基于信号空域稀疏性的阵列处理理论与方法
|
摘要
随着低截获概率技术的广泛应用和空间辐射源数目的显著增加，阵列处理系统所面临的信号环境日趋复杂，低信噪比、小样本和空域邻近信号等非理想条件下的阵列处理需求也日益普遍，这些问题的出现给当前以子空间类方法为主体的阵列信号处理理论及方法的适用性带来了严峻挑战。结合入射信号的空域稀疏性在阵列处理领域所取得的初步探索成果已经显示了它们在上述非理想信号环境中实现超分辨测向的极大优势。然而，这些方法的性能受到了相应稀疏重构算法自身局限性等因素的制约，难以满足各种阵列处理系统在测向精度等方面的需求。
本文从阵列处理和稀疏重构这两类问题的联系与区别角度出发，建立了以准确的信号空域特征重构为前提、以高精度波达方向估计为目的的稀疏重构阵列处理基本理论框架，据此提出了基于相关向量机的阵列测向方法RVM-DOA(Relevance Vector Machine based Direction-of-Arrival estimation)，并理论分析了其收敛性和可分辨信号数等性质。该方法充分利用入射信号的空域稀疏性，在重构过程中通过避免不同信号之间的相互耦合实现了对信号空域特征更准确的恢复，并结合后续精测向过程得到了高精度的波达方向估计结果。RVM-DOA方法可直接用于对窄带信号的波达方向估计，具有显著优于子空间类方法的低信噪比、小样本适应能力和超分辨能力，以及显著优于已有稀疏重构类方法的测向精度，而且在相关信号处理等方面也体现出了明显优势。
为了更好地满足阵列测向系统在计算效率和对相关信号的测向精度要求，本文随后提出了基于阵列输出协方差向量空域稀疏重构的窄带独立/相关信号测向方法CV-RVM (Covariance Vector-exploited RVM)和基于空域滤波的窄带相关信号测向方法SF RVM-DOA (Spatial Filtering-based RVM-DOA)。CV-RVM方法利用协方差向量包含入射信号空域分布信息及其在适宜样本数条件下具有比原始阵列输出更高信噪比的特点，在深入分析协方差向量估计误差一阶和二阶统计特性的基础上，以牺牲一定的小样本适应能力为代价，得到了显著高于RVM-DOA方法的计算效率，以及明显增强的低信噪比适应能力和超分辨能力。该方法还可以通过阵列结构的优化设计，实现对多于阵列阵元数的同时入射信号的分辨，这一结论得到了严格的理论分析结果的支持。论文随后提出了用于窄带相关信号测向的SFRVM-DOA方法，以弥补各种稀疏重构类测向方法在相关信号测向精度方面所存在的不足。SF RVM-DOA方法借助一组依赖于信号方向的空域滤波器实现对多个时域混叠相关信号的分离，并通过滤波器参数与信号方向的联合优化得到高精度的测向结果。该方法适用于任意结构阵列，且在空时白噪声等典型信号环境中的测向精度十分逼近甚至达到了克拉美－罗下界，这是现有各种方法都无法做到的。
针对雷达、通信等领域所使用的各种具有典型时延相关特征的宽带信号的测向问题，利用阵列输出协方差向量的空域稀疏性和时域调制信息，提出了分别适用于独立和多径宽带信号波达方向估计的WCV-RVM (Wideband CV-RVM)和STS-RVM (Sequential Temporal-Spatial RVM)方法，避免了常规子空间类宽带测向方法的频域分解与聚焦过程。WCV-RVM方法较好地继承了窄带信号稀疏重构类测向方法在低信噪比、小样本适应能力和超分辨能力等方面的优势，理论分析结果还证明了该方法借助阵列结构优化设计实现对多于阵列阵元数的同时入射信号的分辨，以及在保证无模糊测向的条件下显著放宽对阵列阵元间距要求的性质。STS-RVM方法进一步结合多径分量在时延域的稀疏性，实现对多径时延远小于信号时宽的多径分量的分辨，然后基于该时延估计结果完成对多个同时入射宽带信号所有多径分量的波达方向估计，相应的测向性能远优于已有宽带子空间类方法。
实际系统中广泛存在的阵列互耦、幅相不一致和阵元位置不准确等模型误差会对稀疏重构类阵列测向方法的性能产生显著的负面影响。在充分利用入射信号空域稀疏性的基础上，本文随后研究了模型失配条件下的阵列误差校正与信号波达方向估计方法，以增强测向系统对上述各类典型阵列误差的适应能力。该方法以阵列互耦、幅相不一致和阵元位置不准确等误差条件下统一的阵列观测模型为基础，其模型和实现过程适用于其中任一阵列误差类型，并可以方便地推广至多种误差同时存在时的阵列测向环境。论文进一步分析了模型失配条件下阵列校准与信号测向精度的理论下界，该理论结果同时适用于上述三类典型误差类型，而且具有比已有结果更简洁和直观的形式。与传统阵列误差校正方法相比，新方法在低信噪比、小样本适应能力和超分辨能力等方面仍然具备显著优势，在典型信号环境中则体现出了更好的误差校正性能和更高的测向精度。
最后，本文针对多模型多观测条件下的联合重构问题研究了相应的贝叶斯稀疏重构技术，并在充分分析重构问题本身和重构方法基本性质的基础上，将该技术应用于解决时变阵列中的窄带信号测向和基于频域分解的一般宽带信号测向等两类典型的阵列处理问题，相关模型、方法和理论成果进一步充实了基于信号空域稀疏性的阵列处理理论与方法体系。
The requirement of direction-of-arrival (DOA) estimation in demanding scenarios oflow signal-to-noise ratio (SNR), much limited snapshots and spatially adjacent targetshave emerged in various areas, partially due to the widespread applications of the lowprobability of intercept (LPI) techniques and the increase of the electromagneticemitters. The state-of-the-art array processing theory and technique dominated by thesubspace-based methods can hardly meet the requirement. Recently, the sparserepresentation algorithms have been introduced into this area to make use of the priorinformation of the spatial sparsity of the incident signals, and the correspondingmethods have witnessed a significant enhancement in adaptation to theabove-mentioned demanding scenarios. Unfortunately, the DOA estimation precision ofthem is constrained by the shortcomings of the sparse reconstruction algorithms andmay be not satisfyingly enough.
In this dissertation, a spatial sparsity-based array signal processing framework isestablished after analyzing the relations and distinctions between the array signalprocessing and the sparse reconstruction problems. This framework grounds on aprecise spatial reconstruction of the signal characters and aims at high-precision DOAestimation. The relevance vector machine (RVM) is then introduced for theimplementation of the framework, and a method named RVM-DOA is proposed fornarrowband signals. Theoretical analyses are also carried out to reveal the properties ofthe method in convergence and separable signal number. RVM-DOA makes good useof the spatial sparsity of the incident signals and achieves precise signal recovery byevading the interaction of different signals, which is then exploited to obtainhigh-precision narrowband DOA estimates. It is demonstrated empirically thatRVM-DOA owns much enhanced superresolution and adaptation to low SNR andlimited snapshots, and surpasses the existing sparsity-inducing methods in DOAestimation precision, and it also performs well in separating correlated signals.
After that, two methods of CV-RVM (Covariance Vector-exploited RVM) and SFRVM-DOA (Spatial Filtering-based RVM-DOA) are proposed to improve thecomputational efficiency of RVM-DOA at low SNR and its DOA estimation precisionof correlated signals, respectively. By exploiting the first-and second-order statistics ofthe covariance vector estimation errors, CV-RVM realizes DOA estimation of bothindependent and correlated narrowband signals by reconstructing those vectors, whichcontains the directional information of the incident signals and their SNR is higher thanthat of the raw array output vectors when sufficient snapshots have been collected.CV-RVM gains much improved computational efficiency than RVM-DOA at theexpense of adaptation to much limited snapshots, and it also owns better performance at low SNR and for spatially adjacent signals. Moreover, CV-RVM is able to separatemore signals than the sensor number in well-designed arrays, which is supported bytheoretical analysis. SF RVM-DOA introduces a group of spatial filters to separate thesimultaneously impinging correlated signals and realize DOA estimation of them, so asto make up for the negative influence of the inter-signal correlation on the DOAestimation precision of the existing sparse reconstruction methods. SF RVM-DOAadapts to arbitrary array geometries and approaches or even reaches the Cramer-RaoLower Bound (CRLB) in scenarios of adequate SNR and snapshots, which has neverbeen achieved by the existing methods.
When the incident signals are wideband and own typical temporal correlationcharacters, the directions of them can still be estimated by recovering the covariancevectors of the array outputs. Following this idea, two methods of WCV-RVM(Wideband CV-RVM) and STS-RVM (Sequential Temporal-Spatial RVM) areproposed for independent and multipath wideband DOA estimation, respectively. Theproposed methods do not require similar spectral decomposition and focusingprocedures as the subspace-based methods. WCV-RVM makes use of the spatialsparsity and modulation characters of the wideband signals, thus it well reserves thesuperiorities of its narrowband counterparts in superresoltion and adaptation to lowSNR and limited snapshots. It is also able to separate more signals than sensors inwell-designed arrays and owns a much relaxed restriction on the array geometry inavoiding DOA estimation ambiguity. Besides the spatial sparsity and modulationcharacters, STS-RVM also exploits the sparsity of the multipath signals in thetime-delay domain, it succeeds to estimate the time delays that are much smaller thanthe signal time-width, and its DOA estimation performance for all the multipathcomponents when more than one wideband signals impinge simultaneously surpassesthe subspace-based methods significantly.
The widespread array imperfections, with mutual coupling, gain/phase uncertaintyand sensor location error being the most typical examples, play a significantly negativerole in the sparsity-inducing array processing methods. In order to enhance theadaptability of those methods to the array imperfections, we propose a joint arraycalibration and direction estimation method by taking the spatial sparsity of the incidentsignals into account. A unified framework of the uncalibrated array output is developedfirst in the presence of a single array imperfection type, it applies to any of theabove-mentioned typical array imperfections after reification, and can be easilyextended to scenarios when more than one type of array imperfections coexist. Thelower bounds of the array calibration and direction estimation precisions are thenanalyzed theoretically. The theoretical results also apply in any of the single-typeimperfection scenarios, and they have much compact expressions than the existingcounterparts. When compared with the previous array calibration methods, the proposed one dominates in adaptation to the much demanding environments, including low SNR,limited snapshots and spatially adjacent sources, and it exceeds with large margin inarray calibration and DOA estimation precisions.
A sparse Bayesian joint reconstruction technique is finally studied for themulti-model multi-measurement (M4) reconstruction problems. The properties of the M4problem itself and the joint reconstruction method are analyzed theoretically to indicatethe general behavior of them, and the technique is then introduced to solve twoimportant array signal processing problems, including narrowband DOA estimation intime-varying array systems and wideband DOA estimation via spectral decomposition.The joint reconstruction technique and its applications help to enrich the spatialsparsity-based array signal processing theory largely.
