实现对目标数目较少时的最优波形设计。针对基于WLS-MI的方法求解最优波形效率低的问题，从目标冲激响应角度，提出了基于多目标冲激响应线性加权求和（WLS-TIR）及基于多目标自相关矩阵线性加权求和（WLS-ACM）两种最优波形设计方法；从发射波形角度，提出了对各目标的差异最大波形线性加权求和（WLS-ST-D）、对各目标的互信息最大波形加权求和（WLS-ST-MI）以及对各假设的差异最大波形加权求和（WLS-SH-D）三种波形设计方法，以上方法克服了目标数目较多时基于WLS-MI的方法无法获得最优波形的解析表示、需要从高次方程多个根中搜索选择确定最优波形的困难。在以上六种基于线性加权求和最优波形设计算法的基础上，提出了一种权值计算方法及波形自适应方法。最后，通过仿真实验，分析比较了本章所提的各种最优波形设计方法相比于传统固定发射的宽带信号的性能改善，验证了本章所提方法的有效性。
第五章针对现有目标识别波形自适应方法仅面向静态目标、对动目标识别的性能下降及其初始误判问题，研究针对动目标识别的波形自适应技术。首先，针对目标运动方向与与雷达视线不一致和一致两种场景分别分析了目标运动对现有波形自适应方法识别性能的影响。然后，针对目标运动方向与雷达视线不一致的场景，提出了利用最小二乘支持向量机预测目标姿态并用于更新最优波形的波形自适应方法；针对目标运动方向与雷达视线一致的场景，提出了基于决策层融合的波形自适应方法。最后，通过仿真实验验证了本章提出的两种方法的有效性，并研究了基于融合的波形自适应方法相对于仅利用宽带信息的波形自适应方法的识别性能改善因子随信号能量的变化关系。
第六章针对调制方式不变调制参数可变的波形参数选择问题，研究波形参数与识别性能之间的关系。首先，阐述了适用于具有复杂结构及运动特性目标的信号模型，给出了面向目标识别的序贯假设检验模型。然后，在噪声环境下，以平均观测脉冲次数作为评判目标识别性能的标准，分别针对各次观测独立同分布和指数相关两种情况，推导了平均观测脉冲次数和发射信号的关系，建立了目标函数关于发射信号的表达式。之后，基于杂波环境统计特性，以Kullback-Leibler信息数作为评判标准，推导了Kullback-Leibler信息数与发射信号的关系，建立了杂波环境下目标函数关于发射信号的表达式。最后，针对特定目标，通过仿真实验分析了宽带信号的带宽、载频、脉宽等参数与识别性能的关系。本章研究从理论上为波形参数的选择提供了依据。
第七章总结了论文的主要工作和创新点，对下一步的研究进行了展望。
Cognitive radar can improve target recognition performance dramatically byadaptively and dynamically optimizing the waveforms according to the current targetand environment. Adaptive waveform design is a critical problem in the research ofcognitive reader. This dissertation analyzes the relation of the object functions for bothdeterministic signal and stochastic signal to overcome the lack of a general descriptionof the present object functions. Then, we address the impact of the relation of targetand environment to the waveform behavior for its importance in performanceevaluation. After that we study the problem of adaptive waveform design for multipletargets and moving target. At last, we investigate the relationship of recognitionperformance and waveform parameters with fixed modulation but variableparameter.The main scientific contributions of this dissertation are summarized asfollows:
In Chapter One, the background and significance of this research is introduced, andits key technologies are summarized. After that, the importance and scientific value aswell as the technology development of the adaptive waveform design for targetrecognition are expatiated. At last, the main contributions of this dissertation aresummed up.
In Chapter Two, the optimal waveform design methods based on deterministicsignals detection theory are addressed. First, the relation of radar target recognitionbetween signal detection and waveform design is analyzed, and the model fordeterminant target is introduced. Then, the object function for known determinanttarget in noise based on Neyman-Pearson criterion and minimum probability of errorcriterion are given; A new method for target recognition in clutter based on thedetection performance under Neyman-Pearson theory is proposed, which can obtainthe analytical result of the optimal waveform, and overcomes the disadvantage oftraditional transmit-receive optimization methods, which is neither guaranteed toconverge nor to produce the optimal signal; then object functions for knowndeterminant target recognition in the present articles are analyzed and added into thesystem of object function based on detection theory. At the last part of this chapter, thebehavior of the optimal waveforms is analyzed by simulations, and the impact ofrelation between target and environment on the waveform behavior and its causes arestudied intensively.
In Chapter Three, the optimal waveform design methods based on stochasticsignals estimation are addressed. First of all, the stochastic signal model is present,which treats the target to be recognized as a stochastic process. After that, the optimalwaveform design methods based on linear Bayesian estimation theory and information theory are discussed, and a new object function of local SNR is proposed which enrichthe methods of optimal waveform based on linear Bayesian estimation theory. Based onthe results of waveform design in noise, the object functions in clutter based on LinearMinimum Mean Square Error (LMMSE), local SNR and Mutual Information (MI) aregiven. Then the relationship of the object functions both in noise and clutter areestablished, and the behavior of the optimal waveforms is analyzed. Then the impacts ofenvironment on the waveforms obtained by different criteria are studied by large mountof simulations. At last, the method of waveform synthesis is introduced simply. Thischapter and Chapter Two make an intensive collection and analysis on the presentwaveform design methods, and together with the analysis on the waveform behavioraccording to the environment, they will provide a valuable theoretic and technicalsupport for the selection of object function and performance evaluation of optimalwaveforms.
In Chapter Four, adaptive waveform design methods for multiple targetsrecognition are studied on the realization that the adaptive waveform design method forsingle target is unsuitable for multiple targets. First of all, the signal model of multipletargets is present, and the measure of recognition goodness is defined, then the problemsof waveform for multiple targets recognition are pointed out. After that, six kinds ofwaveform design methods are proposed from different stages of signal processing. Thefirst one is based on the Weighted Linear Sum of MI (WLS-MI) between theobservation and each target impulse response, which performs well when target numberis less than3. Then to overcome the difficulty of WLS-MI when dealing with moretargets, two methods are proposed based on WLS of Target Impulse Response(WLS-TIR) and the method based on WLS of Autocorrelation Matrix (WLS-ACM)founded in the processing of target impulse responses. Besides, three methods are putforward founded in the processing of optimal waveform, two of which are obtained byWLS of the optimal waveforms for each target, and are called WLS-ST-D andWLS-ST-MI, respectively, according to the kind of criteria of optimal waveform whichare difference maximum and MI maximum, and the left one is to obtain the waveformfor multiple targets by WLS of the optimal signal for each hypothesis based ondifference maximum (WLS-SH-D). Then the algorithm of weight calculation isresearched, based on which an adaptation mechanism for multiple targets recognition isproposed. The simulation results show the performance improvement of the proposedwaveform design methods and the adaptation mechanism relative to the traditionalwaveform.
In Chapter Five, the adaptive methods for moving target are researched for thepoor performance of existing methods aims at static target when used for moving target.Two moving scenarios are considered. The first one is the assumption that the target ismoving in a direction different from the radar line of sight, and the second one is the assumption that the target moves parallel to the radar line of sight. The impact of targetmotion on the performance of adaptive waveform is analyzed intensively. After that,two methods of waveform adaptation are proposed aims at the two scenarios. For thefirst scenario, an adaptive waveform method based on the aspect prediction by LeastSquare Support Vector Machine (LSSVM) is proposed; and to improve the recognitionperformance in the second scenario, a method based on information fusion on decisionlevel is proposed, which makes use of the recognition results of both wideband andnarrow band signals. The simulations verify the validity of the proposed method.Besides, the recognition performance improvements of the sensor fusion methodrelative to the wideband signal only method are compared under different energyrestriction. The conclusion about the impact of target to noise ratio on optimalwaveform obtained in the third chapter and the theory of sequential hypothesis test areused to explain the recognition improvements variations.
In Chapter Six, the relationship of recognition performance and waveformparameters with fixed modulation but variable parameter is studied. The signal modelwhich is suitable for target with complex structure and moving behavior is depicted, andthe sequential hypothesis test model is present. The average number of observations isdefined as measure of recognition performance in noise, and the relationship of averagenumber of observations for noise of IID (Independent and Identically Distributed) andexponential correlations is studied respectively, then the object functions about thetransmitted signal for both cases are present. After that, KLIN (Kullback-Leiblerinformation numbers) is defined as measure of recognition performance in clutter, andthe relationship of KLIN and transmitted signals are establish based on the analysis ofthe statistical feature of the clutter, and the object function about the transmitted signalis depicted. At the last part, for some given targets, the relationship of the recognitionperformance and parameters (bandwidth, carry frequency and pulse width) fortraditional signals are analyzed by simulation. The research of this chapter providestheoretical references for parameter selection for recognition of given target.
In Chapter Seven, the main contributions and innovative work of this dissertationare concluded. And the potential problems and future work to be further researched arepointed out.