本文从阵列处理和稀疏重构这两类问题的联系与区别角度出发，建立了以准确的信号空域特征重构为前提、以高精度波达方向估计为目的的稀疏重构阵列处理基本理论框架，据此提出了基于相关向量机的阵列测向方法RVM-DOA(Relevance Vector Machine based Direction-of-Arrival estimation)，并理论分析了其收敛性和可分辨信号数等性质。该方法充分利用入射信号的空域稀疏性，在重构过程中通过避免不同信号之间的相互耦合实现了对信号空域特征更准确的恢复，并结合后续精测向过程得到了高精度的波达方向估计结果。RVM-DOA方法可直接用于对窄带信号的波达方向估计，具有显著优于子空间类方法的低信噪比、小样本适应能力和超分辨能力，以及显著优于已有稀疏重构类方法的测向精度，而且在相关信号处理等方面也体现出了明显优势。
为了更好地满足阵列测向系统在计算效率和对相关信号的测向精度要求，本文随后提出了基于阵列输出协方差向量空域稀疏重构的窄带独立/相关信号测向方法CV-RVM (Covariance Vector-exploited RVM)和基于空域滤波的窄带相关信号测向方法SF RVM-DOA (Spatial Filtering-based RVM-DOA)。CV-RVM方法利用协方差向量包含入射信号空域分布信息及其在适宜样本数条件下具有比原始阵列输出更高信噪比的特点，在深入分析协方差向量估计误差一阶和二阶统计特性的基础上，以牺牲一定的小样本适应能力为代价，得到了显著高于RVM-DOA方法的计算效率，以及明显增强的低信噪比适应能力和超分辨能力。该方法还可以通过阵列结构的优化设计，实现对多于阵列阵元数的同时入射信号的分辨，这一结论得到了严格的理论分析结果的支持。论文随后提出了用于窄带相关信号测向的SFRVM-DOA方法，以弥补各种稀疏重构类测向方法在相关信号测向精度方面所存在的不足。SF RVM-DOA方法借助一组依赖于信号方向的空域滤波器实现对多个时域混叠相关信号的分离，并通过滤波器参数与信号方向的联合优化得到高精度的测向结果。该方法适用于任意结构阵列，且在空时白噪声等典型信号环境中的测向精度十分逼近甚至达到了克拉美－罗下界，这是现有各种方法都无法做到的。
针对雷达、通信等领域所使用的各种具有典型时延相关特征的宽带信号的测向问题，利用阵列输出协方差向量的空域稀疏性和时域调制信息，提出了分别适用于独立和多径宽带信号波达方向估计的WCV-RVM (Wideband CV-RVM)和STS-RVM (Sequential Temporal-Spatial RVM)方法，避免了常规子空间类宽带测向方法的频域分解与聚焦过程。WCV-RVM方法较好地继承了窄带信号稀疏重构类测向方法在低信噪比、小样本适应能力和超分辨能力等方面的优势，理论分析结果还证明了该方法借助阵列结构优化设计实现对多于阵列阵元数的同时入射信号的分辨，以及在保证无模糊测向的条件下显著放宽对阵列阵元间距要求的性质。STS-RVM方法进一步结合多径分量在时延域的稀疏性，实现对多径时延远小于信号时宽的多径分量的分辨，然后基于该时延估计结果完成对多个同时入射宽带信号所有多径分量的波达方向估计，相应的测向性能远优于已有宽带子空间类方法。
实际系统中广泛存在的阵列互耦、幅相不一致和阵元位置不准确等模型误差会对稀疏重构类阵列测向方法的性能产生显著的负面影响。在充分利用入射信号空域稀疏性的基础上，本文随后研究了模型失配条件下的阵列误差校正与信号波达方向估计方法，以增强测向系统对上述各类典型阵列误差的适应能力。该方法以阵列互耦、幅相不一致和阵元位置不准确等误差条件下统一的阵列观测模型为基础，其模型和实现过程适用于其中任一阵列误差类型，并可以方便地推广至多种误差同时存在时的阵列测向环境。论文进一步分析了模型失配条件下阵列校准与信号测向精度的理论下界，该理论结果同时适用于上述三类典型误差类型，而且具有比已有结果更简洁和直观的形式。与传统阵列误差校正方法相比，新方法在低信噪比、小样本适应能力和超分辨能力等方面仍然具备显著优势，在典型信号环境中则体现出了更好的误差校正性能和更高的测向精度。
最后，本文针对多模型多观测条件下的联合重构问题研究了相应的贝叶斯稀疏重构技术，并在充分分析重构问题本身和重构方法基本性质的基础上，将该技术应用于解决时变阵列中的窄带信号测向和基于频域分解的一般宽带信号测向等两类典型的阵列处理问题，相关模型、方法和理论成果进一步充实了基于信号空域稀疏性的阵列处理理论与方法体系。
The requirement of direction-of-arrival (DOA) estimation in demanding scenarios oflow signal-to-noise ratio (SNR), much limited snapshots and spatially adjacent targetshave emerged in various areas, partially due to the widespread applications of the lowprobability of intercept (LPI) techniques and the increase of the electromagneticemitters. The state-of-the-art array processing theory and technique dominated by thesubspace-based methods can hardly meet the requirement. Recently, the sparserepresentation algorithms have been introduced into this area to make use of the priorinformation of the spatial sparsity of the incident signals, and the correspondingmethods have witnessed a significant enhancement in adaptation to theabove-mentioned demanding scenarios. Unfortunately, the DOA estimation precision ofthem is constrained by the shortcomings of the sparse reconstruction algorithms andmay be not satisfyingly enough.
In this dissertation, a spatial sparsity-based array signal processing framework isestablished after analyzing the relations and distinctions between the array signalprocessing and the sparse reconstruction problems. This framework grounds on aprecise spatial reconstruction of the signal characters and aims at high-precision DOAestimation. The relevance vector machine (RVM) is then introduced for theimplementation of the framework, and a method named RVM-DOA is proposed fornarrowband signals. Theoretical analyses are also carried out to reveal the properties ofthe method in convergence and separable signal number. RVM-DOA makes good useof the spatial sparsity of the incident signals and achieves precise signal recovery byevading the interaction of different signals, which is then exploited to obtainhigh-precision narrowband DOA estimates. It is demonstrated empirically thatRVM-DOA owns much enhanced superresolution and adaptation to low SNR andlimited snapshots, and surpasses the existing sparsity-inducing methods in DOAestimation precision, and it also performs well in separating correlated signals.
After that, two methods of CV-RVM (Covariance Vector-exploited RVM) and SFRVM-DOA (Spatial Filtering-based RVM-DOA) are proposed to improve thecomputational efficiency of RVM-DOA at low SNR and its DOA estimation precisionof correlated signals, respectively. By exploiting the first-and second-order statistics ofthe covariance vector estimation errors, CV-RVM realizes DOA estimation of bothindependent and correlated narrowband signals by reconstructing those vectors, whichcontains the directional information of the incident signals and their SNR is higher thanthat of the raw array output vectors when sufficient snapshots have been collected.CV-RVM gains much improved computational efficiency than RVM-DOA at theexpense of adaptation to much limited snapshots, and it also owns better performance at low SNR and for spatially adjacent signals. Moreover, CV-RVM is able to separatemore signals than the sensor number in well-designed arrays, which is supported bytheoretical analysis. SF RVM-DOA introduces a group of spatial filters to separate thesimultaneously impinging correlated signals and realize DOA estimation of them, so asto make up for the negative influence of the inter-signal correlation on the DOAestimation precision of the existing sparse reconstruction methods. SF RVM-DOAadapts to arbitrary array geometries and approaches or even reaches the Cramer-RaoLower Bound (CRLB) in scenarios of adequate SNR and snapshots, which has neverbeen achieved by the existing methods.
When the incident signals are wideband and own typical temporal correlationcharacters, the directions of them can still be estimated by recovering the covariancevectors of the array outputs. Following this idea, two methods of WCV-RVM(Wideband CV-RVM) and STS-RVM (Sequential Temporal-Spatial RVM) areproposed for independent and multipath wideband DOA estimation, respectively. Theproposed methods do not require similar spectral decomposition and focusingprocedures as the subspace-based methods. WCV-RVM makes use of the spatialsparsity and modulation characters of the wideband signals, thus it well reserves thesuperiorities of its narrowband counterparts in superresoltion and adaptation to lowSNR and limited snapshots. It is also able to separate more signals than sensors inwell-designed arrays and owns a much relaxed restriction on the array geometry inavoiding DOA estimation ambiguity. Besides the spatial sparsity and modulationcharacters, STS-RVM also exploits the sparsity of the multipath signals in thetime-delay domain, it succeeds to estimate the time delays that are much smaller thanthe signal time-width, and its DOA estimation performance for all the multipathcomponents when more than one wideband signals impinge simultaneously surpassesthe subspace-based methods significantly.
The widespread array imperfections, with mutual coupling, gain/phase uncertaintyand sensor location error being the most typical examples, play a significantly negativerole in the sparsity-inducing array processing methods. In order to enhance theadaptability of those methods to the array imperfections, we propose a joint arraycalibration and direction estimation method by taking the spatial sparsity of the incidentsignals into account. A unified framework of the uncalibrated array output is developedfirst in the presence of a single array imperfection type, it applies to any of theabove-mentioned typical array imperfections after reification, and can be easilyextended to scenarios when more than one type of array imperfections coexist. Thelower bounds of the array calibration and direction estimation precisions are thenanalyzed theoretically. The theoretical results also apply in any of the single-typeimperfection scenarios, and they have much compact expressions than the existingcounterparts. When compared with the previous array calibration methods, the proposed one dominates in adaptation to the much demanding environments, including low SNR,limited snapshots and spatially adjacent sources, and it exceeds with large margin inarray calibration and DOA estimation precisions.
A sparse Bayesian joint reconstruction technique is finally studied for themulti-model multi-measurement (M4) reconstruction problems. The properties of the M4problem itself and the joint reconstruction method are analyzed theoretically to indicatethe general behavior of them, and the technique is then introduced to solve twoimportant array signal processing problems, including narrowband DOA estimation intime-varying array systems and wideband DOA estimation via spectral decomposition.The joint reconstruction technique and its applications help to enrich the spatialsparsity-based array signal processing theory largely.
引文
[1] Trees H L V. Optimum array processing. Part IV of detection, estimation, andmodulation theory [M]. New York, John Wiley&Sons, Inc.,2002.
[2]王永良,陈辉,等.空间谱估计理论与算法[M].北京:清华大学出版社,2004.
[3] Krim H, Viberg M. Two decades of array signal processing research: The parametricapproach [J]. IEEE Signal Processing Magazine,1996(7):67-94.
[4] Capon J. High-resolution frequency-wavenumber spectrum analysis [J]. Proceedingsof the IEEE,1969,57(8):1408-1418.
[5] Applebaum S P. Adaptive arrays [J]. IEEE Trans. Antennas and Propagation,1976,24(9):585-598.
[6] Gabriel W F. Spectral analysis and adaptive array superresolution techniques [J].Proceedings of the IEEE,1980,68(6):654-666.
[7] Schmidt R O. Multiple emitter location and signal parameter estimation [J]. IEEETrans. Antennas and Propagation,1986,34(2):276-280.
[8] Roy R, Kailath T. ESPRIT-Estimation of signal parameters via rotational invariancetechniques [J]. IEEE Trans. Acoustics, Speech and Signal Processing,1989,37(7):984-995.
[9] Barabell A J. Improving the resolution performance of eigenstructure-baseddirection-finding algorithms [C]. Proceedings of ICASSP, Boston, USA,1983:336-339.
[10] Kumaresan R, Tufts D W. Estimating the angles of arrival of multiple plane waves[J]. IEEE Trans. Aerospace and Electronic Systems,1983,19(1):134-139.
[11] Lee H B, Wengrovitz M S. Resolution threshold of beamspace MUSIC for twoclosely spaced emitters [J]. IEEE Trans. Acoustics, Speech, and Signal Processing,1990,38(9):1545-1559.
[12] Xu G, Silverstein S D, et al. Beamspace ESPRIT [J]. IEEE Trans. SignalProcessing,1994,42(2):349-356.
[13] Stoica P, Nehorai A. MUSIC, maximum likelihood, and Cramer-Rao bound [J].IEEE Trans. Acoustics, Speech, and Signal Processing,1989,37(5):720-741.
[14] Shan T J, Kailath T. On spatial smoothing for direction-of-arrival estimation ofcoherent signals [J]. IEEE Trans. Acoustics, Speech and Signal Processing,1985,33(4):806-811.
[15] Pillai S U, Kwon B H. Forward/Backward spatial smoothing techniques forcoherent signal identification [J]. IEEE Trans. Acoustics, Speech and Signal Processing,1989,37(1):8-15.
[16] Moghaddamjoo A. Application of spatial filters to DOA estimation of coherentsources [J]. IEEE Trans. Signal Processing,1991,39(1):221-224.
[17] Delis A, Papadopoulos G. Enhanced forward/backward spatial filtering method forDOA estimation of narrowband coherent sources [J]. IEE Proc.–Radar, SonarNavigation,1996,143(1):10-16.
[18] Xin J, Sano A. Direction estimation of coherent signals using spatial signature [J].IEEE Signal Processing Letters,2002,9(12):414-417.
[19] Han F M, Zhang X D. An ESPRIT-like algorithm for coherent DOA estimation [J].IEEE Antennas and Wireless Propagation Letters,2005,4:443-446.
[20] Choi Y H. ESPRIT-based coherent source localization with forward and backwardvectors [J]. IEEE Trans. Signal Processing,2010,58(12):6416-6420.
[21] Uttam S, Goodman N A. Superresolution of coherent sources in real-beam data [J].IEEE Trans. Aerospace and Electronic Systems,2010,46(3):1557-1566.
[22] Yeh C C, Lee J H, Chen Y M. Estimating two-dimensional angles of arrival incoherent source environment [J]. IEEE Trans. Acoustics, Speech, and Signal Processing,1989,37(1):153-155.
[23] Hua Y. A pencil-MUSIC algorithm for finding two-dimensional angles andpolarizations using crossed dipoles [J]. IEEE Trans. Antennas and Propagation,1993,41(3):370-376.
[24] Chen Y M. On spatial smoothing for two-dimensional direction-of-arrivalestimation of coherent signals [J]. IEEE Trans. Signal Processing,1997,45(7):1689-1696.
[25] Chen F J, Fung C C. Estimation of two-dimensional frequencies using modifiedmatrix pencil method [J]. IEEE Trans. Signal Processing,2007,55(2):718-724.
[26] Chen F J, Kwong S, Kok C W. ESPRIT-like two-dimensional DOA estimation forcoherent signals [J]. IEEE Trans. Aerospace and Electronic Systems,2010,46(3):1477-1484.
[27] Wang G, Xin J, et al. Computationally efficient subspace-based method fortwo-dimensional direction estimation with L-shaped array [J]. IEEE Trans. SignalProcessing,2011,59(7):3197-3212.
[28] Liu K J R, O’Leary D P, et al. URV ESPRIT for tracking time-varying signals [J].IEEE Trans. Signal Processing,1994,42(12):3441-3448.
[29] Yang B. Projection approximation subspace tracking [J]. IEEE Trans. SignalProcessing,1995,43(1):95-107.
[30] Xin J, Sano A. Efficient subspace-based algorithm for adaptive bearing estimationand tracking [J]. IEEE Trans. Signal Processing,2005,53(12):4485-4505.
[31] Su G, Morf M. The signal subspace approach for multiple wide-band emitterlocation [J]. IEEE Trans. Acoustics, Speech, and Signal Processing,1983,31(6):1502-1522.
[32] Wang H, Kaveh M. Coherent signal-subspace processing for the detection andestimation of angles of arrival of multiple wide-band sources [J]. IEEE Trans. Acoustics,Speech, and Signal Processing,1985,33(4):823-831.
[33] Valaee S, Kabal P. Wideband array processing using a two-sided correlationtransformation [J]. IEEE Trans. Signal Processing,1995,43(1):160-172.
[34] Claudio E D D, Parisi R. WAVES: Weighted average of signal subspaces forrobust wideband direction finding [J]. IEEE Trans. Signal Processing,2001,49(10):2179-2191.
[35] Yoon Y S, Kaplan L M, McClellan J H. TOPS: New DOA estimator for widebandsignals [J]. IEEE Trans. Signal Processing,2006,54(6):1977-1989.
[36] Wu Q, K M Wong. UN-MUSIC and UN-CLE: An application of generalizedcorrelation analysis to the estimation of the direction of arrival of signals in unknowncorrelated noise [J]. IEEE Trans. Signal Processing,1994,42(9):2331-2343.
[37] Belouchrani A, Amin M G, Meraim K A. Direction finding in correlated noisefields based on joint block-diagonalization of spatio-temporal correlation matrices [J].IEEE Signal Processing Letters,1997,4(9):266-268.
[38] Wu Y, Hou C, et al. Direction-of-arrival estimation in the presence of unknownnonuniform noise fields [J]. IEEE Journal of Oceanic Engineering,2006,31(2):504-510.
[39] Friedlander B, Weiss A J. Direction finding in the presence of mutual coupling [J].IEEE Trans. Antennas and Propagation,1991,39(3):273-284.
[40] Flanagan B P, Bell K L. Array self-calibration with large sensor position errors [J].Signal Processing,2001,81:2201-2214.
[41] Belloni F, Koivunen V. Beamspace transform for UCA: Error analysis and biasreduction [J]. IEEE Trans. Signal Processing,2006,54(8):3078-3089.
[42] Sellone F. A novel online mutual coupling compensation algorithm for uniformand linear arrays [J]. IEEE Trans. Signal Processing,2007,55(2):560-573.
[43] Liu Z, Huang Z, et al. DOA estimation with uniform linear arrays in the presenceof mutual coupling via blind calibration [J]. Signal Processing,2009,89:1446-1456.
[44] Ye Z, Dai J, et al. DOA estimation for uniform linear array with mutual coupling[J]. IEEE Trans. Aerospace and Electronic Systems,2009,45(1):280-288.
[45] Ferreol A, Larzabal P, Viberg M. Statistical analysis of the MUSIC algorithm inthe presence of modeling errors, Taking into account the resolution probability [J].IEEE Trans. Signal Processing,2010,58(8):4156-4166.
[46] Wu Y, Lui H S. Improved DOA estimations using the receiving mutualimpedances for mutual coupling compensation: An experimental study [J]. IEEE Trans.Wireless Communications,2011,10(7):2228-2233.
[47] Liu A, Liao G, et al. An eigenstructure method for estimating DOA and sensorgain-phase errors [J]. IEEE Trans. Signal Processing,2011,59(12):5944-5956.
[48] Gardner W A. Simplification of MUSIC and ESPRIT by exploitation ofcyclostationarity [J]. Proceedings of the IEEE,1988,76(7):845-847.
[49] Xu G, T Kailath. Direction-of-arrival estimation via exploitation ofcyclostationarity—A combination of temporal and spatial processing [J]. IEEE Trans.Signal Processing,1992,40(7):1775-1786.
[50] Liu Z M, Huang Z T, Zhou Y Y. Generalized wideband cyclic MUSIC [J].EURASIP Journal on Advances in Signal Processing,2009:1-8.
[51] Abeida H, Delmas J P. MUSIC-like estimation of direction of arrival fornoncircular sources [J]. IEEE Trans. Signal Processing,2006,54(7):2678-2690.
[52] Gao F, Nallanathan A, Wang Y. Improved MUSIC under the coexistence of bothcircular and noncircular sources [J]. IEEE Trans. Signal Processing,2008,56(7):3033-3038.
[53] Stoica P, Nehorai A. MUSIC, maximum likelihood, and Cramer-Rao bound:Further results and comparisons [J]. IEEE Trans. Acoustics, Speech, and SignalProcessing,1990,38(12):2140-2150.
[54] Sarac U, Harmanci F K, Akgui T. Experimental analysis of detection andlocalization of multiple emitters in multipath environments [J]. IEEE Antennas andPropagation Magazine,2008,50(5):61-70.
[55] Steinhardt A O. Thresholds in frequency estimation [C]. IEEE ICASSP,1985:1273-1276.
[56] Tufts D W, Kot A C, Vaccaro R J. The threshold analysis of SVD-based algorithms[C]. IEEE ICASSP, Kingston, USA,1988:2416-2419.
[57] Lee H B, Wengrovitz. Theoretical resolution-threshold curve for the MUSICalgorithm [C]. IEEE ICASSP, Waltham, USA,1991:3313-3316.
[58] Zhou C G, Haber F, Jaggard D L. The resolution threshold of MUSIC withunknown spatially colored noise [J]. IEEE Trans. Signal Processing,1993,41(1):511-516.
[59] Tufts D W, Kot A C, Vaccaro R J. The threshold effect in signal processingalgorithms with use an estimated subspace [C]. SVD and Signal Processing II:Algorithms, Analysis and Applications, New York, USA,1991:301-320.
[60] Thomas J K, Scharf L L, Tufts D W. The probability of a subspace swap in theSVD [J]. IEEE Trans. Signal Processing,1995,43(3):730-736.
[61] Chang L, Yeh C C. Resolution threshold for coherent sources using smoothedeigenstructure methods [J]. IEE Proceedings-F,1991,138(5):470-478.
[62] Lee H, Li F. Quantification of the difference between detection and resolutionthresholds for multiple closely spaced emitters [J]. IEEE Trans. Signal Processing,1993,41(6):2274-2277.
[63] Smith S T. Statistical resolution limits and the complexified Cramer-Rao bound [J].IEEE Trans. Signal Processing,2005,53(5):1597-1609.
[64] Weiss A J, Friedlander B. Effects of modeling errors on the resolution threshold ofthe MUSIC algorithm [J]. IEEE Trans. Signal Processing,1994,42(6):1519-1526.
[65] Friedlander B, Weiss A J. The resolution threshold of a direction-finding algorithmfor diversely polarized arrays [J]. IEEE Trans. Signal Processing,1994,42(7):1719-1727.
[66] Johnson B A, Abramovich Y I, Mestre X. MUSIC, G-MUSIC, andmaximum-likelihood performance breakdown [J]. IEEE Trans. Signal Processing,2008,56(8):3944-3958.
[67] Gershman A B. Pseudo-randomly generated estimator banks: A new tool forimproving the threshold performance of direction finding [J]. IEEE Trans. SignalProcessing,1998,46(5):1351-1364.
[68] Gershman A B, Stoica P. New MODE-based techniques for direction finding withan improved threshold performance [J]. Signal Processing,1999,76:221-235.
[69] Mestre X, Lagunas M A. Modified subspace algorithms for DOA estimation withlarge arrays [J]. IEEE Trans. Signal Processing,2008,56(2):598-614.
[70] Spencer N K, Abramovich Y I. Performance analysis of DOA estimation usinguniform circular antenna arrays in the threshold region [C]. IEEE CSSIP, Adelaide,Australia,2004, II:233-236.
[71] Abramovich Y I, Spencer N K, Gorokhov A Y. GLRT-based thresholddetection-estimation performance improvement and application to uniform circularantenna arrays [J]. IEEE Trans. Signal Processing,2007,55(1):20-31.
[72] Stoica P, Sharman K C. Maximum likelihood methods for direction-of-arrivalestimation [J]. IEEE Trans. Acoustics, Speech, and Signal Processing,1990,38(7):1132-1143.
[73] Bohme J F. Estimation of source parameters by maximum likelihood and nonlinearregression [C]. IEEE ICASSP,1984:7.3.1-4.
[74] Jaffer A G. Maximum likelihood direction finding of stochastic sources: Aseparable solution [C]. IEEE ICASSP, Fullerton, USA,1988:2893-2896.
[75] Kenefic R J. Maximum likelihood estimation of the parameters of a plane wavetone at an equispaced linear array [J]. IEEE Trans. Acoustics, Speech, and SignalProcessing,1988,36(1):128-130.
[76] Viberg M, Ottersten B. Sensor array processing based on subspace fitting [J]. IEEETrans. Signal Processing,1991,39(5):1110-1121.
[77] Doron M A, Weiss A J, Messer H. Maximum-likelihood direction finding ofwide-band sources [J]. IEEE Trans. Signal Processing,1993,41(1):411-414.
[78] Sheinvald J, Wax M, Weiss A J. On maximum-likelihood localization of coherentsignals [J]. IEEE Trans. Signal Processing,1996,44(10):2475-2482.
[79] Chen J C, Hudson R E, Yao K. Maximum-likelihood source localization andunknown sensor location estimation for wideband signals in the near-field [J]. IEEETrans. Signal Processing,2002,50(8):1843-1854.
[80] Vorobyov S A, Gershman A B, Wong K M. Maximum likelihooddirection-of-arrival estimation in unknown noise fields using sparse sensor arrays [J].IEEE Trans. Signal Processing,2005,53(1):34-43.
[81] Chen C E, Lorenzelli F. Stochastic maximum-likelihood DOA estimation in thepresence of unknown nonuniform noise [J]. IEEE Trans. Signal Processing,2008,56(7):3038-3044.
[82] Li T, Nehorai A. Maximum likelihood direction-of-arrival estimation ofunderwater acoustic signals containing sinusoidal and random components [J]. IEEETrans. Signal Processing,2011,59(11):5302-5314.
[83] Li T, Nehorai A. Maximum likelihood direction finding in spatially colored noisefields using sparse sensor arrays [J]. IEEE Trans. Signal Processing,2011,59(3):1048-1062.
[84] Ziskind I, Wax M. Maximum likelihood localization of multiple sources byalternating projection [J]. IEEE Trans. Signal Processing,1988,36(10):1553-1560.
[85] Miller M I, Fuhrmann D R. Maximum-likelihood narrow-band direction findingand the EM algorithm [J]. IEEE Trans. Acoustics, Speech, and Signal Processing,1990,38(9):1560-1577.
[86] Oh S K, Un C K. Simple computational methods of the AP algorithm for maximumlikelihood localization of multiple radiating sources [J]. IEEE Trans. Signal Processing,1992,40(11):2848-2854.
[87] Stoica P, Gershman A B. Maximum-likelihood DOA estimation by data-supportedgrid search [J]. IEEE Signal Processing Letters,1999,6(10):273-275.
[88] Cadalli N, Arikan O. Wideband maximum likelihood direction finding and signalparameter estimation by using the tree-structured EM algorithm [J]. IEEE Trans. SignalProcessing,1999,47(1):201-206.
[89] Chung P J, Bohme J F. DOA estimation using fast EM and SAGE algorithms [J].Signal Processing,2002,82:1753-1762.
[90] Chung P J, Bohme J F. Comparative convergence analysis of EM and SAGEalgorithms in DOA estimation [J]. IEEE Trans. Signal Processing,2001,49(12):2940-2949.
[91] Wu J, Wang T, Bao Z. Fast realization of maximum likelihood angle estimationwith small adaptive uniform linear array [J]. IEEE Trans. Antennas and Propagation,2010,58(12):3951-3960.
[92] Weiss A J, Friedlander B. Array shape calibration using sources in unknownlocations—A maximum likelihood approach [J]. IEEE Trans. Acoustics, Speech, andSignal Processing,1989,37(12):1958-1966.
[93] Wax M, Kailath T. Detection of signals by information theoretic criteria [J]. IEEETrans. Acoustics, Speech, and Signal Processing,1985,33(2):387-392.
[94] Wong K M, Zhang Q T, et al. On information theoretic criteria for determining thenumber of signals in high resolution array processing [J]. IEEE Trans. Acoustics,Speech, and Signal Processing,1990,38(11):1959-1971.
[95] Zhang Q T, Wong K M. Information theoretic criteria for the determination of thenumber of signals in spatially correlated noise [J]. IEEE Trans. Signal Processing,1993,41(4):1652-1663.
[96] Liavas A P, Regalia P A. On the behavior of information theoretic criteria formodel order selection [J]. IEEE Trans. Signal Processing,2001,49(8):1689-1695.
[97] Stoica P, Selen Y. Model-order selection: A review of information criterion rules[J]. IEEE Signal Processing Magazine,2004,7:36-47.
[98] Fishler E, Poor H V. Estimation of the number of sources in unbalanced arrays viainformation theoretic criteria [J]. IEEE Trans. Signal Processing,2005,53(9):3543-3553.
[99] Nadler B. Nonparametric detection of signals by information theoretic criteria:Performance analysis and an improved estimator [J]. IEEE Trans. Signal Processing,2010,58(5):2746-2756.
[100] Zhang Q T. Asymptotic performance analysis of information-theoretic criteria fordetermining the number of signals in spatially correlated noise [J]. IEEE Trans. SignalProcessing,1994,42(6):1537-1539.
[101] Fishler E, Messer H. On the use of order statistics for improved detection ofsignals by the MDL criterion [J]. IEEE Trans. Signal Processing,2000,48(8):2242-2247.
[102] Ding Q, Kay S K. Inconsistency of the MDL: On the performance of model orderselection criteria with increasing signal-to-noise ratio [J]. IEEE Trans. Signal Processing,2011,59(5):1959-1969.
[103] Schmidt D F, Makalic E. The consistency of MDL for linear regression modelswith increasing signal-to-noise ratio [J]. IEEE Trans. Signal Processing,2012,60(3):1508-1510.
[104] Quinlan A, Barbot J P, et al. Model order selection for short data: An exponentialfitting test (EFT)[J]. EURASIP Journal on Advances in Signal Processing,2007:1-11.