第一章阐述了论文的研究背景及意义，总结了认知雷达系统的关键技术，论述了针对目标识别的自适应波形设计技术研究的意义，分析了该技术的研究现状及存在的问题，最后介绍了本文的主要工作与组织结构。
第二章研究基于确定性信号检测理论的最优波形设计方法。首先，分析了雷达目标识别问题与信号检测、最优波形设计之间的联系，建立了目标识别最优波形设计的确定性信号模型。然后，分别基于Neyman-Pearson准则和最小错误概率准则推导了噪声环境下最优波形设计的目标函数，提出了杂波环境下，以检测性能最优为准则的波形设计方法，克服了传统发射-接收联合最优化技术迭代寻优难以收敛到全局最优值的缺点；在上述研究基础上，将噪声和杂波环境下针对确定性目标识别的波形设计目标函数统一到确定性信号检测理论的框架内。最后，通过仿真实验分析不同环境下最优波形的特点，研究目标能量谱密度与噪声和杂波功率谱密度间的关系对最优波形的影响，并深入分析产生该影响的原因。
第三章研究基于随机信号估计方法的最优波形设计问题。首先，建立了随机目标的信号模型，将待识别目标冲激响应视为具有一定先验知识的随机信号。在此基础上，研究了基于线性贝叶斯理论和基于信息论的两类波形最优化设计方法，提出了基于局部SNR最大准则的目标函数，充实了基于线性贝叶斯理论的最优波形设计方法；然后，给出了杂波环境下基于线性最小均方误差估计（LMMSE）、局部SNR和互信息准则的目标函数，并针对噪声和杂波环境，建立了各准则下目标函数的相互关系，揭示了各目标函数的联系，并分析了不同准则最优波形特点及其与环境适应的能力。之后，通过仿真实验研究目标与环境的相互关系对最优波形的影响。论文第二章和第三章针对不同类型（确定、随机）目标识别的波形最优化技术进行了梳理和统一，分析讨论了不同环境下最优波形的特点，为后续最优波形设计问题中目标函数的选择和波形性能的评估奠定了理论基础。
第四章针对现有波形自适应方法仅面向单个目标、不适用于多目标识别的问题，研究针对多目标识别的波形自适应技术。首先，建立多目标识别的信号模型，定义了识别性能评判准则。在此基础上，提出了以各目标与观测信号间的互信息线性加权和（WLS-MI）为目标函数的最优波形设计方法，实现对目标数目较少时的最优波形设计。针对基于WLS-MI的方法求解最优波形效率低的问题，从目标冲激响应角度，提出了基于多目标冲激响应线性加权求和（WLS-TIR）及基于多目标自相关矩阵线性加权求和（WLS-ACM）两种最优波形设计方法；从发射波形角度，提出了对各目标的差异最大波形线性加权求和（WLS-ST-D）、对各目标的互信息最大波形加权求和（WLS-ST-MI）以及对各假设的差异最大波形加权求和（WLS-SH-D）三种波形设计方法，以上方法克服了目标数目较多时基于WLS-MI的方法无法获得最优波形的解析表示、需要从高次方程多个根中搜索选择确定最优波形的困难。在以上六种基于线性加权求和最优波形设计算法的基础上，提出了一种权值计算方法及波形自适应方法。最后，通过仿真实验，分析比较了本章所提的各种最优波形设计方法相比于传统固定发射的宽带信号的性能改善，验证了本章所提方法的有效性。
第五章针对现有目标识别波形自适应方法仅面向静态目标、对动目标识别的性能下降及其初始误判问题，研究针对动目标识别的波形自适应技术。首先，针对目标运动方向与与雷达视线不一致和一致两种场景分别分析了目标运动对现有波形自适应方法识别性能的影响。然后，针对目标运动方向与雷达视线不一致的场景，提出了利用最小二乘支持向量机预测目标姿态并用于更新最优波形的波形自适应方法；针对目标运动方向与雷达视线一致的场景，提出了基于决策层融合的波形自适应方法。最后，通过仿真实验验证了本章提出的两种方法的有效性，并研究了基于融合的波形自适应方法相对于仅利用宽带信息的波形自适应方法的识别性能改善因子随信号能量的变化关系。
第六章针对调制方式不变调制参数可变的波形参数选择问题，研究波形参数与识别性能之间的关系。首先，阐述了适用于具有复杂结构及运动特性目标的信号模型，给出了面向目标识别的序贯假设检验模型。然后，在噪声环境下，以平均观测脉冲次数作为评判目标识别性能的标准，分别针对各次观测独立同分布和指数相关两种情况，推导了平均观测脉冲次数和发射信号的关系，建立了目标函数关于发射信号的表达式。之后，基于杂波环境统计特性，以Kullback-Leibler信息数作为评判标准，推导了Kullback-Leibler信息数与发射信号的关系，建立了杂波环境下目标函数关于发射信号的表达式。最后，针对特定目标，通过仿真实验分析了宽带信号的带宽、载频、脉宽等参数与识别性能的关系。本章研究从理论上为波形参数的选择提供了依据。
第七章总结了论文的主要工作和创新点，对下一步的研究进行了展望。
Cognitive radar can improve target recognition performance dramatically byadaptively and dynamically optimizing the waveforms according to the current targetand environment. Adaptive waveform design is a critical problem in the research ofcognitive reader. This dissertation analyzes the relation of the object functions for bothdeterministic signal and stochastic signal to overcome the lack of a general descriptionof the present object functions. Then, we address the impact of the relation of targetand environment to the waveform behavior for its importance in performanceevaluation. After that we study the problem of adaptive waveform design for multipletargets and moving target. At last, we investigate the relationship of recognitionperformance and waveform parameters with fixed modulation but variableparameter.The main scientific contributions of this dissertation are summarized asfollows:
In Chapter One, the background and significance of this research is introduced, andits key technologies are summarized. After that, the importance and scientific value aswell as the technology development of the adaptive waveform design for targetrecognition are expatiated. At last, the main contributions of this dissertation aresummed up.
In Chapter Two, the optimal waveform design methods based on deterministicsignals detection theory are addressed. First, the relation of radar target recognitionbetween signal detection and waveform design is analyzed, and the model fordeterminant target is introduced. Then, the object function for known determinanttarget in noise based on Neyman-Pearson criterion and minimum probability of errorcriterion are given; A new method for target recognition in clutter based on thedetection performance under Neyman-Pearson theory is proposed, which can obtainthe analytical result of the optimal waveform, and overcomes the disadvantage oftraditional transmit-receive optimization methods, which is neither guaranteed toconverge nor to produce the optimal signal; then object functions for knowndeterminant target recognition in the present articles are analyzed and added into thesystem of object function based on detection theory. At the last part of this chapter, thebehavior of the optimal waveforms is analyzed by simulations, and the impact ofrelation between target and environment on the waveform behavior and its causes arestudied intensively.
In Chapter Three, the optimal waveform design methods based on stochasticsignals estimation are addressed. First of all, the stochastic signal model is present,which treats the target to be recognized as a stochastic process. After that, the optimalwaveform design methods based on linear Bayesian estimation theory and information theory are discussed, and a new object function of local SNR is proposed which enrichthe methods of optimal waveform based on linear Bayesian estimation theory. Based onthe results of waveform design in noise, the object functions in clutter based on LinearMinimum Mean Square Error (LMMSE), local SNR and Mutual Information (MI) aregiven. Then the relationship of the object functions both in noise and clutter areestablished, and the behavior of the optimal waveforms is analyzed. Then the impacts ofenvironment on the waveforms obtained by different criteria are studied by large mountof simulations. At last, the method of waveform synthesis is introduced simply. Thischapter and Chapter Two make an intensive collection and analysis on the presentwaveform design methods, and together with the analysis on the waveform behavioraccording to the environment, they will provide a valuable theoretic and technicalsupport for the selection of object function and performance evaluation of optimalwaveforms.
In Chapter Four, adaptive waveform design methods for multiple targetsrecognition are studied on the realization that the adaptive waveform design method forsingle target is unsuitable for multiple targets. First of all, the signal model of multipletargets is present, and the measure of recognition goodness is defined, then the problemsof waveform for multiple targets recognition are pointed out. After that, six kinds ofwaveform design methods are proposed from different stages of signal processing. Thefirst one is based on the Weighted Linear Sum of MI (WLS-MI) between theobservation and each target impulse response, which performs well when target numberis less than3. Then to overcome the difficulty of WLS-MI when dealing with moretargets, two methods are proposed based on WLS of Target Impulse Response(WLS-TIR) and the method based on WLS of Autocorrelation Matrix (WLS-ACM)founded in the processing of target impulse responses. Besides, three methods are putforward founded in the processing of optimal waveform, two of which are obtained byWLS of the optimal waveforms for each target, and are called WLS-ST-D andWLS-ST-MI, respectively, according to the kind of criteria of optimal waveform whichare difference maximum and MI maximum, and the left one is to obtain the waveformfor multiple targets by WLS of the optimal signal for each hypothesis based ondifference maximum (WLS-SH-D). Then the algorithm of weight calculation isresearched, based on which an adaptation mechanism for multiple targets recognition isproposed. The simulation results show the performance improvement of the proposedwaveform design methods and the adaptation mechanism relative to the traditionalwaveform.
In Chapter Five, the adaptive methods for moving target are researched for thepoor performance of existing methods aims at static target when used for moving target.Two moving scenarios are considered. The first one is the assumption that the target ismoving in a direction different from the radar line of sight, and the second one is the assumption that the target moves parallel to the radar line of sight. The impact of targetmotion on the performance of adaptive waveform is analyzed intensively. After that,two methods of waveform adaptation are proposed aims at the two scenarios. For thefirst scenario, an adaptive waveform method based on the aspect prediction by LeastSquare Support Vector Machine (LSSVM) is proposed; and to improve the recognitionperformance in the second scenario, a method based on information fusion on decisionlevel is proposed, which makes use of the recognition results of both wideband andnarrow band signals. The simulations verify the validity of the proposed method.Besides, the recognition performance improvements of the sensor fusion methodrelative to the wideband signal only method are compared under different energyrestriction. The conclusion about the impact of target to noise ratio on optimalwaveform obtained in the third chapter and the theory of sequential hypothesis test areused to explain the recognition improvements variations.
In Chapter Six, the relationship of recognition performance and waveformparameters with fixed modulation but variable parameter is studied. The signal modelwhich is suitable for target with complex structure and moving behavior is depicted, andthe sequential hypothesis test model is present. The average number of observations isdefined as measure of recognition performance in noise, and the relationship of averagenumber of observations for noise of IID (Independent and Identically Distributed) andexponential correlations is studied respectively, then the object functions about thetransmitted signal for both cases are present. After that, KLIN (Kullback-Leiblerinformation numbers) is defined as measure of recognition performance in clutter, andthe relationship of KLIN and transmitted signals are establish based on the analysis ofthe statistical feature of the clutter, and the object function about the transmitted signalis depicted. At the last part, for some given targets, the relationship of the recognitionperformance and parameters (bandwidth, carry frequency and pulse width) fortraditional signals are analyzed by simulation. The research of this chapter providestheoretical references for parameter selection for recognition of given target.