[105] Dixon R C. Spread spectrum systems [M]. John Wiley&Sons,1984.
[106] Schleher D C. LPI radar: Fact or fiction [J]. IEEE Aerospace and ElectronicSystems Magazine,2006,5:3-6.
[107] Pace P E. Detecting and classifying low probability of intercept radar [M]. ArtechHouse, Boston,2009.
[108] Gao S, Sambell A, Zhong S S. Polarization-agile antennas [J]. IEEE Antennas andPropagation Magazine,2006,48(3):28-37.
[109] Garcia M B, Jara J S, et al. Radar sensor using low probability of interceptionSS-FH signals [J]. IEEE Aerospace and Electronic Systems Magazine,2000,4:23-28.
[110] Sira S P, Li Y, et al. Waveform-agile sensing for tracking [J]. IEEE SignalProcessing Magazine,2009,1:53-64.
[111] Schoolcraft R. Low probability of detection communications—LPD waveformdesign and detection techniques [C]. IEEE MILCOM, Torrance, USA,1991:35.3.1-9.
[112] Mireles F R. Performance of ultrawideband SSMA using time hopping and M-aryPPM [J]. IEEE Journal on Selected Areas in Communications,2001,19(6):1186-1196.
[113] Yang L L. Time-hopping multicarrier code-division multiple access [J]. IEEETrans. Vehicular Technology,2007,56(2):731-741.
[114] Kim B J, Cox D C. Blind equalization for short burst wireless communications [J].IEEE Trans. Vehicular Technology,2000,49(4):1235-1247.
[115] Glover I A. Meteor burst communications—I: Meteor burst propagation [J].Electronics and Communication Engineering Journal,1991,8:185-192.
[116]刘培国.电磁环境基础[M].西安:西安电子科技大学出版社,2010.
[117] Angelosante D, Giannakis G B, Sidiropoulos N D. Estimating multiplefrequency-hopping signal parameters via sparse linear regression [J]. IEEE Trans.Signal Processing,2010,58(10):5044-5056.
[118] Ender J H G. On compressive sensing applied to radar [J]. Signal Processing,2010,90:1402-1414.
[119] Gorodnitsky I F, George J S, Rao B D. Neuromagnetic source imaging withFOCUSS: A recursive weighted minimum norm algorithm [J]. Electroencephalogr.Clinical Neurophysiol.,1995,95:231-251.
[120] Lustig M, Donoho D, Pauly J M. Sparse MRI: The application of compressedsensing for rapid MR imaging [J]. Magnetic Resonance in Medicine,2007,58:1182-1195.
[121] Mallat S G, Zhang Z. Matching pursuits with time-frequency dictionaries [J].IEEE Trans. Signal Processing,1993,41(12):3397-3415.
[122] O’Brien M S, Sinclair A N, Kramer S M. Recovery of a sparse spike time seriesby L1norm deconvolution [J]. IEEE Trans. Signal Processing,1994,42(12):3353-3365.
[123] Tibshirani R. Regression shrinkage and selection via the Lasso [J]. Journal ofRoyal Statistical Society Series B,1996,58(1):267-288.
[124] Lewicki M S, Sejnowski T J. Learning overcomplete representations [J]. NeuralComputation,2000,12:337-365.
[125] Candes E J, Romberg J, Tao T. Robust uncertainty principles: Exact signalreconstruction from highly incomplete frequency information [J]. IEEE Trans.Information Theory,2006,52(2):489-509.
[126] Donoho D L. Compressed sensing [J]. IEEE Trans. Information Theory,2006,52(4):1289-1307.
[127] Compressive sensing resources [EB]. http://dsp.rice.edu/cs.
[128] Gribonval R, Bacry E. Harmonic decomposition of audio signals with matchingpursuit [J]. IEEE Trans. Signal Processing,2003,51(1):101-111.
[129] Tipping M E. Sparse Bayesian learning and the relevance vector machine [J].Journal of Machine Learning Research,2001,1:211-244.
[130] Zibulevsky M, Elad M. L1-L2optimization in signal and image processing [J].IEEE Signal Processing Magazine,2010,5:76-88.
[131] Eslami R, Jacob M. Robust reconstruction of MRSI data using a sparse spectralmodel and high resolution MRI priors [J]. IEEE Trans. Medical Imaging,2010,29(6):1297-1309.
[132] Pati Y C, Rezaiifar R, Krishnaprasad P S. Orthogonal matching pursuit: Recursivefunction approximation with applications to wavelet decomposition [C]. The27thAsilomar Conference on Signals, Systems and Computers, CA, USA1993:40-44.
[133] Cotter S F, Adler J, et al. Forward sequential algorithms for best basis selection[J]. Proc. Inst. Elect. Eng. Vision, Image, Signal Processing,1999,146(5):235-244.
[134] Natarajan B K. Sparse approximate solutions to linear systems [J]. SIAM Journalof Computation,1995,24(2):227-234.
[135] Chen S, Wigger J. Fast orthogonal least squares algorithm for efficient subsetmodel selection [J]. IEEE Trans. Signal Processing,1995,43(7):1713-1715.
[136] Donoho D L, Tsaig Y, et al. Sparse solution of underdetermined systems of linearequations by stagewise orthogonal matching pursuit [J]. IEEE Trans. InformationTheory,2012,58(2):1094-1121.
[137] Cotter S F, Rao B D, et al. Sparse solutions to linear inverse problems withmultiple measurement vectors [J]. IEEE Trans. Signal Processing,2005,53(7):2477-2488.
[138] Davies M E, Eldar Y C. Rank awareness in joint sparse recovery [J]. IEEE Trans.Information Theory,2012,58(2):1135-1146.
[139] Cotter S F, Rao B D. Sparse channel estimation via matching pursuit withapplication to equalization [J]. IEEE Trans. Communications,2002,50(3):374-377.
[140] Mileounis G, Babadi B, et al. An adaptive greedy algorithm with application tononlinear communications [J]. IEEE Trans. Signal Processing,2010,58(6):2998-3007.
[141] Tropp J A. Greed is good: Algorithmic results for sparse approximation [J]. IEEETrans. Information Theory,2004,50(10):2231-2242.
[142] Tropp J A, Gilbert A C, Strauss M J. Algorithms for simultaneous sparseapproximation. Part I: Greedy pursuit [J]. Signal Processing,2006,86:572-588.
[143] Davenport M A, Wakin M B. Analysis of orthogonal matching pursuit using therestricted isometry property [J]. IEEE Trans. Information Theory,2010,56(9):4395-4401.
[144] Zhang T. Sparse recovery with orthogonal matching pursuit under RIP [J]. IEEETrans. Information Theory,2011,57(9):6215-6221.
[145] Portilla J, Mancera L. L0-based sparse approximation: Two alternative methodsand some applications [C]. Wavelets XII, Spain,2007, Z:1-15.
[146] Mohimani H, Zadeh M B, Jutten C. A fast approach for overcomplete sparsedecomposition based on smoothed0norm [J]. IEEE Trans. Signal Processing,2009,57(1):289-301.
[147] Hyder M M, Mahata K. An improved smoothed0approximation algorithm forsparse representation [J]. IEEE Trans. Signal Processing,2010,58(4):2194-2205.
[148] Mourad N, Reilly J P. Minimizing nonconvex functions for sparse vectorreconstruction [J]. IEEE Trans. Signal Processing,2010,58(7):3485-3496.
[149] Gu Y, Jin J, Mei S.0norm constraint LMS algorithm for sparse systemidentification [J]. IEEE Signal Processing Letters,2009,16(9):774-777.
[150] Nikolova M, Ng M K, Tam C P. Fast nonconvex nonsmooth minimizationmethods for image restoration and reconstruction [J]. IEEE Trans. Image Processing,2010,19(12):3073-3088.
[151] Gorodnitsky I F, Rao B D. Sparse signal reconstruction from limited data usingFOCUSS: A re-weighted minimum norm algorithm [J]. IEEE Trans. Signal Processing,1997,45(3):600-616.
[152] Zdunek R, Cichocki A. Improved M-FOCUSS algorithm with overlapping blocksfor locally smooth sparse signals [J]. IEEE Trans. Signal Processing,2008,56(10):4752-4761.
[153] Rakotomamonjy A, Flamary R, et al. p-q penalty for sparse linear and sparsemultiple kernel multitask learning [J]. IEEE Trans. Neural Networks,2011,22(8):1307-1320.
[154] Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit[J]. SIAM Review,2001,43(1):129-159.
[155] Tropp J A. Algorithms for simultaneous sparse approximation. Part II: Convexrelaxation [J]. Signal Processing,2006,86:589-602.
[156] Tropp J A. Just relax: Convex programming methods for identifying sparsesignals in noise [J]. IEEE Trans. Signal Processing,2006,52(3):1030-1051.
[157] Stojnic M.2/1–optimization in block-sparse compressed sensing and its strongthresholds [J]. IEEE Journal of Selected Topics in Signal Processing,2010,4(2):350-357.
[158] Boyd S, Vandenberghe L. Convex optimization [M]. Cambridge University Press,New York,2009.
[159] Sturm J S. Using SeDuMi1.02, A Matlab toolbox for optimization oversymmetric cones [EB]. http://fewcal.kub.nl/~strum.
[160] Fuchs J J. Multipath time-delay detection and estimation [J]. IEEE Trans. SignalProcessing,1999,47(1):237-243.
[161] Fuchs J J, Delyon B. Minimum L1-norm reconstruction function for oversampledsignals: Application to time-delay estimation [J]. IEEE Trans. Information Theory,2000,46(7):1666-1673.
[162] Fuchs J J. On the application of the global matched filter to DOA estimation withuniform circular arrays [J]. IEEE Trans. Signal Processing,2001,49(4):702-709.
[163] Model D, Zibulevsky M. Signal reconstruction in sensor arrays using sparserepresentations [J]. Signal Processing,2006,86:624-638.
[164] Gershman A B, Sidiropoulos N D, et al. Convex optimization-based beamforming:From receive to transmit and network designs [J]. IEEE Signal Processing Magazine,2010,5:62-75.
[165] Kopsinis Y, Slavakis K, Theodoridis S. Online sparse system identification andsignal reconstruction using projections onto weighted1balls [J]. IEEE Trans. SignalProcessing,2011,59(3):936-952.
[166] Donoho D L, Huo X. Uncertainty principles and ideal atomic decomposition [J].IEEE Trans. Information Theory,2001,47(7):2845-2862.
[167] Fuchs J J. On sparse representations in arbitrary redundant bases [J]. IEEE Trans.Information Theory,2004,50(6):1341-1344.
[168] Candes E J, Tao T. Decoding by linear programming [J]. IEEE Trans. InformationTheory,2005,51(12):4203-4215.
[169] Chen J, Huo X. Theoretical results on sparse representations ofmultiple-measurement vectors [J]. IEEE Trans. Signal Processing,2006,54(12):4634-4643.
[170] Donoho D L, Elad M, Temlyakov V N. Stable recovery of sparse overcompleterepresentations in the presence of noise [J]. IEEE Trans. Information Theory,2006,52(1):6-18.
[171] Davies M E, Gribonval R. Restricted isometry constants where p sparse recoverycan fail for0 [172] Wang M, Xu W, Tang A. On the performance of sparse recovery viap-minimization (0 [173] Zadeh M B, Jutten C. On the stable recovery of the sparsest overcompleterepresentations in presence of noise [J]. IEEE Trans. Signal Processing,2010,58(10):5396-5400.
[174] Maleki A, Donoho D L. Optimally tuned iterative reconstruction algorithms forcompressed sensing [J]. IEEE Journal of Selected Topics in Signal Processing,2010,4(2):330-341.
[175] Tipping M E. The relevance vector machine [J]. Advances in Neural InformationProcessing Systems,2000,12:652-658.
[176] Tipping M E. Bayesian inference: An introduction to principles and practice inmachine learning [J]. Advanced Lectures on Machine Learning,2004:41-62.
[177] Wipf D P, Rao B D. Sparse Bayesian learning for basis selection [J]. IEEE Trans.Signal Processing,2004,52(8):2153-2164.
[178] Wipf D P, Rao B D. An empirical Bayesian strategy for solving the simultaneoussparse approximation problem [J]. IEEE Trans. Signal Processing,2007,55(7):3704-3716.
[179] Wipf D P, Rao B D, Nagarajan S. Latent variable Bayesian models for promotingsparsity [J]. IEEE Trans. Information Theory,2011,57(9),6236-6255.
[180] Kim J M, Lee O K, Ye J C. Compressive MUSIC: Revisiting the link betweencompressive sensing and array signals processing [J]. IEEE Trans. Information Theory,2012,58(1):278-300.
[181] Williams O, Blake A, Cipolla R. Sparse Bayesian learning for efficient visualtracking [J]. IEEE Trans. Pattern Analysis and Machine Intelligence,2005,27(8):1292-1304.
[182] Qiu K, Dogandzic A. Variance-component based sparse signal reconstruction andmodel selection [J]. IEEE Trans. Signal Processing,2010,58(6):2935-2952.
[183] Wu J, Liu F, et al. Compressive sensing SAR image reconstruction based onBayesian framework and evolutionary computation [J]. IEEE Trans. Image Processing,2011,20(7):1904-1911.
[184] Babacan S D, Molina R, Katsaggelos A K. Variational Bayesian super resolution[J]. IEEE Trans. Image Processing,2011,20(4):984-999.