In Chapter Seven, the main contributions and innovative work of this dissertationare concluded. And the potential problems and future work to be further researched arepointed out.
引文
[1]马林.雷达目标识别技术综述[J].现代雷达,2011,33(6):1~7.
[2]李丽亚.宽带雷达目标识别技术[D].西安:西安电子科技大学,2009.
[3] Haykin S. Cognitive Radar-A Way of The Future[J]. IEEE Signal ProcessingMagzine,2006,23(1):30~40.
[4] Dai F Z, Liu H W, Wang P H, et al. Adaptive Waveform Design forRange-Spread Target Tracking[J]. Electronics Letters,2010,46(11):793~794.
[5] Wicks M C. A Brief History of Waveform Diversity[C]//IEEE RadarConference. Pasadena,2009:1~6.
[6]周宇.基于知识的雷达自适应处理方法研究[D].西安:西安电子科技大学,2010.
[7] Guerci J R. Cognitive Radar: The Knowledge-Aided Fully AdaptiveApproach[M]. Boston: Artech House,2010.
[8] Gini F. Knowledge-Based Systems for Adaptive Radar[J]. IEEE SignalProcessing Magazine,2006,23(1):10~11.
[9] Capraro G T, Farina A, Griffiths H, et al. Knowledge-Based Radar Signal andData Processing-A Tutorial Overview [J]. IEEE Signal Process Magazine,2006(1):18~29.
[10] Guerci J R, Baranoski E J. Knowledge-Aided Adaptive Radar at DARPA [J].IEEE Signal Process Magazine,2006(1):41~50.
[11] Miranda S, Baker C, Woodbridge K, et al. Knowledge-Based ResourceManagement for Multifunction Radar [J]. IEEE Signal Process Magazine,2006(1):66~76.
[12] Baldygo W, Brown R, Antonik P, et al. Artificial Intelligence Applications toConstant False Alarm Rate (CFAR) Processing[C]//IEEE National Radar Conference.Lynnfield,1993:275~280.
[13] Wicks M, Melvin W, Chen P. An Efficient Architecture for NonhomogeneityDetection in Space-Time Adaptive Processing Airborne Early Warning Radar[C]//IEEInternational Radar Conference. Edinburgh,1997:295~299.
[14] DARPA. KASSPER: Knowledge-Aided Sensor Signal Processing and ExpertReasoning [EB/OL].http://www.darpa.mil/spo/pmgrams/kassper. html.2008-12-5
[15] Wicks M. Spectrum Crowding and Cognitive Radar [C]//2nd InternationalWorkshop on Cognitive Information Processing. Elba,2010:452~457.
[16] Cochran D, Suvorova S, Howard S D, et al. Waveform Libraries-Measures ofeffectiveness for radar scheduling[J].. IEEE Signal Process Magazine,2009(1):12-21.
[17] Calderbank R, Howard S D, Moran B. Waveform Diversity in Radar SignalProcessing-A Focus on The Use and Control of Degrees of Freedom[J].. IEEE Signalprocessing magazine,2009(1):32-41.
[18] Cochran D. Waveform-Agile Sensing: Opportunities and Challenges[C]//IEEE International Conference on Acoustics, Speech, and Signal Processing.2005:877~880.
[19] Goodman N A. Closed-Loop Radar with Adaptively MatchedWaveforms[C]//International Conference on Electromagnetics in AdvancedApplications. Torino,2007:468~471.
[20] Picciolo M, Griesbach J D, Gerlach K. Adaptive LFM WaveformDiversity[C]//IEEE Radar Conference. Rome,2008:1863~1868.
[21] Capraro C T, Capraro G T, Weiner D, et al. Knowledge Based Map SpaceTime Adaptive Processing (KBMapSTAP)[C]//International Conference on ImagingScience, Systems, and Technology. Las Vegas:2001.
[22] Capraro, C T, Capraro G T, Weiner D, et al. Improved STAP Performanceusing Knowledge-Aided Secondary Data Selection[C]//IEEE Radar Conference.Philadelphia,2004:361~365.
[23] DARPA. Adaptive Waveform Design for Detecting Low-Grazing-Angle andSmall-RCS Targets in Complex Maritime Environments [EB/OL].http://signal.ese.wustl.edu/DARPA/index.html.
[24] MURI. Adaptive Waveform Design for Full Spectral Dominance[EB/OL].http://signal.ese.wustl.edu/MURI/index.html.
[25] Baker J. Intelligence and radar systems[C]//IEEE Radar Conference.Washington,2010:1276~1279.
[26] Wei Y, Meng H, Wang X. Adaptive Single-Tone Waveform Design forTarget Recognition in Cognitive Radar[C]//IEEE Radar Conference. Guilin,2009.
[27] Haykin S, Xue Y, Davidson T N. Optimal Waveform Design For CognitiveRadar[C]//42nd Asilomar Conference on Signals, Sytems and Computers. PacificGrove,2008:3~7.
[28] Liu F, Wang J. An Optimal ADP Algorithm for Waveform Selection inCognitive Radar Systems [C]//Progress in Electromagnetic Research Symposium.Beijing,2009:728~730.
[29] Wei Y, Meng H, Liu Y,et al. Extended Target Recognition in Cognitive RadarNetworks[J]. Sensor,2010,10(11):10181~10197.
[30] Zhang J D, Zhu D Y, Zhang G. Multi-objective Waveform Design forCognitive Radar[C]//IEEE International Conference on CIE. Chengdu,2011:580~583.
[31] Inggs M. Passive Coherent Location as Cognitive Radar [J]. IEEE A&ESystems Magazine,2010(5):12~17.
[32] Haykin S. Cognitive Dynamic Systems: An Integrative Field that will be aHallmak of the21st Century[C]//10th IEEE International Conference on Informatics&Cognitive Computing. Banff,2011:2.
[33] Fan L, Wang J, Wang B. Time Allocation in Cognitive Radar for MultipleTarget Detection[C]//IEEE Third International Symposium on Intelligent InformationTechnology Application. Nanchang,2009:169~172.
[34] Haykin S, Zia A, Arasaratnam I, et al. Cognitive Tracking Radar[C]//IEEERadar Conference. Washington,2010:1467~1470.
[35] Haykin S. New Generation of Radar Systems Enabled withCognition[C]//IEEE Radar Conference. Washington,2010.
[36] Guerci J R. Cognitive Radar: A Knowledge-Aided Fully AdaptiveApproach[C]//IEEE Radar Conference.Washington.2010:135~1370.
[37]黄培康,遥感目标的特征提取与反演[Z].长沙:国防科技大学,2011.
[38] National Institutes of Health, N.I.o.M.H. and.“Definition of Cognition,”[EB/OL].http://science-education.nih.gov/supplements/nih5/Mental/other/glossary.html
[39] Sowelam S M, Tewfik A. Optimal Waveform Selection for Radar TargetClassification[C]//IEEE International Conference on Image Processing. SantaBarbara,1997:476~479.
[40] Sowelam S, Tewfik A. Waveform Selection in Radar Target Classification.IEEE Transactions on Information Theory,2000,46(3):1014~1029.
[41] LaScala F, Moran R, Evans W. Optimal Adaptive Waveform Selection forTarget Detection[C]//International Conference on Radar. Adelaide,2003:492~496.
[42] Wang B, Wang J Song X, et al. Stochastic Dynamic Programming Model ofAdaptive Waveform Selection[C]//International Conference on Networking, Sensingand Control. Chicago,2010:267~272.
[43] Zhang Z, Zhou J, Wang F. An algorithm of target tracking based on adaptiveLFM waveform design[C]//2nd International Conference on Advanced ComputerControl.Shenyang,2010:130~132
[44] Cochran D, Suvorova S, Howard D S, et al. Waveform Libraries-Measures ofEffectiveness for Radar Scheduling[J]. IEEE Signal Process Magazine,2009,26(1):12~21.
[45] Haykin S, Currie B, Kirubarajan T. Literature Search on Adaptive RadarTransmit Waveforms[R]. Ottawa: Defence R&D Canada,2002.
[46] Bonneau R J, Wicks M C. A numerical waveform design approach todecorrelate target and noise[C]//IEEE Radar Conference.Atlanta,2001:448~450.
[47]黎湘，范梅梅.认知雷达及其关键技术研究进展[J].电子学报,2012,40(6).
[48] Pillai S U, Oh S, Youla D C. Optimum Transmit-Receiver Design in thePresence of Signal-Dependent Interference and Channel Noise[J]. IEEE Transactions onInformation Theory,2000,46(2):577~584.
[49] Garren D A, Osborn M K, Odom A C, et al. Enhanced Target Detection andIdentification via Optimised Radar Transmission Pulse Shape[J]. IEE Proc.-Radar,Sonar Navig,2001,148(3):130~138.
[50] Kay S. Optimal Signal Design for Detection of Gaussian Point Targets inStationary Gaussian Clutter/Reverberation [J]. IEEE Journal of Selected Topics inSignal Processing,2007,1(1):31~41.
[51] Kay S. Optimum Radar Signal for Detection in Clutter [J].. IEEE Transactionson Aerospace and Electronic Systems,2007,43(3):1059~1065.
[52] Sira S P, Cochran D, Antonia P, et al. Adaptive Waveform Design forImproved Detection of Low-RCS Targets in Heavy Sea Clutter [J]. IEEE Journal ofSelected Topics in Signal Processing,2007,1(1):56~66.
[53] Guerci J R, Pillai S U. Theory and Application of Optimum Transmit-ReceiveRadar [C]//IEEE Internal radar Conference. Alexandria:2000:705~710.
[54] Leshem A, Naparstek O, Nehorai A. Information theoretic adaptive radarwaveform design for multiple extended targets[J]. IEEE Journal of Selected Topics inSignal Processing,2007,1(1):42~55.