[185] Zhou M, Chen H, et al. Nonparametric Bayesian dictionary learning for analysisof noisy and incomplete images [J]. IEEE Trans. Image Processing,2012,21(1):130-144.
[186] Wipf D, Nagarajan S. A unified Bayesian framework for MEG/EEG sourceimaging [J]. NeuroImage,2009,44:947-966.
[187] Wipf D P, Owen J P, et al. Robust Bayesian estimation of the location, orientation,and time course of multiple correlated neural sources using MEG [J]. NeuroImage,2010,49:641-655.
[188] Zhang Z, Rao B D. Sparse signal recovery with temporally correlated sourcevectors using sparse Bayesian learning [J]. IEEE Journal of Selected Topics in SignalProcessing,2011,5(5):912-926.
[189] Chen S, Gunn S R, Harris C J. The relevance vector machine technique forchannel equalization application [J]. IEEE Trans. Neural Networks,2001,12(6):1529-1532.
[190] Hwang K, Choi S. Blind equalization method based on sparse Bayesian learning[J]. IEEE Signal Processing Letters,2009,16(4):315-318.
[191] Tan X, Li J. Range-doppler imaging via forward-backward sparse Bayesianlearning [J]. IEEE Trans. Signal Processing,2010,58(4):2421-2425.
[192] Austin C D, Moses R L, et al. On the relation between sparse reconstruction andparameter estimation with model order selection [J]. IEEE Journal of Selected Topics inSignal Processing,2010,4(3):560-570.
[193] Li J, Sadler M, Viberg M. Sensor array and multichannel signal processing [J].IEEE Signal Processing Magazine,2011,9:157-158.
[194] Karabulut G Z, Kurt T, Yongacoglu A. Estimation of directions of arrival bymatching pursuit (EDAMP)[J]. EURASIP Journal on Wireless Communications andNetworking,2005,2:197-205.
[195] Cotter S F. Multiple snapshot matching pursuit for direction of arrival (DOA)estimation [C].15th European Signal Processing Conference, Poznan, Poland,2007:247-251.
[196] Malioutov D, Cetin M, Willsky A S. A sparse signal reconstruction perspectivefor source localization with sensor arrays [J]. IEEE Trans. Signal Processing,2005,53(8):3010-3022.
[197] Hyder M M, Mahata K. Direction-of-arrival estimation using a mixed2,0normapproximation [J]. IEEE Trans. Signal Processing,2010,58(9):4646-4655.
[198] Sun K, Liu Y, et al. Adaptive sparse representation for source localization withgain/phase errors [J]. Sensors,2011,11:4780-4793.
[199] Zheng C, Li G, et al. An approach of DOA estimation using noise subspaceweighted1minimization [C]. IEEE ICASSP,2011:2856-2859.
[200] Yin J, Chen T. Direction-of-arrival estimation using a sparse representation ofarray covariance vectors [J]. IEEE Trans. Signal Processing,2011,59(9):4489-4493.
[201] Panahi A, Viberg M. Fast LASSO based DOA tracking [C].4th IEEEInternational Workshop on Computational Advances in Multi-Sensor AdaptiveProcessing,2011:397-400.
[202] Blunt S D, Chan T, Gerlach K. Robust DOA estimation: The reiterativesuperresolution (RISR) algorithm [J]. IEEE Trans. Aerospace and Electronic Systems,2011,47(1):332-346.
[203] Bilik I. Spatial compressive sensing for direction-of-arrival estimation of multiplesources using dynamic sensor arrays [J]. IEEE Trans. Aerospace and ElectronicSystems,2011,47(3):1754-1769.
[204] Xu X, Wei X, Ye Z. DOA estimation based on sparse signal recovery utilizingweighted1-norm penalty [J]. IEEE Signal Processing Letters,2012,19(3):155-158.
[205] Wipf D and Nagarajan S. Beamforming using the relevance vector machine [C].Proc. International Conference on Machine Learning, Corvallis, OR,2007:1-8.
[206] Rakotomamonjy A. Surveying and comparing simultaneous sparse approximation(or group-lasso) algorithms [J]. Signal Processing,2011,91:1505-1526.
[207] Yin W, Osher S, et al. Bregman iterative algorithms for1-minimization withapplications to compressed sensing [J]. SIAM Journal of Imaging Sciences,2008,1(1):143-168.
[208] Wipf D and Nagarajan S. A new view of automatic relevance determination [J].Advances in Neural Information Processing Systems,2008,8pages.
[209] Dempster A P, Laird N M, Rubin D B. Maximum likelihood from incompletedata via the EM algorithm [J]. Journal of Royal Statistical Society Series B,1977,39:1-38.
[210] Wipf D P. Bayesian methods for finding sparse representations [D]. San Diego,USA, University of California,2006.
[211] Brillinger D R. Time series: Data analysis and theory [M]. SIAM press,Philadelphia,2001.
[212]张贤达.矩阵分析与应用[M].北京:清华大学出版社,2004.
[213] Moussaoui S, Brie D, et al. Separation of non-negative mixture of non-negativesources using a Bayesian approach and MCMC sampling [J]. IEEE Trans. SignalProcessing,2006,54(11):4133-4145.
[214] Moffet A T. Minimum-redundancy linear arrays [J]. IEEE Trans. Antennas andPropagation,1968,16(2):172-175.
[215] Donoho D L, Tanner J. Sparse nonnegative solution of underdetermined linearequations by linear programming [J]. Proceedings of the National Academy of Sciences,2005,102(27):9446-9451.
[216] Manabe T, TaKai H. Superresolution of multipath delay profiles measured by PNcorrelation method [J]. IEEE Trans. Antennas and Propagation,1992,40(5):500-509.
[217] Bouchereau F, Brady D, Lanzl C. Multipath delay estimation using asuperresolution PN-correlation method [J]. IEEE Trans. Signal Processing,2001,49(5):938-949.
[218] Vanderveen M C, Veen A, Paulraj A. Estimation of multipath parameters inwireless communications [J]. IEEE Trans. Signal Processing,1998,46(3):682-690.
[219] Wang Z, Huang J, Zhou S. Application of compressive sensing to sparse channelestimation [J]. IEEE Communications Magazine,2010(11):164-174.
[220] Svantesson T. Modeling and estimation of mutual coupling in a uniform lineararray of dipoles [R]. Sweden, Dept. Signals and Systems, Chalmers Univ. of Tech.,Goteborg,1999, Tech. Rep. S-41296.
[221] Liu Z M, Huang Z T, Zhou Y Y. Bias analysis of MUSIC in the presence ofmutual coupling [J]. IET Signal Processing,2009,3(1):74-84.
[222] Glegg S. A passive sonar system based on an autonomous underwater vehicle [J].IEEE Journal of Oceanic Engineering,2001,26(4):700-710.
[223] Edelson G S, Tufts D W. On the ability to estimate narrow-band signal parametersusing towed arrays [J]. IEEE Journal of Oceanic Engineering,1992,17(1):48-61.
[224] Zeira A, Friedlander B. Direction finding with time-varying arrays [J]. IEEETrans. Signal Processing,1995,43(4):927-937.
[225] Friedlander B, Zeira A. Eigenstructure-based algorithms for direction finding withtime-varying arrays [J]. IEEE Trans. Aerospace and Electronic Systems,1996,32(2):689-701.
[226] Ng B C, Ser W. Array shape calibration using sources in known locations [C].IEEE ICCS, Singapore,1992:836-840.
[227] Adve R S， Sarkar T K. Compensation for the effects of mutual coupling ondirect data domain adaptive algorithms [J]. IEEE Trans. Antennas and Propagation,2000,48(1):86-94.
[228] Hui H T, Low H P, et al. Receiving mutual impedance between two normal modehelical antennas (NMHAs)[J]. IEEE Antenna and Propagation Magazine,2006,48(4):92-96.
[229] Ye Z, Liu C. On the resiliency of MUSIC direction finding against antenna sensorcoupling [J]. IEEE Trans. Antennas and Propagation,2008,56(2):371-380.
[230] Weiss A J, Friedlander B. Eigenstructure methods for direction finding withsensor gain and phase uncertainties [J]. Circuits Systems Signal Processing,1990,9(3):271-300.
[231] Li Y, Er M H. Theoretical analyses of gain and phase error calibration withoptimal implementation for linear equispaced array [J]. IEEE Trans. Signal Processing,2006,54(2):712-723.
[232] Chen Y M, Lee J H, Yeh C C, Mar J. Bearing estimation without calibration forrandomly perturbed arrays [J]. IEEE Trans. Signal Processing,1991,39(1):194-197.
[233] Takahashi T, Nakamoto N, et al. On-board calibration methods for mechanicaldistortions of satellite phased array antennas [J]. IEEE Trans. Antennas and Propagation,2012,60(3):1362-1372.
[234] See C M S. Method for array calibration in high-resolution sensor arrayprocessing [J]. IEE Proc. Radar, Sonar, Navig.,1995,142(3):90-96.
[235] Ng B C, See C M S. Sensor-array calibration using a maximum-likelihoodapproach [J]. IEEE Trans. Antennas and Propagation,1996,44(6):827-835.
[236] Svantesson T. Modeling and estimation of mutual coupling in a uniform lineararray of dipoles [R]. Dept. Signals and Systems, Chalmers Univ. of Technology,Goteborg, Sweden, Tech. Rep. S-41296,1999.
[237] Friedlander B, Weiss A J. Performance of direction-finding systems with sensorgain and phase uncertainties [J]. Circuits Systems Signal Processing,1993,12(1):3-35.
[238] Wahlberg B, Ottersten B, Viberg M. Robust signal parameter estimation in thepresence of array perturbations [C]. Sweden, Apr.1991, ICASSP-1991:3277-3280.
[239] Figueiredo M A T. Adaptive sparseness for supervised learning [J]. IEEE Trans.Pattern Analysis and Machine Intelligence,2003,25(9):1150-1159.
[240] Tzikas D G, Likas A C, Galatsanos N P. The variational approximation forBayesian inference [J]. IEEE Signal Processing Magazine,2008,6:131-146.
[241] Wu C. On the convergence properties of the EM algorithm [J]. Annual Statistics,1983,11(1):95-103.
[242] Boyles R A. On the convergence of the EM algorithm [J]. Journal of RoyalStatistics Series B,1983,45(1):47-50.
[243] Ji S, Dunson D, Carin L. Multitask compressive sensing [J]. IEEE Trans. SignalProcessing,2009,57(1):92-106.
[244] Baron D, Wakin M B, et al. Distributed compressed sensing [EB]. Nov.2005.
[245] Moon T K. The expectation-maximization algorithm [J]. IEEE Signal ProcessingMagazine,1996,6:47-60.
[246] Shutin D, Buchgraber T, et al. Fast variational sparse Bayesian learning withautomatic relevance determination for superimposed signals [J]. IEEE Trans. SignalProcessing,2011,59(12):6257-6261.
[2]王永良,陈辉,等.空间谱估计理论与算法[M].北京:清华大学出版社,2004.
[3] Krim H, Viberg M. Two decades of array signal processing research: The parametricapproach [J]. IEEE Signal Processing Magazine,1996(7):67-94.
[4] Capon J. High-resolution frequency-wavenumber spectrum analysis [J]. Proceedingsof the IEEE,1969,57(8):1408-1418.
[5] Applebaum S P. Adaptive arrays [J]. IEEE Trans. Antennas and Propagation,1976,24(9):585-598.
[6] Gabriel W F. Spectral analysis and adaptive array superresolution techniques [J].Proceedings of the IEEE,1980,68(6):654-666.
[7] Schmidt R O. Multiple emitter location and signal parameter estimation [J]. IEEETrans. Antennas and Propagation,1986,34(2):276-280.
[8] Roy R, Kailath T. ESPRIT-Estimation of signal parameters via rotational invariancetechniques [J]. IEEE Trans. Acoustics, Speech and Signal Processing,1989,37(7):984-995.
[9] Barabell A J. Improving the resolution performance of eigenstructure-baseddirection-finding algorithms [C]. Proceedings of ICASSP, Boston, USA,1983:336-339.
[10] Kumaresan R, Tufts D W. Estimating the angles of arrival of multiple plane waves[J]. IEEE Trans. Aerospace and Electronic Systems,1983,19(1):134-139.
[11] Lee H B, Wengrovitz M S. Resolution threshold of beamspace MUSIC for twoclosely spaced emitters [J]. IEEE Trans. Acoustics, Speech, and Signal Processing,1990,38(9):1545-1559.
[12] Xu G, Silverstein S D, et al. Beamspace ESPRIT [J]. IEEE Trans. SignalProcessing,1994,42(2):349-356.
[13] Stoica P, Nehorai A. MUSIC, maximum likelihood, and Cramer-Rao bound [J].IEEE Trans. Acoustics, Speech, and Signal Processing,1989,37(5):720-741.
[14] Shan T J, Kailath T. On spatial smoothing for direction-of-arrival estimation ofcoherent signals [J]. IEEE Trans. Acoustics, Speech and Signal Processing,1985,33(4):806-811.
[15] Pillai S U, Kwon B H. Forward/Backward spatial smoothing techniques forcoherent signal identification [J]. IEEE Trans. Acoustics, Speech and Signal Processing,1989,37(1):8-15.
[16] Moghaddamjoo A. Application of spatial filters to DOA estimation of coherentsources [J]. IEEE Trans. Signal Processing,1991,39(1):221-224.
[17] Delis A, Papadopoulos G. Enhanced forward/backward spatial filtering method forDOA estimation of narrowband coherent sources [J]. IEE Proc.–Radar, SonarNavigation,1996,143(1):10-16.
[18] Xin J, Sano A. Direction estimation of coherent signals using spatial signature [J].IEEE Signal Processing Letters,2002,9(12):414-417.
[19] Han F M, Zhang X D. An ESPRIT-like algorithm for coherent DOA estimation [J].IEEE Antennas and Wireless Propagation Letters,2005,4:443-446.
[20] Choi Y H. ESPRIT-based coherent source localization with forward and backwardvectors [J]. IEEE Trans. Signal Processing,2010,58(12):6416-6420.
[21] Uttam S, Goodman N A. Superresolution of coherent sources in real-beam data [J].IEEE Trans. Aerospace and Electronic Systems,2010,46(3):1557-1566.
[22] Yeh C C, Lee J H, Chen Y M. Estimating two-dimensional angles of arrival incoherent source environment [J]. IEEE Trans. Acoustics, Speech, and Signal Processing,1989,37(1):153-155.
[23] Hua Y. A pencil-MUSIC algorithm for finding two-dimensional angles andpolarizations using crossed dipoles [J]. IEEE Trans. Antennas and Propagation,1993,41(3):370-376.
[24] Chen Y M. On spatial smoothing for two-dimensional direction-of-arrivalestimation of coherent signals [J]. IEEE Trans. Signal Processing,1997,45(7):1689-1696.
[25] Chen F J, Fung C C. Estimation of two-dimensional frequencies using modifiedmatrix pencil method [J]. IEEE Trans. Signal Processing,2007,55(2):718-724.
[26] Chen F J, Kwong S, Kok C W. ESPRIT-like two-dimensional DOA estimation forcoherent signals [J]. IEEE Trans. Aerospace and Electronic Systems,2010,46(3):1477-1484.
[27] Wang G, Xin J, et al. Computationally efficient subspace-based method fortwo-dimensional direction estimation with L-shaped array [J]. IEEE Trans. SignalProcessing,2011,59(7):3197-3212.
[28] Liu K J R, O’Leary D P, et al. URV ESPRIT for tracking time-varying signals [J].IEEE Trans. Signal Processing,1994,42(12):3441-3448.
[29] Yang B. Projection approximation subspace tracking [J]. IEEE Trans. SignalProcessing,1995,43(1):95-107.
[30] Xin J, Sano A. Efficient subspace-based algorithm for adaptive bearing estimationand tracking [J]. IEEE Trans. Signal Processing,2005,53(12):4485-4505.
[31] Su G, Morf M. The signal subspace approach for multiple wide-band emitterlocation [J]. IEEE Trans. Acoustics, Speech, and Signal Processing,1983,31(6):1502-1522.
[32] Wang H, Kaveh M. Coherent signal-subspace processing for the detection andestimation of angles of arrival of multiple wide-band sources [J]. IEEE Trans. Acoustics,Speech, and Signal Processing,1985,33(4):823-831.
[33] Valaee S, Kabal P. Wideband array processing using a two-sided correlationtransformation [J]. IEEE Trans. Signal Processing,1995,43(1):160-172.
[34] Claudio E D D, Parisi R. WAVES: Weighted average of signal subspaces forrobust wideband direction finding [J]. IEEE Trans. Signal Processing,2001,49(10):2179-2191.
[35] Yoon Y S, Kaplan L M, McClellan J H. TOPS: New DOA estimator for widebandsignals [J]. IEEE Trans. Signal Processing,2006,54(6):1977-1989.
[36] Wu Q, K M Wong. UN-MUSIC and UN-CLE: An application of generalizedcorrelation analysis to the estimation of the direction of arrival of signals in unknowncorrelated noise [J]. IEEE Trans. Signal Processing,1994,42(9):2331-2343.
[37] Belouchrani A, Amin M G, Meraim K A. Direction finding in correlated noisefields based on joint block-diagonalization of spatio-temporal correlation matrices [J].IEEE Signal Processing Letters,1997,4(9):266-268.
[38] Wu Y, Hou C, et al. Direction-of-arrival estimation in the presence of unknownnonuniform noise fields [J]. IEEE Journal of Oceanic Engineering,2006,31(2):504-510.
[39] Friedlander B, Weiss A J. Direction finding in the presence of mutual coupling [J].IEEE Trans. Antennas and Propagation,1991,39(3):273-284.
[40] Flanagan B P, Bell K L. Array self-calibration with large sensor position errors [J].Signal Processing,2001,81:2201-2214.
[41] Belloni F, Koivunen V. Beamspace transform for UCA: Error analysis and biasreduction [J]. IEEE Trans. Signal Processing,2006,54(8):3078-3089.
[42] Sellone F. A novel online mutual coupling compensation algorithm for uniformand linear arrays [J]. IEEE Trans. Signal Processing,2007,55(2):560-573.
[43] Liu Z, Huang Z, et al. DOA estimation with uniform linear arrays in the presenceof mutual coupling via blind calibration [J]. Signal Processing,2009,89:1446-1456.
[44] Ye Z, Dai J, et al. DOA estimation for uniform linear array with mutual coupling[J]. IEEE Trans. Aerospace and Electronic Systems,2009,45(1):280-288.
[45] Ferreol A, Larzabal P, Viberg M. Statistical analysis of the MUSIC algorithm inthe presence of modeling errors, Taking into account the resolution probability [J].IEEE Trans. Signal Processing,2010,58(8):4156-4166.
[46] Wu Y, Lui H S. Improved DOA estimations using the receiving mutualimpedances for mutual coupling compensation: An experimental study [J]. IEEE Trans.Wireless Communications,2011,10(7):2228-2233.
[47] Liu A, Liao G, et al. An eigenstructure method for estimating DOA and sensorgain-phase errors [J]. IEEE Trans. Signal Processing,2011,59(12):5944-5956.
[48] Gardner W A. Simplification of MUSIC and ESPRIT by exploitation ofcyclostationarity [J]. Proceedings of the IEEE,1988,76(7):845-847.
[49] Xu G, T Kailath. Direction-of-arrival estimation via exploitation ofcyclostationarity—A combination of temporal and spatial processing [J]. IEEE Trans.Signal Processing,1992,40(7):1775-1786.
[50] Liu Z M, Huang Z T, Zhou Y Y. Generalized wideband cyclic MUSIC [J].EURASIP Journal on Advances in Signal Processing,2009:1-8.
[51] Abeida H, Delmas J P. MUSIC-like estimation of direction of arrival fornoncircular sources [J]. IEEE Trans. Signal Processing,2006,54(7):2678-2690.
[52] Gao F, Nallanathan A, Wang Y. Improved MUSIC under the coexistence of bothcircular and noncircular sources [J]. IEEE Trans. Signal Processing,2008,56(7):3033-3038.
[53] Stoica P, Nehorai A. MUSIC, maximum likelihood, and Cramer-Rao bound:Further results and comparisons [J]. IEEE Trans. Acoustics, Speech, and SignalProcessing,1990,38(12):2140-2150.
[54] Sarac U, Harmanci F K, Akgui T. Experimental analysis of detection andlocalization of multiple emitters in multipath environments [J]. IEEE Antennas andPropagation Magazine,2008,50(5):61-70.
[55] Steinhardt A O. Thresholds in frequency estimation [C]. IEEE ICASSP,1985:1273-1276.
[56] Tufts D W, Kot A C, Vaccaro R J. The threshold analysis of SVD-based algorithms[C]. IEEE ICASSP, Kingston, USA,1988:2416-2419.
[57] Lee H B, Wengrovitz. Theoretical resolution-threshold curve for the MUSICalgorithm [C]. IEEE ICASSP, Waltham, USA,1991:3313-3316.
[58] Zhou C G, Haber F, Jaggard D L. The resolution threshold of MUSIC withunknown spatially colored noise [J]. IEEE Trans. Signal Processing,1993,41(1):511-516.
[59] Tufts D W, Kot A C, Vaccaro R J. The threshold effect in signal processingalgorithms with use an estimated subspace [C]. SVD and Signal Processing II:Algorithms, Analysis and Applications, New York, USA,1991:301-320.
[60] Thomas J K, Scharf L L, Tufts D W. The probability of a subspace swap in theSVD [J]. IEEE Trans. Signal Processing,1995,43(3):730-736.
[61] Chang L, Yeh C C. Resolution threshold for coherent sources using smoothedeigenstructure methods [J]. IEE Proceedings-F,1991,138(5):470-478.
[62] Lee H, Li F. Quantification of the difference between detection and resolutionthresholds for multiple closely spaced emitters [J]. IEEE Trans. Signal Processing,1993,41(6):2274-2277.
[63] Smith S T. Statistical resolution limits and the complexified Cramer-Rao bound [J].IEEE Trans. Signal Processing,2005,53(5):1597-1609.
[64] Weiss A J, Friedlander B. Effects of modeling errors on the resolution threshold ofthe MUSIC algorithm [J]. IEEE Trans. Signal Processing,1994,42(6):1519-1526.
[65] Friedlander B, Weiss A J. The resolution threshold of a direction-finding algorithmfor diversely polarized arrays [J]. IEEE Trans. Signal Processing,1994,42(7):1719-1727.
[66] Johnson B A, Abramovich Y I, Mestre X. MUSIC, G-MUSIC, andmaximum-likelihood performance breakdown [J]. IEEE Trans. Signal Processing,2008,56(8):3944-3958.
[67] Gershman A B. Pseudo-randomly generated estimator banks: A new tool forimproving the threshold performance of direction finding [J]. IEEE Trans. SignalProcessing,1998,46(5):1351-1364.
[68] Gershman A B, Stoica P. New MODE-based techniques for direction finding withan improved threshold performance [J]. Signal Processing,1999,76:221-235.
[69] Mestre X, Lagunas M A. Modified subspace algorithms for DOA estimation withlarge arrays [J]. IEEE Trans. Signal Processing,2008,56(2):598-614.
[70] Spencer N K, Abramovich Y I. Performance analysis of DOA estimation usinguniform circular antenna arrays in the threshold region [C]. IEEE CSSIP, Adelaide,Australia,2004, II:233-236.
[71] Abramovich Y I, Spencer N K, Gorokhov A Y. GLRT-based thresholddetection-estimation performance improvement and application to uniform circularantenna arrays [J]. IEEE Trans. Signal Processing,2007,55(1):20-31.
[72] Stoica P, Sharman K C. Maximum likelihood methods for direction-of-arrivalestimation [J]. IEEE Trans. Acoustics, Speech, and Signal Processing,1990,38(7):1132-1143.
[73] Bohme J F. Estimation of source parameters by maximum likelihood and nonlinearregression [C]. IEEE ICASSP,1984:7.3.1-4.
[74] Jaffer A G. Maximum likelihood direction finding of stochastic sources: Aseparable solution [C]. IEEE ICASSP, Fullerton, USA,1988:2893-2896.
[75] Kenefic R J. Maximum likelihood estimation of the parameters of a plane wavetone at an equispaced linear array [J]. IEEE Trans. Acoustics, Speech, and SignalProcessing,1988,36(1):128-130.
[76] Viberg M, Ottersten B. Sensor array processing based on subspace fitting [J]. IEEETrans. Signal Processing,1991,39(5):1110-1121.
[77] Doron M A, Weiss A J, Messer H. Maximum-likelihood direction finding ofwide-band sources [J]. IEEE Trans. Signal Processing,1993,41(1):411-414.
[78] Sheinvald J, Wax M, Weiss A J. On maximum-likelihood localization of coherentsignals [J]. IEEE Trans. Signal Processing,1996,44(10):2475-2482.
[79] Chen J C, Hudson R E, Yao K. Maximum-likelihood source localization andunknown sensor location estimation for wideband signals in the near-field [J]. IEEETrans. Signal Processing,2002,50(8):1843-1854.
[80] Vorobyov S A, Gershman A B, Wong K M. Maximum likelihooddirection-of-arrival estimation in unknown noise fields using sparse sensor arrays [J].IEEE Trans. Signal Processing,2005,53(1):34-43.
[81] Chen C E, Lorenzelli F. Stochastic maximum-likelihood DOA estimation in thepresence of unknown nonuniform noise [J]. IEEE Trans. Signal Processing,2008,56(7):3038-3044.
[82] Li T, Nehorai A. Maximum likelihood direction-of-arrival estimation ofunderwater acoustic signals containing sinusoidal and random components [J]. IEEETrans. Signal Processing,2011,59(11):5302-5314.
[83] Li T, Nehorai A. Maximum likelihood direction finding in spatially colored noisefields using sparse sensor arrays [J]. IEEE Trans. Signal Processing,2011,59(3):1048-1062.
[84] Ziskind I, Wax M. Maximum likelihood localization of multiple sources byalternating projection [J]. IEEE Trans. Signal Processing,1988,36(10):1553-1560.
[85] Miller M I, Fuhrmann D R. Maximum-likelihood narrow-band direction findingand the EM algorithm [J]. IEEE Trans. Acoustics, Speech, and Signal Processing,1990,38(9):1560-1577.
[86] Oh S K, Un C K. Simple computational methods of the AP algorithm for maximumlikelihood localization of multiple radiating sources [J]. IEEE Trans. Signal Processing,1992,40(11):2848-2854.
[87] Stoica P, Gershman A B. Maximum-likelihood DOA estimation by data-supportedgrid search [J]. IEEE Signal Processing Letters,1999,6(10):273-275.
[88] Cadalli N, Arikan O. Wideband maximum likelihood direction finding and signalparameter estimation by using the tree-structured EM algorithm [J]. IEEE Trans. SignalProcessing,1999,47(1):201-206.