[55] Leshem A, Naparstek O, Nehorai A. Adaptive Radar Waveform Design forMultiple Targets: Computational Aspects[C]//IEEE International Conference onAcoustics, Speech and Signal Processing. Honolulu,2007:910~912.
[56] Leshem A, Naparstek O, Nehorai A. Information Theoretic Radar WaveformDesign for Multiple Targets[C]//IEEE Waveform Diversity&Design. Pisa,2007:362~366.
[57] Patton L K. On the Satisfaction of Modulus and Ambiguity FunctionConstraints in Radar Waveform Optimization for Detection [D]. Dayton: Wright StateUniversity,2009.
[58] Pillai S U, Li K Y, Beyer H. Reconstruction of Constant Envelop Signals withGiven Fourier Transform Magnitude[C]//IEEE Radar Conference.2009.
[59] Jackson L, Kay S, Vankayalapati N. Iterative Method for Nonlinear FMSynthesis of Radar Signals[J]. IEEE Transactions on Aerospace and Electronic Systems,2010,46(2):910~917.
[60] Bell M R. Information Theory and Radar Waveform Design [J]. IEEETransactions on Information Theory,1993,39(5):1578~1597.
[61] Goodman N A, Venkata P R, Neifeld M A. Adaptive Waveform Design andSequential Hypothesis Testing for Target Recognition with Active Sensors[J]. IEEEJournal of Selected Topics in Signal Processing,2007,1(1):105~113.
[62] Yang Y, Blum R S. MIMO Radar Waveform Design Based on MutualInformation and Minimum Mean-Square Error Estimation [J]. IEEE Transactions onAerospace and Electronic Systems,2007,43(1):330~343.
[63] Romero R A, Goodman N A. Waveform Design in Signal-DependentInterference and Application to Target Recognition with Multiple Transmissions [J].IET Radar Sonar and Navigation,2009,3(4):328~340.
[64]纠博,刘宏伟,李丽亚等.一种基于互信息的波形优化设计方法[J].西安电子科技大学学报（自然科学版）,2008,35(4):678~684.
[65]纠博,刘宏伟,李丽亚等.雷达波形优化的特征互信息方法[J].西安电子科技大学学报（自然科学版）,2009,36(1):139~144.
[66] Sira S P, Li Y, Antonia P, et al. Waveform-Agile Sensing for Tracking-areview perspective[J]. IEEE Signal processing magazine,2009,26(1):53~64.
[67] Haykin S, Zia A, Xue Y, et al. Control Theoretic Approach to Tracking Radar:First Step towards Cognition[J]. Digital Signal Processing,2011:576~585.
[68] Sowelam S M, Tewfik A H. Optimal Waveforms for Wideband RadarImaging[J]. J. Franklin Institute,1998.335B(8):1341~1366.
[69] Linnehana, R, Bradyb D, Schindlerc J, et al. Using Cram′er-Rao Theory ForSAR Waveform Design[C]//IEEE International Radar Conference.2005:217~212.
[70] Bakker, R, Kirubarajan, T, Currie B, et al. Adaptive Radar Detection: ABayesian Approach[C]//EPSRC IEE Workshop Nonlinear Non-Gaussian SignalProcessing. Peebles:2002.
[71] Bakke R, Lopez G, Haykin S. Bayesian Approach to The Direct Filtering ofRadar Targets in Clutter[R]. Hamiltion: Adaptive Systems Laboratory, McMasterUniversity,2002.
[72] Bruno M G and Moura J M. Multiframe detector/tracker: Optimalperformance [J]. IEEE Transactions on Aerospace and Electronic System,2001,37:925~944.
[73] Haykin S, Currie B, Kirubarajan T, Adaptive Radar for Improved SmallTarget Detection in a Maritime Environment[R]. Hamilton: McMaster University,2003.
[74] Li Y, Moran W, Sira S P, et al. Adaptive Waveform Design inRapidly-Varying Radar Scenes[C]//International Waveform Diversity and Design.Kissimmee,2009:263~267.
[75] Bellman R. Dynamic Programming[M]. Princeton: Princeton UniversityPress,1957.
[76]王彬,汪晋宽，宋昕等.认知雷达中基于Q学习的自适应波形选择算法.系统工程与电子技术[J].2011,33(5):1007~1012.
[77] Bae J H, Goodman N A. Adaptive Waveforms for Target ClassDiscrimination[C]//International Waveform Diversity and Design. Pisa,2007:395~399.
[78] Romero R, Goodman N A. Improved Waveform Design for TargetRecognition with Multiple Transmissions[C]//International Waveform Diversity andDesign. Kissimmee2009:26~30.
[79] Venkata R P, Goodman N A. Novel Iterative Techniques for Radar TargetDiscrimination[C]//Waveform Design and Diversity.2006.
[80]黄德双.高分辨雷达智能信号处理技术[M].机械工业出版社.2001.
[81] Richards M A.雷达信号处理基础[M].北京:电子工业出版社,2008.
[82] Kennaugh E M, Moffatt D L. Transient and Impulse ResponseApproximations[J]Proceedings of the IEEE.1965,53(8):893~901.
[83]黄培康,殷红成,许小剑.雷达目标特性[M].北京:电子工业出版社.2006.
[84] Kennaugh E. The K-Pulse Concept[J]. IEEE Transactions on AntennasPropagation,1981,29(2):327~331.
[85]郭桂蓉,庄钊文,陈曾平.电磁特征提取与目标识别[M].长沙:国防科技大学出版社.1995.
[86] Baum C E, Rothwell E J, Chen K M,et al. The Singularity Expansion Methodand Its Application to Target Identification [J].Proceedings of IEEE,1991,79(10):1481~1491.
[87] Gjessing D. Target Adaptive Matched Illumination Radar: Principles&Applications[M]. Peter Peregrinus on behalf of the Institution of Electrical Engineers,1986.
[88] Pell C. Book review: Target Adaptive Matched Illumination Radar: Principlesand Applications [J]. IEE Proceedings of Communications, Radar and SignalProcessing,1986,133(6):581~582.
[89] Guerci J R, Schutz R W, Hulsmann J D. Constrained optimum matchedillumination-reception radar [P]. US Patent:51462291992,1992.
[90] Guerci J R. Optimum Matched Illumination-Reception Radar for TargetClassification[P]. US Patent:5381154,1995.
[91] Garren D A, Osborn M K, Odom A C, et al. Optimization of single transmitpulse shape to maximize detection and identification of ground mobile targets[C]//34thAsilomar Conference on Sinals, Systems, and Computers. Pacific Grove,2000:1535~1539.
[92] Garren D A, Osborn M K, Odom A C, et al. Optimal Transmission PulseShape for Detection and Identification with Uncertain Target Aspect[C]//IEEE RadarConference. Atlanta,2001:123~128.
[93] Romero R A, Bae J, Goodman N A. Theory and Application of SNR andMutual Information Matched Illumination Waveforms [J]. IEEE Transactions onAerospace and Electronics Systems,2011,47(2):912~927.
[94] Bell M R. Information Theory and Radar: Mutual Information and the Designand Analysis of Radar Waveforms and Systems [D]. Pasadena: California Institute ofTechnology,1988.
[95] Romero R, Goodman N A. Information-Theoretic Matched Waveform inSignal Dependent Interference[C]//IEEE Radar Conference. Rome,2008:30~35.
[96] Salmond D J, Parr M C. Track maintenance using measurements of targetextent [J]. IEE Radar Sonar Navigation,2003(6):389~395.
[97]纠博,刘宏伟，胡利平等.针对目标识别的波形优化设计方法[J].电子与信息学报,2009,31(11):2585~2590.
[98] Jiu B, Liu H W, Feng D Z, et al. Minimax Robust Transmission Waveformand Receiving Filter Design for Extended Target Detection with Imprecise PriorKnowledge[J]. Signal Processing,2012:210~218.
[99] Patton L K, Rigling B D. Modulus Constraints in Adaptive Radar WaveformDesign[C]//IEEE Radar Conference. Rome,2008.
[100]蒋飞,刘中,胡文等.任意频谱结构的连续混沌调频雷达波形设计[J].电子学报,2010,38(9):2195~2198.
[101] Levanon N, Mozeson E. Radar Signals[M]. New Jersey: John Wiley&Sons,2004.
[102] Li H J，Yang S H. Using Range Profiles as Feature Vectors to IdentifyAerospace Objects [J]. IEEE Transactions on Antennas and Propagation,1993,41(3):261~268.
[103] Rosenbach k, Schiller J. Non co-operative air target identification usingradar imagery: identification rate as a function of signal bandwidth[C]//IEEEInternational Radar Conference. Alexandria,2000:305~309.
[104] Shirman Y D, Leshchenkko S P, Orlenko V M. Advantages and Problems ofWideband Radar[C]//IEEE International Radar Conference.2003:16~21.
[105]闫锦.基于高分辨距离像的雷达目标识别研究[D].北京:航天科工集团二院,2003.
[106] Jin G H, Gao X Z, Li X. The Relationship between the Radar Bandwidth andRecognition Ability Based on the Ballistic Target[C]//International Conference onRadar Adelaide,2008:188~192.
[107]邓泳,王彩云.高分辨率雷达信号参数对目标识别的影响[J].现代雷达,2008,30(8):40~45.
[108] Haykin S. Cognitive radar networks[C]//The Fourth IEEE Workshop onSensor Array and Multichannel Process. Waltham,2006:1~24.
[109] Liang Q. Waveform Design and Diversity in Radar Sensor Networks:Theoretical Analysis and Application to Automatic Target Recognition[J].2006:684~689.
[110] Liang Q. Automatic target recognition using waveform diversity in radarsensor networks [J]. Pattern Recognition Letters,2008(29):377~381.
[111]范梅梅，廖东平，丁小峰.基于机会辐射源的被动雷达系统研究现状[J].电光与控制,2010.17(10):55~57.
[112]范梅梅，丁小峰，廖东平等.基于北斗卫星信号的无源雷达可行性研究[J].信号处理,2010,26(4):631~636.