[89] Chung P J, Bohme J F. DOA estimation using fast EM and SAGE algorithms [J].Signal Processing,2002,82:1753-1762.
[90] Chung P J, Bohme J F. Comparative convergence analysis of EM and SAGEalgorithms in DOA estimation [J]. IEEE Trans. Signal Processing,2001,49(12):2940-2949.
[91] Wu J, Wang T, Bao Z. Fast realization of maximum likelihood angle estimationwith small adaptive uniform linear array [J]. IEEE Trans. Antennas and Propagation,2010,58(12):3951-3960.
[92] Weiss A J, Friedlander B. Array shape calibration using sources in unknownlocations—A maximum likelihood approach [J]. IEEE Trans. Acoustics, Speech, andSignal Processing,1989,37(12):1958-1966.
[93] Wax M, Kailath T. Detection of signals by information theoretic criteria [J]. IEEETrans. Acoustics, Speech, and Signal Processing,1985,33(2):387-392.
[94] Wong K M, Zhang Q T, et al. On information theoretic criteria for determining thenumber of signals in high resolution array processing [J]. IEEE Trans. Acoustics,Speech, and Signal Processing,1990,38(11):1959-1971.
[95] Zhang Q T, Wong K M. Information theoretic criteria for the determination of thenumber of signals in spatially correlated noise [J]. IEEE Trans. Signal Processing,1993,41(4):1652-1663.
[96] Liavas A P, Regalia P A. On the behavior of information theoretic criteria formodel order selection [J]. IEEE Trans. Signal Processing,2001,49(8):1689-1695.
[97] Stoica P, Selen Y. Model-order selection: A review of information criterion rules[J]. IEEE Signal Processing Magazine,2004,7:36-47.
[98] Fishler E, Poor H V. Estimation of the number of sources in unbalanced arrays viainformation theoretic criteria [J]. IEEE Trans. Signal Processing,2005,53(9):3543-3553.
[99] Nadler B. Nonparametric detection of signals by information theoretic criteria:Performance analysis and an improved estimator [J]. IEEE Trans. Signal Processing,2010,58(5):2746-2756.
[100] Zhang Q T. Asymptotic performance analysis of information-theoretic criteria fordetermining the number of signals in spatially correlated noise [J]. IEEE Trans. SignalProcessing,1994,42(6):1537-1539.
[101] Fishler E, Messer H. On the use of order statistics for improved detection ofsignals by the MDL criterion [J]. IEEE Trans. Signal Processing,2000,48(8):2242-2247.
[102] Ding Q, Kay S K. Inconsistency of the MDL: On the performance of model orderselection criteria with increasing signal-to-noise ratio [J]. IEEE Trans. Signal Processing,2011,59(5):1959-1969.
[103] Schmidt D F, Makalic E. The consistency of MDL for linear regression modelswith increasing signal-to-noise ratio [J]. IEEE Trans. Signal Processing,2012,60(3):1508-1510.
[104] Quinlan A, Barbot J P, et al. Model order selection for short data: An exponentialfitting test (EFT)[J]. EURASIP Journal on Advances in Signal Processing,2007:1-11.
[105] Dixon R C. Spread spectrum systems [M]. John Wiley&Sons,1984.
[106] Schleher D C. LPI radar: Fact or fiction [J]. IEEE Aerospace and ElectronicSystems Magazine,2006,5:3-6.
[107] Pace P E. Detecting and classifying low probability of intercept radar [M]. ArtechHouse, Boston,2009.
[108] Gao S, Sambell A, Zhong S S. Polarization-agile antennas [J]. IEEE Antennas andPropagation Magazine,2006,48(3):28-37.
[109] Garcia M B, Jara J S, et al. Radar sensor using low probability of interceptionSS-FH signals [J]. IEEE Aerospace and Electronic Systems Magazine,2000,4:23-28.
[110] Sira S P, Li Y, et al. Waveform-agile sensing for tracking [J]. IEEE SignalProcessing Magazine,2009,1:53-64.
[111] Schoolcraft R. Low probability of detection communications—LPD waveformdesign and detection techniques [C]. IEEE MILCOM, Torrance, USA,1991:35.3.1-9.
[112] Mireles F R. Performance of ultrawideband SSMA using time hopping and M-aryPPM [J]. IEEE Journal on Selected Areas in Communications,2001,19(6):1186-1196.
[113] Yang L L. Time-hopping multicarrier code-division multiple access [J]. IEEETrans. Vehicular Technology,2007,56(2):731-741.
[114] Kim B J, Cox D C. Blind equalization for short burst wireless communications [J].IEEE Trans. Vehicular Technology,2000,49(4):1235-1247.
[115] Glover I A. Meteor burst communications—I: Meteor burst propagation [J].Electronics and Communication Engineering Journal,1991,8:185-192.
[116]刘培国.电磁环境基础[M].西安:西安电子科技大学出版社,2010.
[117] Angelosante D, Giannakis G B, Sidiropoulos N D. Estimating multiplefrequency-hopping signal parameters via sparse linear regression [J]. IEEE Trans.Signal Processing,2010,58(10):5044-5056.
[118] Ender J H G. On compressive sensing applied to radar [J]. Signal Processing,2010,90:1402-1414.
[119] Gorodnitsky I F, George J S, Rao B D. Neuromagnetic source imaging withFOCUSS: A recursive weighted minimum norm algorithm [J]. Electroencephalogr.Clinical Neurophysiol.,1995,95:231-251.
[120] Lustig M, Donoho D, Pauly J M. Sparse MRI: The application of compressedsensing for rapid MR imaging [J]. Magnetic Resonance in Medicine,2007,58:1182-1195.
[121] Mallat S G, Zhang Z. Matching pursuits with time-frequency dictionaries [J].IEEE Trans. Signal Processing,1993,41(12):3397-3415.
[122] O’Brien M S, Sinclair A N, Kramer S M. Recovery of a sparse spike time seriesby L1norm deconvolution [J]. IEEE Trans. Signal Processing,1994,42(12):3353-3365.
[123] Tibshirani R. Regression shrinkage and selection via the Lasso [J]. Journal ofRoyal Statistical Society Series B,1996,58(1):267-288.
[124] Lewicki M S, Sejnowski T J. Learning overcomplete representations [J]. NeuralComputation,2000,12:337-365.
[125] Candes E J, Romberg J, Tao T. Robust uncertainty principles: Exact signalreconstruction from highly incomplete frequency information [J]. IEEE Trans.Information Theory,2006,52(2):489-509.
[126] Donoho D L. Compressed sensing [J]. IEEE Trans. Information Theory,2006,52(4):1289-1307.
[127] Compressive sensing resources [EB]. http://dsp.rice.edu/cs.
[128] Gribonval R, Bacry E. Harmonic decomposition of audio signals with matchingpursuit [J]. IEEE Trans. Signal Processing,2003,51(1):101-111.
[129] Tipping M E. Sparse Bayesian learning and the relevance vector machine [J].Journal of Machine Learning Research,2001,1:211-244.
[130] Zibulevsky M, Elad M. L1-L2optimization in signal and image processing [J].IEEE Signal Processing Magazine,2010,5:76-88.
[131] Eslami R, Jacob M. Robust reconstruction of MRSI data using a sparse spectralmodel and high resolution MRI priors [J]. IEEE Trans. Medical Imaging,2010,29(6):1297-1309.
[132] Pati Y C, Rezaiifar R, Krishnaprasad P S. Orthogonal matching pursuit: Recursivefunction approximation with applications to wavelet decomposition [C]. The27thAsilomar Conference on Signals, Systems and Computers, CA, USA1993:40-44.
[133] Cotter S F, Adler J, et al. Forward sequential algorithms for best basis selection[J]. Proc. Inst. Elect. Eng. Vision, Image, Signal Processing,1999,146(5):235-244.
[134] Natarajan B K. Sparse approximate solutions to linear systems [J]. SIAM Journalof Computation,1995,24(2):227-234.
[135] Chen S, Wigger J. Fast orthogonal least squares algorithm for efficient subsetmodel selection [J]. IEEE Trans. Signal Processing,1995,43(7):1713-1715.
[136] Donoho D L, Tsaig Y, et al. Sparse solution of underdetermined systems of linearequations by stagewise orthogonal matching pursuit [J]. IEEE Trans. InformationTheory,2012,58(2):1094-1121.
[137] Cotter S F, Rao B D, et al. Sparse solutions to linear inverse problems withmultiple measurement vectors [J]. IEEE Trans. Signal Processing,2005,53(7):2477-2488.
[138] Davies M E, Eldar Y C. Rank awareness in joint sparse recovery [J]. IEEE Trans.Information Theory,2012,58(2):1135-1146.
[139] Cotter S F, Rao B D. Sparse channel estimation via matching pursuit withapplication to equalization [J]. IEEE Trans. Communications,2002,50(3):374-377.
[140] Mileounis G, Babadi B, et al. An adaptive greedy algorithm with application tononlinear communications [J]. IEEE Trans. Signal Processing,2010,58(6):2998-3007.
[141] Tropp J A. Greed is good: Algorithmic results for sparse approximation [J]. IEEETrans. Information Theory,2004,50(10):2231-2242.
[142] Tropp J A, Gilbert A C, Strauss M J. Algorithms for simultaneous sparseapproximation. Part I: Greedy pursuit [J]. Signal Processing,2006,86:572-588.
[143] Davenport M A, Wakin M B. Analysis of orthogonal matching pursuit using therestricted isometry property [J]. IEEE Trans. Information Theory,2010,56(9):4395-4401.
[144] Zhang T. Sparse recovery with orthogonal matching pursuit under RIP [J]. IEEETrans. Information Theory,2011,57(9):6215-6221.
[145] Portilla J, Mancera L. L0-based sparse approximation: Two alternative methodsand some applications [C]. Wavelets XII, Spain,2007, Z:1-15.
[146] Mohimani H, Zadeh M B, Jutten C. A fast approach for overcomplete sparsedecomposition based on smoothed0norm [J]. IEEE Trans. Signal Processing,2009,57(1):289-301.
[147] Hyder M M, Mahata K. An improved smoothed0approximation algorithm forsparse representation [J]. IEEE Trans. Signal Processing,2010,58(4):2194-2205.
[148] Mourad N, Reilly J P. Minimizing nonconvex functions for sparse vectorreconstruction [J]. IEEE Trans. Signal Processing,2010,58(7):3485-3496.
[149] Gu Y, Jin J, Mei S.0norm constraint LMS algorithm for sparse systemidentification [J]. IEEE Signal Processing Letters,2009,16(9):774-777.
[150] Nikolova M, Ng M K, Tam C P. Fast nonconvex nonsmooth minimizationmethods for image restoration and reconstruction [J]. IEEE Trans. Image Processing,2010,19(12):3073-3088.
[151] Gorodnitsky I F, Rao B D. Sparse signal reconstruction from limited data usingFOCUSS: A re-weighted minimum norm algorithm [J]. IEEE Trans. Signal Processing,1997,45(3):600-616.
[152] Zdunek R, Cichocki A. Improved M-FOCUSS algorithm with overlapping blocksfor locally smooth sparse signals [J]. IEEE Trans. Signal Processing,2008,56(10):4752-4761.
[153] Rakotomamonjy A, Flamary R, et al. p-q penalty for sparse linear and sparsemultiple kernel multitask learning [J]. IEEE Trans. Neural Networks,2011,22(8):1307-1320.
[154] Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit[J]. SIAM Review,2001,43(1):129-159.
[155] Tropp J A. Algorithms for simultaneous sparse approximation. Part II: Convexrelaxation [J]. Signal Processing,2006,86:589-602.
[156] Tropp J A. Just relax: Convex programming methods for identifying sparsesignals in noise [J]. IEEE Trans. Signal Processing,2006,52(3):1030-1051.
[157] Stojnic M.2/1–optimization in block-sparse compressed sensing and its strongthresholds [J]. IEEE Journal of Selected Topics in Signal Processing,2010,4(2):350-357.
[158] Boyd S, Vandenberghe L. Convex optimization [M]. Cambridge University Press,New York,2009.
[159] Sturm J S. Using SeDuMi1.02, A Matlab toolbox for optimization oversymmetric cones [EB]. http://fewcal.kub.nl/~strum.
[160] Fuchs J J. Multipath time-delay detection and estimation [J]. IEEE Trans. SignalProcessing,1999,47(1):237-243.
[161] Fuchs J J, Delyon B. Minimum L1-norm reconstruction function for oversampledsignals: Application to time-delay estimation [J]. IEEE Trans. Information Theory,2000,46(7):1666-1673.
[162] Fuchs J J. On the application of the global matched filter to DOA estimation withuniform circular arrays [J]. IEEE Trans. Signal Processing,2001,49(4):702-709.
[163] Model D, Zibulevsky M. Signal reconstruction in sensor arrays using sparserepresentations [J]. Signal Processing,2006,86:624-638.
[164] Gershman A B, Sidiropoulos N D, et al. Convex optimization-based beamforming:From receive to transmit and network designs [J]. IEEE Signal Processing Magazine,2010,5:62-75.
[165] Kopsinis Y, Slavakis K, Theodoridis S. Online sparse system identification andsignal reconstruction using projections onto weighted1balls [J]. IEEE Trans. SignalProcessing,2011,59(3):936-952.
[166] Donoho D L, Huo X. Uncertainty principles and ideal atomic decomposition [J].IEEE Trans. Information Theory,2001,47(7):2845-2862.