[113] Inggs M, Lange G, Paichard Y. Autonomic Subsystems for Cognition inPassive Coherent Location.2010.
[114]纠博,刘宏伟,何学辉.基于凸优化的宽带雷达波形优化方法[J].电波科学学报,2009,24(2):264~269.
[115]裴炳南.高分辩雷达自动目标识别方法研究[D].西安:西安电子科技大学,2002.
[116] Lowe D, Webb A R． Optimized feature extraction and the Bayes decisionin feed-forward classifier networks [J]. IEEE Transactions on Pattern Analysis andMachine Intelligence,1991,13(4):355~364.
[117]许俊刚,柯有安.投影法雷达目标识别[J].电子学报,1994,22(7):28~34.
[118] Zyweck A, Bogner R E. Radar target classification of commercial aircraft[J].IEEE Transactions on Aerospace and Electronic Systems,1996,32(2):598~606.
[119] Li H J, Wang Y D, Wang L. Matching score properties between rangeprofiles of high-resolution radar targets [J]. IEEE Transactions on Antennas andPropagation,1996,44(4):444~452.
[120] Guo J, Guo X, Li P F. A New Method for Automatic TargetRecognition[C]//IEEE National Aerospace and Electronics Conference.Dayton,1997:1019~1024.
[121]陈江峰.基于HMM分类器和Relax算法特征提取的高分辨雷达目标自动识别问题研究[D].郑州:郑州大学,2003.
[122] Liao X J, Runkle P, Carin L. Identification of ground targets from sequentialhigh-range-resolution radar signatures. IEEE Transactions on Aerospace and ElectronicSystems [J],2002,38(4):1230~1242.
[123]孟继威,杨万麟.雷达目标识别中的双距离像子空间法[J].系统工程与电子技术,2004,6(6):724～725,835.
[124] Hudson S, Psaltis D. Correlation Filters for Aircraft Identification fromRadar Range Profiles [J].IEEE Transactions on Aerospace and Electronic Systems,1993,29(3):741~748.
[125]何松华,孙文峰.自适应相关匹配法在雷达目标距离剖面像识别中的应用[J].系统工程与电子技术,1997,19(1):10~16.
[126]廖东平.支持向量机方法及其在机载毫米波雷达目标识别中的应用研究[D].长沙:国防科学技术大学,2006.
[127] Kennaugh E M, Cosgriff R L. The Use of Impulse Response inElectromagnetic Scattering Problem[J]. IRE National Convention Record,1958. Part1:72~77.
[128]张劲东.自适应雷达系统中波形分集技术的研究[D].南京:南京理工大学,2010.
[129] Hyde J W, Alabaster C M. Correlation of Target Transfer Functions andRange Profiles as a Function of Aspect Angle and Resolution[C]//Waveform Designand Diversity. London,2008:1~5.
[130]潘仲英,江长荫,林萍实.用瞬态电磁场测量目标的冲激响应和宽带雷达截面[J].电子学报,1994,22(3):1~9.
[131] Mahafza B R. Radar System Analysis and Design using Matlab[M]. London:Taylor&Francis Group,2005.
[132]姜斌.地、海杂波建模及目标检测技术研究[D].长沙:国防科学技术大学,2006.
[133] Kay S M. Fundamentals of Statistical Signal Processing: Detection Theory.Vol. Ⅱ:Detection Theory[M]. New Jersey: Prentice-Hall Inc,1993.
[134] Lexa M A, Johnson D H. Distributed Structures, Sequential Optimization,and Quantization for Detection [J]. IEEE Transactions on signal processing,2008,56(4):1740~1745.
[135] Fan meimei, Liao Dongping, Ding Xiaofeng, etc..Relationship of targetrecognition performance and radar waveform parameters. Journal of Electronics(China),2011.28(1):10.
[136] Pillai S U, Li K Y, Beyer H. Waveform Design Optimization usingBandwidth and Energy Considerations[C]//IEEE Radar Conference. Rome,2008:1875~1879.
[137]张贤达.矩阵分析与应用[M].北京:清华大学出版社,2004.
[138] Chen C Y, Vaidyanathan P P. MIMO Radar Waveform Optimization WithPrior Information of the Extended Target and Clutter [J]. IEEE Transactions on SignalProcessing2009,57(9):3533~3544.
[139] Jongen H T, Meer K, Triesch E. Optimization Theory [M]. Boston: KluwerAcademic Publishers,2004.
[140] Trees H V. Detection, Estimation, and Modulation Theory,Part Ⅲ:Radar-Sonar processing and Gaussian Signals in Noise[M]. New York:JohnWiley&Sons,Inc,2001.
[141] Swerling P. Probability of Detection for Fluctuating Targets[R].1954,RAND Report.
[142] Swerling P. Detection of Fluctuating Pulsed Signals in the Presence of Noise[J]. IRE Transaction on Information Theory,1957, IT-3:175~178.
[143] Kay S M. Fundamentals of Statistical Signal Processing: Estimation Theory.Vol.Ⅰ[M]. New Jersey: Prentice-Hall Inc,1993.
[144] Guo D, Shitz S S, Verdú S. Mutual Information and Minimum Mean-SquareError in Gaussian Channels [J]. IEEE Transactions on Information Theory,2005,51(4):1261~1282.
[145] Nielsen P, Goodman N A. Integrated Detection and Tracking viaClosed-Loop Radar with Spatial-Domain Matched Illumination[C]//InternationalConference on Radar. Adelaide,2008:546~551.
[146] Cover T M, Thomas J A. Elements of Information Theory [M]. New York:John Wiley&Sons. Inc.,1991.
[147]傅祖芸.信息论-基础理论与应用[M].北京:电子工业出版社,2001.
[148] Mosca E. Probing Signal Design for Linear Channel Identification [J]. IEEETransactions on Information Theory,1972,18(4):481~487.
[149] Tartakovsky A. Asymptotic Optimality of Certain MultihypothesisSequential Tests: Non-i.i.d. Case [J]. Statistical Inference for Stochastic Processes,1998,1(3):265~295.
[150] Tartakovsky A. Asymptotically Optimal Sequential Tests forNonhomogeneous Processes[J].Sequential Analysis,1998,17(1):33~61.
[151]戴奉周.宽带雷达信号处理-检测、杂波抑制与认知跟踪[D].西安:西安电子科技大学,2010.
[152]张万宏.非平稳时间序列的预测方法研究[D].兰州:兰州理工大学,2007.
[153]曹星平,易东云.基于神经网络的时间序列预测方法进展[J].电脑与信息技术,1999(6):1~3.
[154] Vapnik V N. The Nature of Statistical Learning Theory[M]. New York:Springer-Verlag,1999.
[155] Vapnik V V.统计学习理论[M].北京:电子工业出版社,2004.
[156]李世平,周代刚,杨尚达等.基于改进LSSVM的动态测量误差实时预测方法[J].中国测试,2009.35(3):20~23.
[157] Suykens J, Vandewalle J. Least squares support vector machine classifiers[J].Neural Processing Letter,1999(9):293~300.
[158] Suykens J A, Lukas L, Vandewalle J. Sparse Approximation using LeastSquares Support Vector Machines[C]//Proceeding of the IEEE International Symposiumon Circuits and Systems. Geneva,2000:757~760.
[159]吴宗亮.基于核函数的雷达一维距离像目标识别方法研究[D].成都:电子科技大学,2009.
[160]姜静清.最小二乘支持向量机算法及应用研究[D].长春:吉林大学,2007.
[161]许丽佳,龙兵,王厚军.基于LSSVM-HMM的发射机故障预测研究[J].仪器仪表学报,2008,29(1):22~26.
[162]续瑞瑞.支持向量机方法在非线性时间序列预测中的应用[D].天津:南开大学,2005.
[163]陈磊,张士乔.基于贝叶斯最小二乘支持向量机的时用水量预测模型[J]..天津大学学报,2006,39(9):1037~1042.
[164]白鹏,张喜斌,张斌等,支持向量机理论及工程应用实例[M].西安:西安电子科技大学出版社,2008.
[165]江田汉,束炯.基于LSSVM的混沌时间序列的多步预测[J].控制与决策,2006,21(1):77~80.
[166]张弦,王宏力.嵌入维数自适应最小二乘支持向量机状态时间序列预测方法[J].航空学报,2010.31(12):2309~2314.
[167]付耀文.雷达目标融合识别研究[D].长沙:国防科学技术大学,2003.
[168] Tahani H, Keller J M. Information Fusion in Computer Vision using TheFuzzy Integral[J]. IEEE Transactions on Systems, Man and Cybernetics,1990,20(3):733~741.
[169] Cho S B, Kim J H. Multiple Network Fusion using Fuzzy Logic [J]. IEEETransactions on Neural Networks,1995,6(2):497~501.
[170] Cho S B, Kim J H. Combining Multiple Neural Networks by Fuzzy Integralfor Robust Classification [J]. IEEE Transactions on Systems, Man and Cybernetics,1995,25(2).380~384
[171] Sugeno M. Theroy of Fuzzy Integrals and Its Applications [D].Tokyo:Tokyo Institute of Technology,1974.
[172] Wald A. Sequential Analysis [M]. New York: John Wiley and SonsInc,1947.
[173] Bussagang J, Middleton D. Optimum Sequential Detection of Signals inNoise[J]. IRE transactions-Information Theory,1955, IT(1):5~18.
[174] Fan meimei, Liao Dongping, Ding Xiaofeng, etc.. Relationship of TargetIdentification Performance and Waveform Parameters[C]//The10th IEEE InternationalConference on Signal Processing. Beijing,2010:2227~2230.
[175] Naparst H.Dense Target Signal Processing [J]. IEEE Transactions on InformTheory,1991.37(2):317~327.
[176]刘福声,罗鹏飞.统计信号处理[M].长沙:国防科技大学出版社,1999.
[177] Reich E, Swerling P. On The Detection of a Sinewave in Gaussian Noise[R].Santa Monica: The Rand Corporation,1952:1~28.
[178] Kullback S, Leibler R A. On information and sufficiency [J]. Annals ofMathematical Statistics,1951,22(1):79~86.
[2]李丽亚.宽带雷达目标识别技术[D].西安:西安电子科技大学,2009.
[3] Haykin S. Cognitive Radar-A Way of The Future[J]. IEEE Signal ProcessingMagzine,2006,23(1):30~40.
[4] Dai F Z, Liu H W, Wang P H, et al. Adaptive Waveform Design forRange-Spread Target Tracking[J]. Electronics Letters,2010,46(11):793~794.
[5] Wicks M C. A Brief History of Waveform Diversity[C]//IEEE RadarConference. Pasadena,2009:1~6.
[6]周宇.基于知识的雷达自适应处理方法研究[D].西安:西安电子科技大学,2010.
[7] Guerci J R. Cognitive Radar: The Knowledge-Aided Fully AdaptiveApproach[M]. Boston: Artech House,2010.
[8] Gini F. Knowledge-Based Systems for Adaptive Radar[J]. IEEE SignalProcessing Magazine,2006,23(1):10~11.
[9] Capraro G T, Farina A, Griffiths H, et al. Knowledge-Based Radar Signal andData Processing-A Tutorial Overview [J]. IEEE Signal Process Magazine,2006(1):18~29.
[10] Guerci J R, Baranoski E J. Knowledge-Aided Adaptive Radar at DARPA [J].IEEE Signal Process Magazine,2006(1):41~50.
[11] Miranda S, Baker C, Woodbridge K, et al. Knowledge-Based ResourceManagement for Multifunction Radar [J]. IEEE Signal Process Magazine,2006(1):66~76.
[12] Baldygo W, Brown R, Antonik P, et al. Artificial Intelligence Applications toConstant False Alarm Rate (CFAR) Processing[C]//IEEE National Radar Conference.Lynnfield,1993:275~280.
[13] Wicks M, Melvin W, Chen P. An Efficient Architecture for NonhomogeneityDetection in Space-Time Adaptive Processing Airborne Early Warning Radar[C]//IEEInternational Radar Conference. Edinburgh,1997:295~299.
[14] DARPA. KASSPER: Knowledge-Aided Sensor Signal Processing and ExpertReasoning [EB/OL].http://www.darpa.mil/spo/pmgrams/kassper. html.2008-12-5
[15] Wicks M. Spectrum Crowding and Cognitive Radar [C]//2nd InternationalWorkshop on Cognitive Information Processing. Elba,2010:452~457.
[16] Cochran D, Suvorova S, Howard S D, et al. Waveform Libraries-Measures ofeffectiveness for radar scheduling[J].. IEEE Signal Process Magazine,2009(1):12-21.
[17] Calderbank R, Howard S D, Moran B. Waveform Diversity in Radar SignalProcessing-A Focus on The Use and Control of Degrees of Freedom[J].. IEEE Signalprocessing magazine,2009(1):32-41.
[18] Cochran D. Waveform-Agile Sensing: Opportunities and Challenges[C]//IEEE International Conference on Acoustics, Speech, and Signal Processing.2005:877~880.
[19] Goodman N A. Closed-Loop Radar with Adaptively MatchedWaveforms[C]//International Conference on Electromagnetics in AdvancedApplications. Torino,2007:468~471.
[20] Picciolo M, Griesbach J D, Gerlach K. Adaptive LFM WaveformDiversity[C]//IEEE Radar Conference. Rome,2008:1863~1868.
[21] Capraro C T, Capraro G T, Weiner D, et al. Knowledge Based Map SpaceTime Adaptive Processing (KBMapSTAP)[C]//International Conference on ImagingScience, Systems, and Technology. Las Vegas:2001.
[22] Capraro, C T, Capraro G T, Weiner D, et al. Improved STAP Performanceusing Knowledge-Aided Secondary Data Selection[C]//IEEE Radar Conference.Philadelphia,2004:361~365.
[23] DARPA. Adaptive Waveform Design for Detecting Low-Grazing-Angle andSmall-RCS Targets in Complex Maritime Environments [EB/OL].http://signal.ese.wustl.edu/DARPA/index.html.
[24] MURI. Adaptive Waveform Design for Full Spectral Dominance[EB/OL].http://signal.ese.wustl.edu/MURI/index.html.
[25] Baker J. Intelligence and radar systems[C]//IEEE Radar Conference.Washington,2010:1276~1279.
[26] Wei Y, Meng H, Wang X. Adaptive Single-Tone Waveform Design forTarget Recognition in Cognitive Radar[C]//IEEE Radar Conference. Guilin,2009.
[27] Haykin S, Xue Y, Davidson T N. Optimal Waveform Design For CognitiveRadar[C]//42nd Asilomar Conference on Signals, Sytems and Computers. PacificGrove,2008:3~7.
[28] Liu F, Wang J. An Optimal ADP Algorithm for Waveform Selection inCognitive Radar Systems [C]//Progress in Electromagnetic Research Symposium.Beijing,2009:728~730.
[29] Wei Y, Meng H, Liu Y,et al. Extended Target Recognition in Cognitive RadarNetworks[J]. Sensor,2010,10(11):10181~10197.
[30] Zhang J D, Zhu D Y, Zhang G. Multi-objective Waveform Design forCognitive Radar[C]//IEEE International Conference on CIE. Chengdu,2011:580~583.
[31] Inggs M. Passive Coherent Location as Cognitive Radar [J]. IEEE A&ESystems Magazine,2010(5):12~17.
[32] Haykin S. Cognitive Dynamic Systems: An Integrative Field that will be aHallmak of the21st Century[C]//10th IEEE International Conference on Informatics&Cognitive Computing. Banff,2011:2.
[33] Fan L, Wang J, Wang B. Time Allocation in Cognitive Radar for MultipleTarget Detection[C]//IEEE Third International Symposium on Intelligent InformationTechnology Application. Nanchang,2009:169~172.
[34] Haykin S, Zia A, Arasaratnam I, et al. Cognitive Tracking Radar[C]//IEEERadar Conference. Washington,2010:1467~1470.
[35] Haykin S. New Generation of Radar Systems Enabled withCognition[C]//IEEE Radar Conference. Washington,2010.
[36] Guerci J R. Cognitive Radar: A Knowledge-Aided Fully AdaptiveApproach[C]//IEEE Radar Conference.Washington.2010:135~1370.
[37]黄培康,遥感目标的特征提取与反演[Z].长沙:国防科技大学,2011.
[38] National Institutes of Health, N.I.o.M.H. and.“Definition of Cognition,”[EB/OL].http://science-education.nih.gov/supplements/nih5/Mental/other/glossary.html
[39] Sowelam S M, Tewfik A. Optimal Waveform Selection for Radar TargetClassification[C]//IEEE International Conference on Image Processing. SantaBarbara,1997:476~479.
[40] Sowelam S, Tewfik A. Waveform Selection in Radar Target Classification.IEEE Transactions on Information Theory,2000,46(3):1014~1029.
[41] LaScala F, Moran R, Evans W. Optimal Adaptive Waveform Selection forTarget Detection[C]//International Conference on Radar. Adelaide,2003:492~496.
[42] Wang B, Wang J Song X, et al. Stochastic Dynamic Programming Model ofAdaptive Waveform Selection[C]//International Conference on Networking, Sensingand Control. Chicago,2010:267~272.
[43] Zhang Z, Zhou J, Wang F. An algorithm of target tracking based on adaptiveLFM waveform design[C]//2nd International Conference on Advanced ComputerControl.Shenyang,2010:130~132
[44] Cochran D, Suvorova S, Howard D S, et al. Waveform Libraries-Measures ofEffectiveness for Radar Scheduling[J]. IEEE Signal Process Magazine,2009,26(1):12~21.
[45] Haykin S, Currie B, Kirubarajan T. Literature Search on Adaptive RadarTransmit Waveforms[R]. Ottawa: Defence R&D Canada,2002.
[46] Bonneau R J, Wicks M C. A numerical waveform design approach todecorrelate target and noise[C]//IEEE Radar Conference.Atlanta,2001:448~450.
[47]黎湘，范梅梅.认知雷达及其关键技术研究进展[J].电子学报,2012,40(6).
[48] Pillai S U, Oh S, Youla D C. Optimum Transmit-Receiver Design in thePresence of Signal-Dependent Interference and Channel Noise[J]. IEEE Transactions onInformation Theory,2000,46(2):577~584.
[49] Garren D A, Osborn M K, Odom A C, et al. Enhanced Target Detection andIdentification via Optimised Radar Transmission Pulse Shape[J]. IEE Proc.-Radar,Sonar Navig,2001,148(3):130~138.
[50] Kay S. Optimal Signal Design for Detection of Gaussian Point Targets inStationary Gaussian Clutter/Reverberation [J]. IEEE Journal of Selected Topics inSignal Processing,2007,1(1):31~41.
[51] Kay S. Optimum Radar Signal for Detection in Clutter [J].. IEEE Transactionson Aerospace and Electronic Systems,2007,43(3):1059~1065.
[52] Sira S P, Cochran D, Antonia P, et al. Adaptive Waveform Design forImproved Detection of Low-RCS Targets in Heavy Sea Clutter [J]. IEEE Journal ofSelected Topics in Signal Processing,2007,1(1):56~66.
[53] Guerci J R, Pillai S U. Theory and Application of Optimum Transmit-ReceiveRadar [C]//IEEE Internal radar Conference. Alexandria:2000:705~710.
[54] Leshem A, Naparstek O, Nehorai A. Information theoretic adaptive radarwaveform design for multiple extended targets[J]. IEEE Journal of Selected Topics inSignal Processing,2007,1(1):42~55.
[55] Leshem A, Naparstek O, Nehorai A. Adaptive Radar Waveform Design forMultiple Targets: Computational Aspects[C]//IEEE International Conference onAcoustics, Speech and Signal Processing. Honolulu,2007:910~912.