[167] Fuchs J J. On sparse representations in arbitrary redundant bases [J]. IEEE Trans.Information Theory,2004,50(6):1341-1344.
[168] Candes E J, Tao T. Decoding by linear programming [J]. IEEE Trans. InformationTheory,2005,51(12):4203-4215.
[169] Chen J, Huo X. Theoretical results on sparse representations ofmultiple-measurement vectors [J]. IEEE Trans. Signal Processing,2006,54(12):4634-4643.
[170] Donoho D L, Elad M, Temlyakov V N. Stable recovery of sparse overcompleterepresentations in the presence of noise [J]. IEEE Trans. Information Theory,2006,52(1):6-18.
[171] Davies M E, Gribonval R. Restricted isometry constants where p sparse recoverycan fail for0
[174] Maleki A, Donoho D L. Optimally tuned iterative reconstruction algorithms forcompressed sensing [J]. IEEE Journal of Selected Topics in Signal Processing,2010,4(2):330-341.
[175] Tipping M E. The relevance vector machine [J]. Advances in Neural InformationProcessing Systems,2000,12:652-658.
[176] Tipping M E. Bayesian inference: An introduction to principles and practice inmachine learning [J]. Advanced Lectures on Machine Learning,2004:41-62.
[177] Wipf D P, Rao B D. Sparse Bayesian learning for basis selection [J]. IEEE Trans.Signal Processing,2004,52(8):2153-2164.
[178] Wipf D P, Rao B D. An empirical Bayesian strategy for solving the simultaneoussparse approximation problem [J]. IEEE Trans. Signal Processing,2007,55(7):3704-3716.
[179] Wipf D P, Rao B D, Nagarajan S. Latent variable Bayesian models for promotingsparsity [J]. IEEE Trans. Information Theory,2011,57(9),6236-6255.
[180] Kim J M, Lee O K, Ye J C. Compressive MUSIC: Revisiting the link betweencompressive sensing and array signals processing [J]. IEEE Trans. Information Theory,2012,58(1):278-300.
[181] Williams O, Blake A, Cipolla R. Sparse Bayesian learning for efficient visualtracking [J]. IEEE Trans. Pattern Analysis and Machine Intelligence,2005,27(8):1292-1304.
[182] Qiu K, Dogandzic A. Variance-component based sparse signal reconstruction andmodel selection [J]. IEEE Trans. Signal Processing,2010,58(6):2935-2952.
[183] Wu J, Liu F, et al. Compressive sensing SAR image reconstruction based onBayesian framework and evolutionary computation [J]. IEEE Trans. Image Processing,2011,20(7):1904-1911.
[184] Babacan S D, Molina R, Katsaggelos A K. Variational Bayesian super resolution[J]. IEEE Trans. Image Processing,2011,20(4):984-999.
[185] Zhou M, Chen H, et al. Nonparametric Bayesian dictionary learning for analysisof noisy and incomplete images [J]. IEEE Trans. Image Processing,2012,21(1):130-144.
[186] Wipf D, Nagarajan S. A unified Bayesian framework for MEG/EEG sourceimaging [J]. NeuroImage,2009,44:947-966.
[187] Wipf D P, Owen J P, et al. Robust Bayesian estimation of the location, orientation,and time course of multiple correlated neural sources using MEG [J]. NeuroImage,2010,49:641-655.
[188] Zhang Z, Rao B D. Sparse signal recovery with temporally correlated sourcevectors using sparse Bayesian learning [J]. IEEE Journal of Selected Topics in SignalProcessing,2011,5(5):912-926.
[189] Chen S, Gunn S R, Harris C J. The relevance vector machine technique forchannel equalization application [J]. IEEE Trans. Neural Networks,2001,12(6):1529-1532.
[190] Hwang K, Choi S. Blind equalization method based on sparse Bayesian learning[J]. IEEE Signal Processing Letters,2009,16(4):315-318.
[191] Tan X, Li J. Range-doppler imaging via forward-backward sparse Bayesianlearning [J]. IEEE Trans. Signal Processing,2010,58(4):2421-2425.
[192] Austin C D, Moses R L, et al. On the relation between sparse reconstruction andparameter estimation with model order selection [J]. IEEE Journal of Selected Topics inSignal Processing,2010,4(3):560-570.
[193] Li J, Sadler M, Viberg M. Sensor array and multichannel signal processing [J].IEEE Signal Processing Magazine,2011,9:157-158.
[194] Karabulut G Z, Kurt T, Yongacoglu A. Estimation of directions of arrival bymatching pursuit (EDAMP)[J]. EURASIP Journal on Wireless Communications andNetworking,2005,2:197-205.
[195] Cotter S F. Multiple snapshot matching pursuit for direction of arrival (DOA)estimation [C].15th European Signal Processing Conference, Poznan, Poland,2007:247-251.
[196] Malioutov D, Cetin M, Willsky A S. A sparse signal reconstruction perspectivefor source localization with sensor arrays [J]. IEEE Trans. Signal Processing,2005,53(8):3010-3022.
[197] Hyder M M, Mahata K. Direction-of-arrival estimation using a mixed2,0normapproximation [J]. IEEE Trans. Signal Processing,2010,58(9):4646-4655.
[198] Sun K, Liu Y, et al. Adaptive sparse representation for source localization withgain/phase errors [J]. Sensors,2011,11:4780-4793.
[199] Zheng C, Li G, et al. An approach of DOA estimation using noise subspaceweighted1minimization [C]. IEEE ICASSP,2011:2856-2859.
[200] Yin J, Chen T. Direction-of-arrival estimation using a sparse representation ofarray covariance vectors [J]. IEEE Trans. Signal Processing,2011,59(9):4489-4493.
[201] Panahi A, Viberg M. Fast LASSO based DOA tracking [C].4th IEEEInternational Workshop on Computational Advances in Multi-Sensor AdaptiveProcessing,2011:397-400.
[202] Blunt S D, Chan T, Gerlach K. Robust DOA estimation: The reiterativesuperresolution (RISR) algorithm [J]. IEEE Trans. Aerospace and Electronic Systems,2011,47(1):332-346.
[203] Bilik I. Spatial compressive sensing for direction-of-arrival estimation of multiplesources using dynamic sensor arrays [J]. IEEE Trans. Aerospace and ElectronicSystems,2011,47(3):1754-1769.
[204] Xu X, Wei X, Ye Z. DOA estimation based on sparse signal recovery utilizingweighted1-norm penalty [J]. IEEE Signal Processing Letters,2012,19(3):155-158.
[205] Wipf D and Nagarajan S. Beamforming using the relevance vector machine [C].Proc. International Conference on Machine Learning, Corvallis, OR,2007:1-8.
[206] Rakotomamonjy A. Surveying and comparing simultaneous sparse approximation(or group-lasso) algorithms [J]. Signal Processing,2011,91:1505-1526.
[207] Yin W, Osher S, et al. Bregman iterative algorithms for1-minimization withapplications to compressed sensing [J]. SIAM Journal of Imaging Sciences,2008,1(1):143-168.
[208] Wipf D and Nagarajan S. A new view of automatic relevance determination [J].Advances in Neural Information Processing Systems,2008,8pages.
[209] Dempster A P, Laird N M, Rubin D B. Maximum likelihood from incompletedata via the EM algorithm [J]. Journal of Royal Statistical Society Series B,1977,39:1-38.
[210] Wipf D P. Bayesian methods for finding sparse representations [D]. San Diego,USA, University of California,2006.
[211] Brillinger D R. Time series: Data analysis and theory [M]. SIAM press,Philadelphia,2001.
[212]张贤达.矩阵分析与应用[M].北京:清华大学出版社,2004.
[213] Moussaoui S, Brie D, et al. Separation of non-negative mixture of non-negativesources using a Bayesian approach and MCMC sampling [J]. IEEE Trans. SignalProcessing,2006,54(11):4133-4145.
[214] Moffet A T. Minimum-redundancy linear arrays [J]. IEEE Trans. Antennas andPropagation,1968,16(2):172-175.
[215] Donoho D L, Tanner J. Sparse nonnegative solution of underdetermined linearequations by linear programming [J]. Proceedings of the National Academy of Sciences,2005,102(27):9446-9451.
[216] Manabe T, TaKai H. Superresolution of multipath delay profiles measured by PNcorrelation method [J]. IEEE Trans. Antennas and Propagation,1992,40(5):500-509.
[217] Bouchereau F, Brady D, Lanzl C. Multipath delay estimation using asuperresolution PN-correlation method [J]. IEEE Trans. Signal Processing,2001,49(5):938-949.
[218] Vanderveen M C, Veen A, Paulraj A. Estimation of multipath parameters inwireless communications [J]. IEEE Trans. Signal Processing,1998,46(3):682-690.
[219] Wang Z, Huang J, Zhou S. Application of compressive sensing to sparse channelestimation [J]. IEEE Communications Magazine,2010(11):164-174.
[220] Svantesson T. Modeling and estimation of mutual coupling in a uniform lineararray of dipoles [R]. Sweden, Dept. Signals and Systems, Chalmers Univ. of Tech.,Goteborg,1999, Tech. Rep. S-41296.
[221] Liu Z M, Huang Z T, Zhou Y Y. Bias analysis of MUSIC in the presence ofmutual coupling [J]. IET Signal Processing,2009,3(1):74-84.
[222] Glegg S. A passive sonar system based on an autonomous underwater vehicle [J].IEEE Journal of Oceanic Engineering,2001,26(4):700-710.
[223] Edelson G S, Tufts D W. On the ability to estimate narrow-band signal parametersusing towed arrays [J]. IEEE Journal of Oceanic Engineering,1992,17(1):48-61.
[224] Zeira A, Friedlander B. Direction finding with time-varying arrays [J]. IEEETrans. Signal Processing,1995,43(4):927-937.
[225] Friedlander B, Zeira A. Eigenstructure-based algorithms for direction finding withtime-varying arrays [J]. IEEE Trans. Aerospace and Electronic Systems,1996,32(2):689-701.
[226] Ng B C, Ser W. Array shape calibration using sources in known locations [C].IEEE ICCS, Singapore,1992:836-840.
[227] Adve R S， Sarkar T K. Compensation for the effects of mutual coupling ondirect data domain adaptive algorithms [J]. IEEE Trans. Antennas and Propagation,2000,48(1):86-94.
[228] Hui H T, Low H P, et al. Receiving mutual impedance between two normal modehelical antennas (NMHAs)[J]. IEEE Antenna and Propagation Magazine,2006,48(4):92-96.
[229] Ye Z, Liu C. On the resiliency of MUSIC direction finding against antenna sensorcoupling [J]. IEEE Trans. Antennas and Propagation,2008,56(2):371-380.
[230] Weiss A J, Friedlander B. Eigenstructure methods for direction finding withsensor gain and phase uncertainties [J]. Circuits Systems Signal Processing,1990,9(3):271-300.
[231] Li Y, Er M H. Theoretical analyses of gain and phase error calibration withoptimal implementation for linear equispaced array [J]. IEEE Trans. Signal Processing,2006,54(2):712-723.
[232] Chen Y M, Lee J H, Yeh C C, Mar J. Bearing estimation without calibration forrandomly perturbed arrays [J]. IEEE Trans. Signal Processing,1991,39(1):194-197.
[233] Takahashi T, Nakamoto N, et al. On-board calibration methods for mechanicaldistortions of satellite phased array antennas [J]. IEEE Trans. Antennas and Propagation,2012,60(3):1362-1372.
[234] See C M S. Method for array calibration in high-resolution sensor arrayprocessing [J]. IEE Proc. Radar, Sonar, Navig.,1995,142(3):90-96.
[235] Ng B C, See C M S. Sensor-array calibration using a maximum-likelihoodapproach [J]. IEEE Trans. Antennas and Propagation,1996,44(6):827-835.
[236] Svantesson T. Modeling and estimation of mutual coupling in a uniform lineararray of dipoles [R]. Dept. Signals and Systems, Chalmers Univ. of Technology,Goteborg, Sweden, Tech. Rep. S-41296,1999.
[237] Friedlander B, Weiss A J. Performance of direction-finding systems with sensorgain and phase uncertainties [J]. Circuits Systems Signal Processing,1993,12(1):3-35.
[238] Wahlberg B, Ottersten B, Viberg M. Robust signal parameter estimation in thepresence of array perturbations [C]. Sweden, Apr.1991, ICASSP-1991:3277-3280.
[239] Figueiredo M A T. Adaptive sparseness for supervised learning [J]. IEEE Trans.Pattern Analysis and Machine Intelligence,2003,25(9):1150-1159.
[240] Tzikas D G, Likas A C, Galatsanos N P. The variational approximation forBayesian inference [J]. IEEE Signal Processing Magazine,2008,6:131-146.
[241] Wu C. On the convergence properties of the EM algorithm [J]. Annual Statistics,1983,11(1):95-103.
[242] Boyles R A. On the convergence of the EM algorithm [J]. Journal of RoyalStatistics Series B,1983,45(1):47-50.
[243] Ji S, Dunson D, Carin L. Multitask compressive sensing [J]. IEEE Trans. SignalProcessing,2009,57(1):92-106.
[244] Baron D, Wakin M B, et al. Distributed compressed sensing [EB]. Nov.2005.
[245] Moon T K. The expectation-maximization algorithm [J]. IEEE Signal ProcessingMagazine,1996,6:47-60.
[246] Shutin D, Buchgraber T, et al. Fast variational sparse Bayesian learning withautomatic relevance determination for superimposed signals [J]. IEEE Trans. SignalProcessing,2011,59(12):6257-6261.