[56] Leshem A, Naparstek O, Nehorai A. Information Theoretic Radar WaveformDesign for Multiple Targets[C]//IEEE Waveform Diversity&Design. Pisa,2007:362~366.
[57] Patton L K. On the Satisfaction of Modulus and Ambiguity FunctionConstraints in Radar Waveform Optimization for Detection [D]. Dayton: Wright StateUniversity,2009.
[58] Pillai S U, Li K Y, Beyer H. Reconstruction of Constant Envelop Signals withGiven Fourier Transform Magnitude[C]//IEEE Radar Conference.2009.
[59] Jackson L, Kay S, Vankayalapati N. Iterative Method for Nonlinear FMSynthesis of Radar Signals[J]. IEEE Transactions on Aerospace and Electronic Systems,2010,46(2):910~917.
[60] Bell M R. Information Theory and Radar Waveform Design [J]. IEEETransactions on Information Theory,1993,39(5):1578~1597.
[61] Goodman N A, Venkata P R, Neifeld M A. Adaptive Waveform Design andSequential Hypothesis Testing for Target Recognition with Active Sensors[J]. IEEEJournal of Selected Topics in Signal Processing,2007,1(1):105~113.
[62] Yang Y, Blum R S. MIMO Radar Waveform Design Based on MutualInformation and Minimum Mean-Square Error Estimation [J]. IEEE Transactions onAerospace and Electronic Systems,2007,43(1):330~343.
[63] Romero R A, Goodman N A. Waveform Design in Signal-DependentInterference and Application to Target Recognition with Multiple Transmissions [J].IET Radar Sonar and Navigation,2009,3(4):328~340.
[64]纠博,刘宏伟,李丽亚等.一种基于互信息的波形优化设计方法[J].西安电子科技大学学报（自然科学版）,2008,35(4):678~684.
[65]纠博,刘宏伟,李丽亚等.雷达波形优化的特征互信息方法[J].西安电子科技大学学报（自然科学版）,2009,36(1):139~144.
[66] Sira S P, Li Y, Antonia P, et al. Waveform-Agile Sensing for Tracking-areview perspective[J]. IEEE Signal processing magazine,2009,26(1):53~64.
[67] Haykin S, Zia A, Xue Y, et al. Control Theoretic Approach to Tracking Radar:First Step towards Cognition[J]. Digital Signal Processing,2011:576~585.
[68] Sowelam S M, Tewfik A H. Optimal Waveforms for Wideband RadarImaging[J]. J. Franklin Institute,1998.335B(8):1341~1366.
[69] Linnehana, R, Bradyb D, Schindlerc J, et al. Using Cram′er-Rao Theory ForSAR Waveform Design[C]//IEEE International Radar Conference.2005:217~212.
[70] Bakker, R, Kirubarajan, T, Currie B, et al. Adaptive Radar Detection: ABayesian Approach[C]//EPSRC IEE Workshop Nonlinear Non-Gaussian SignalProcessing. Peebles:2002.
[71] Bakke R, Lopez G, Haykin S. Bayesian Approach to The Direct Filtering ofRadar Targets in Clutter[R]. Hamiltion: Adaptive Systems Laboratory, McMasterUniversity,2002.
[72] Bruno M G and Moura J M. Multiframe detector/tracker: Optimalperformance [J]. IEEE Transactions on Aerospace and Electronic System,2001,37:925~944.
[73] Haykin S, Currie B, Kirubarajan T, Adaptive Radar for Improved SmallTarget Detection in a Maritime Environment[R]. Hamilton: McMaster University,2003.
[74] Li Y, Moran W, Sira S P, et al. Adaptive Waveform Design inRapidly-Varying Radar Scenes[C]//International Waveform Diversity and Design.Kissimmee,2009:263~267.
[75] Bellman R. Dynamic Programming[M]. Princeton: Princeton UniversityPress,1957.
[76]王彬,汪晋宽，宋昕等.认知雷达中基于Q学习的自适应波形选择算法.系统工程与电子技术[J].2011,33(5):1007~1012.
[77] Bae J H, Goodman N A. Adaptive Waveforms for Target ClassDiscrimination[C]//International Waveform Diversity and Design. Pisa,2007:395~399.
[78] Romero R, Goodman N A. Improved Waveform Design for TargetRecognition with Multiple Transmissions[C]//International Waveform Diversity andDesign. Kissimmee2009:26~30.
[79] Venkata R P, Goodman N A. Novel Iterative Techniques for Radar TargetDiscrimination[C]//Waveform Design and Diversity.2006.
[80]黄德双.高分辨雷达智能信号处理技术[M].机械工业出版社.2001.
[81] Richards M A.雷达信号处理基础[M].北京:电子工业出版社,2008.
[82] Kennaugh E M, Moffatt D L. Transient and Impulse ResponseApproximations[J]Proceedings of the IEEE.1965,53(8):893~901.
[83]黄培康,殷红成,许小剑.雷达目标特性[M].北京:电子工业出版社.2006.
[84] Kennaugh E. The K-Pulse Concept[J]. IEEE Transactions on AntennasPropagation,1981,29(2):327~331.
[85]郭桂蓉,庄钊文,陈曾平.电磁特征提取与目标识别[M].长沙:国防科技大学出版社.1995.
[86] Baum C E, Rothwell E J, Chen K M,et al. The Singularity Expansion Methodand Its Application to Target Identification [J].Proceedings of IEEE,1991,79(10):1481~1491.
[87] Gjessing D. Target Adaptive Matched Illumination Radar: Principles&Applications[M]. Peter Peregrinus on behalf of the Institution of Electrical Engineers,1986.
[88] Pell C. Book review: Target Adaptive Matched Illumination Radar: Principlesand Applications [J]. IEE Proceedings of Communications, Radar and SignalProcessing,1986,133(6):581~582.
[89] Guerci J R, Schutz R W, Hulsmann J D. Constrained optimum matchedillumination-reception radar [P]. US Patent:51462291992,1992.
[90] Guerci J R. Optimum Matched Illumination-Reception Radar for TargetClassification[P]. US Patent:5381154,1995.
[91] Garren D A, Osborn M K, Odom A C, et al. Optimization of single transmitpulse shape to maximize detection and identification of ground mobile targets[C]//34thAsilomar Conference on Sinals, Systems, and Computers. Pacific Grove,2000:1535~1539.
[92] Garren D A, Osborn M K, Odom A C, et al. Optimal Transmission PulseShape for Detection and Identification with Uncertain Target Aspect[C]//IEEE RadarConference. Atlanta,2001:123~128.
[93] Romero R A, Bae J, Goodman N A. Theory and Application of SNR andMutual Information Matched Illumination Waveforms [J]. IEEE Transactions onAerospace and Electronics Systems,2011,47(2):912~927.
[94] Bell M R. Information Theory and Radar: Mutual Information and the Designand Analysis of Radar Waveforms and Systems [D]. Pasadena: California Institute ofTechnology,1988.
[95] Romero R, Goodman N A. Information-Theoretic Matched Waveform inSignal Dependent Interference[C]//IEEE Radar Conference. Rome,2008:30~35.
[96] Salmond D J, Parr M C. Track maintenance using measurements of targetextent [J]. IEE Radar Sonar Navigation,2003(6):389~395.
[97]纠博,刘宏伟，胡利平等.针对目标识别的波形优化设计方法[J].电子与信息学报,2009,31(11):2585~2590.
[98] Jiu B, Liu H W, Feng D Z, et al. Minimax Robust Transmission Waveformand Receiving Filter Design for Extended Target Detection with Imprecise PriorKnowledge[J]. Signal Processing,2012:210~218.
[99] Patton L K, Rigling B D. Modulus Constraints in Adaptive Radar WaveformDesign[C]//IEEE Radar Conference. Rome,2008.
[100]蒋飞,刘中,胡文等.任意频谱结构的连续混沌调频雷达波形设计[J].电子学报,2010,38(9):2195~2198.
[101] Levanon N, Mozeson E. Radar Signals[M]. New Jersey: John Wiley&Sons,2004.
[102] Li H J，Yang S H. Using Range Profiles as Feature Vectors to IdentifyAerospace Objects [J]. IEEE Transactions on Antennas and Propagation,1993,41(3):261~268.
[103] Rosenbach k, Schiller J. Non co-operative air target identification usingradar imagery: identification rate as a function of signal bandwidth[C]//IEEEInternational Radar Conference. Alexandria,2000:305~309.
[104] Shirman Y D, Leshchenkko S P, Orlenko V M. Advantages and Problems ofWideband Radar[C]//IEEE International Radar Conference.2003:16~21.
[105]闫锦.基于高分辨距离像的雷达目标识别研究[D].北京:航天科工集团二院,2003.
[106] Jin G H, Gao X Z, Li X. The Relationship between the Radar Bandwidth andRecognition Ability Based on the Ballistic Target[C]//International Conference onRadar Adelaide,2008:188~192.
[107]邓泳,王彩云.高分辨率雷达信号参数对目标识别的影响[J].现代雷达,2008,30(8):40~45.
[108] Haykin S. Cognitive radar networks[C]//The Fourth IEEE Workshop onSensor Array and Multichannel Process. Waltham,2006:1~24.
[109] Liang Q. Waveform Design and Diversity in Radar Sensor Networks:Theoretical Analysis and Application to Automatic Target Recognition[J].2006:684~689.
[110] Liang Q. Automatic target recognition using waveform diversity in radarsensor networks [J]. Pattern Recognition Letters,2008(29):377~381.
[111]范梅梅，廖东平，丁小峰.基于机会辐射源的被动雷达系统研究现状[J].电光与控制,2010.17(10):55~57.
[112]范梅梅，丁小峰，廖东平等.基于北斗卫星信号的无源雷达可行性研究[J].信号处理,2010,26(4):631~636.
[113] Inggs M, Lange G, Paichard Y. Autonomic Subsystems for Cognition inPassive Coherent Location.2010.
[114]纠博,刘宏伟,何学辉.基于凸优化的宽带雷达波形优化方法[J].电波科学学报,2009,24(2):264~269.
[115]裴炳南.高分辩雷达自动目标识别方法研究[D].西安:西安电子科技大学,2002.
[116] Lowe D, Webb A R． Optimized feature extraction and the Bayes decisionin feed-forward classifier networks [J]. IEEE Transactions on Pattern Analysis andMachine Intelligence,1991,13(4):355~364.
[117]许俊刚,柯有安.投影法雷达目标识别[J].电子学报,1994,22(7):28~34.
[118] Zyweck A, Bogner R E. Radar target classification of commercial aircraft[J].IEEE Transactions on Aerospace and Electronic Systems,1996,32(2):598~606.
[119] Li H J, Wang Y D, Wang L. Matching score properties between rangeprofiles of high-resolution radar targets [J]. IEEE Transactions on Antennas andPropagation,1996,44(4):444~452.
[120] Guo J, Guo X, Li P F. A New Method for Automatic TargetRecognition[C]//IEEE National Aerospace and Electronics Conference.Dayton,1997:1019~1024.
[121]陈江峰.基于HMM分类器和Relax算法特征提取的高分辨雷达目标自动识别问题研究[D].郑州:郑州大学,2003.
[122] Liao X J, Runkle P, Carin L. Identification of ground targets from sequentialhigh-range-resolution radar signatures. IEEE Transactions on Aerospace and ElectronicSystems [J],2002,38(4):1230~1242.
[123]孟继威,杨万麟.雷达目标识别中的双距离像子空间法[J].系统工程与电子技术,2004,6(6):724～725,835.
[124] Hudson S, Psaltis D. Correlation Filters for Aircraft Identification fromRadar Range Profiles [J].IEEE Transactions on Aerospace and Electronic Systems,1993,29(3):741~748.
[125]何松华,孙文峰.自适应相关匹配法在雷达目标距离剖面像识别中的应用[J].系统工程与电子技术,1997,19(1):10~16.
[126]廖东平.支持向量机方法及其在机载毫米波雷达目标识别中的应用研究[D].长沙:国防科学技术大学,2006.
[127] Kennaugh E M, Cosgriff R L. The Use of Impulse Response inElectromagnetic Scattering Problem[J]. IRE National Convention Record,1958. Part1:72~77.
[128]张劲东.自适应雷达系统中波形分集技术的研究[D].南京:南京理工大学,2010.
[129] Hyde J W, Alabaster C M. Correlation of Target Transfer Functions andRange Profiles as a Function of Aspect Angle and Resolution[C]//Waveform Designand Diversity. London,2008:1~5.
[130]潘仲英,江长荫,林萍实.用瞬态电磁场测量目标的冲激响应和宽带雷达截面[J].电子学报,1994,22(3):1~9.
[131] Mahafza B R. Radar System Analysis and Design using Matlab[M]. London:Taylor&Francis Group,2005.
[132]姜斌.地、海杂波建模及目标检测技术研究[D].长沙:国防科学技术大学,2006.
[133] Kay S M. Fundamentals of Statistical Signal Processing: Detection Theory.Vol. Ⅱ:Detection Theory[M]. New Jersey: Prentice-Hall Inc,1993.
[134] Lexa M A, Johnson D H. Distributed Structures, Sequential Optimization,and Quantization for Detection [J]. IEEE Transactions on signal processing,2008,56(4):1740~1745.
[135] Fan meimei, Liao Dongping, Ding Xiaofeng, etc..Relationship of targetrecognition performance and radar waveform parameters. Journal of Electronics(China),2011.28(1):10.
[136] Pillai S U, Li K Y, Beyer H. Waveform Design Optimization usingBandwidth and Energy Considerations[C]//IEEE Radar Conference. Rome,2008:1875~1879.
[137]张贤达.矩阵分析与应用[M].北京:清华大学出版社,2004.
[138] Chen C Y, Vaidyanathan P P. MIMO Radar Waveform Optimization WithPrior Information of the Extended Target and Clutter [J]. IEEE Transactions on SignalProcessing2009,57(9):3533~3544.
[139] Jongen H T, Meer K, Triesch E. Optimization Theory [M]. Boston: KluwerAcademic Publishers,2004.
[140] Trees H V. Detection, Estimation, and Modulation Theory,Part Ⅲ:Radar-Sonar processing and Gaussian Signals in Noise[M]. New York:JohnWiley&Sons,Inc,2001.
[141] Swerling P. Probability of Detection for Fluctuating Targets[R].1954,RAND Report.
[142] Swerling P. Detection of Fluctuating Pulsed Signals in the Presence of Noise[J]. IRE Transaction on Information Theory,1957, IT-3:175~178.
[143] Kay S M. Fundamentals of Statistical Signal Processing: Estimation Theory.Vol.Ⅰ[M]. New Jersey: Prentice-Hall Inc,1993.
[144] Guo D, Shitz S S, Verdú S. Mutual Information and Minimum Mean-SquareError in Gaussian Channels [J]. IEEE Transactions on Information Theory,2005,51(4):1261~1282.
[145] Nielsen P, Goodman N A. Integrated Detection and Tracking viaClosed-Loop Radar with Spatial-Domain Matched Illumination[C]//InternationalConference on Radar. Adelaide,2008:546~551.
[146] Cover T M, Thomas J A. Elements of Information Theory [M]. New York:John Wiley&Sons. Inc.,1991.
[147]傅祖芸.信息论-基础理论与应用[M].北京:电子工业出版社,2001.
[148] Mosca E. Probing Signal Design for Linear Channel Identification [J]. IEEETransactions on Information Theory,1972,18(4):481~487.
[149] Tartakovsky A. Asymptotic Optimality of Certain MultihypothesisSequential Tests: Non-i.i.d. Case [J]. Statistical Inference for Stochastic Processes,1998,1(3):265~295.
[150] Tartakovsky A. Asymptotically Optimal Sequential Tests forNonhomogeneous Processes[J].Sequential Analysis,1998,17(1):33~61.
[151]戴奉周.宽带雷达信号处理-检测、杂波抑制与认知跟踪[D].西安:西安电子科技大学,2010.
[152]张万宏.非平稳时间序列的预测方法研究[D].兰州:兰州理工大学,2007.
[153]曹星平,易东云.基于神经网络的时间序列预测方法进展[J].电脑与信息技术,1999(6):1~3.
[154] Vapnik V N. The Nature of Statistical Learning Theory[M]. New York:Springer-Verlag,1999.
[155] Vapnik V V.统计学习理论[M].北京:电子工业出版社,2004.
[156]李世平,周代刚,杨尚达等.基于改进LSSVM的动态测量误差实时预测方法[J].中国测试,2009.35(3):20~23.
[157] Suykens J, Vandewalle J. Least squares support vector machine classifiers[J].Neural Processing Letter,1999(9):293~300.
[158] Suykens J A, Lukas L, Vandewalle J. Sparse Approximation using LeastSquares Support Vector Machines[C]//Proceeding of the IEEE International Symposiumon Circuits and Systems. Geneva,2000:757~760.
[159]吴宗亮.基于核函数的雷达一维距离像目标识别方法研究[D].成都:电子科技大学,2009.
[160]姜静清.最小二乘支持向量机算法及应用研究[D].长春:吉林大学,2007.
[161]许丽佳,龙兵,王厚军.基于LSSVM-HMM的发射机故障预测研究[J].仪器仪表学报,2008,29(1):22~26.
[162]续瑞瑞.支持向量机方法在非线性时间序列预测中的应用[D].天津:南开大学,2005.
[163]陈磊,张士乔.基于贝叶斯最小二乘支持向量机的时用水量预测模型[J]..天津大学学报,2006,39(9):1037~1042.
[164]白鹏,张喜斌,张斌等,支持向量机理论及工程应用实例[M].西安:西安电子科技大学出版社,2008.
[165]江田汉,束炯.基于LSSVM的混沌时间序列的多步预测[J].控制与决策,2006,21(1):77~80.
[166]张弦,王宏力.嵌入维数自适应最小二乘支持向量机状态时间序列预测方法[J].航空学报,2010.31(12):2309~2314.
[167]付耀文.雷达目标融合识别研究[D].长沙:国防科学技术大学,2003.
[168] Tahani H, Keller J M. Information Fusion in Computer Vision using TheFuzzy Integral[J]. IEEE Transactions on Systems, Man and Cybernetics,1990,20(3):733~741.
[169] Cho S B, Kim J H. Multiple Network Fusion using Fuzzy Logic [J]. IEEETransactions on Neural Networks,1995,6(2):497~501.
[170] Cho S B, Kim J H. Combining Multiple Neural Networks by Fuzzy Integralfor Robust Classification [J]. IEEE Transactions on Systems, Man and Cybernetics,1995,25(2).380~384
[171] Sugeno M. Theroy of Fuzzy Integrals and Its Applications [D].Tokyo:Tokyo Institute of Technology,1974.
[172] Wald A. Sequential Analysis [M]. New York: John Wiley and SonsInc,1947.
[173] Bussagang J, Middleton D. Optimum Sequential Detection of Signals inNoise[J]. IRE transactions-Information Theory,1955, IT(1):5~18.
[174] Fan meimei, Liao Dongping, Ding Xiaofeng, etc.. Relationship of TargetIdentification Performance and Waveform Parameters[C]//The10th IEEE InternationalConference on Signal Processing. Beijing,2010:2227~2230.
[175] Naparst H.Dense Target Signal Processing [J]. IEEE Transactions on InformTheory,1991.37(2):317~327.
[176]刘福声,罗鹏飞.统计信号处理[M].长沙:国防科技大学出版社,1999.
[177] Reich E, Swerling P. On The Detection of a Sinewave in Gaussian Noise[R].Santa Monica: The Rand Corporation,1952:1~28.
[178] Kullback S, Leibler R A. On information and sufficiency [J]. Annals ofMathematical Statistics,1951,22(1):79~86.