空间信号自适应波束形成与参数估计算法研究
|
摘要
空间信号自适应波束形成与参数估计是阵列信号处理中的两个主要研究内容。其在雷达、声纳、地震探测、电子侦察、射电天文等领域有广泛而重要的应用前景。本文研究了不同应用背景和实际环境中空间信号自适应波束形成和参数估计问题。给出了一些有效的算法,并通过理论分析和仿真实验进行了验证。主要工作包括以下几个方面:
1.提出一种基于数据域的二维自适应波束形成算法,将标准的Capon波束形成(Standard Capon Beamforming, SCB)算法的高维权向量分解成两个低维权向量的Kronecker积的形式,通过双迭代算法求解两个低维权向量,再还原成所需的高维权向量。由于使用的数据矩阵维数比SCB算法的相关矩阵维数大大降低,相同情况下用较小的快拍可得到较优的采样数据矩阵的估计,使得所给算法在小快拍下性能优于标准的SCB波束形成算法。分解的两个权向量维数明显降低,使用数据矩阵也避免了大维数采样协方差矩阵的求逆,计算量显著减小。理论上详细分析并对比了所提算法和经典Capon法的计算量。仿真实验对所给算法的收敛速度、方向图、信噪比及快拍变化下的输出信干噪比、指向误差变化下的输出信干噪比等性能进行了研究。
2.针对高斯白噪声环境,提出一种非酉联合对角化算法估计二维频率。利用二维数据的旋转不变性,构造四个具有对角结构的数据矩阵,在时域进一步扩展,形成一组对角结构的数据矩阵。通过数据矩阵组的联合对角化,实现二维频率的估计,所得二维频率能自动配对。相对最近提出的多阶段分解与重构算法,该方法每步迭代具有精确的最小二乘闭式解,消除了多阶段算法的累积误差,提高了估计精度。在理论上证明了所提算法的渐近收敛性。
3.推广了上述非酉联合对角化算法,并将其应用到波达方向估计和谐波恢复中。利用平移不变阵列信号子空间的旋转不变性,构造一组具有对角结构的空时相关矩阵。为抑制噪声、减少计算量并加快收敛速度,对矩阵组降维处理,利用降维后相关矩阵组的结构信息,基于非线性最小二乘建立二次代价函数,提出一种新的三迭代算法(TIA)求解波达方向。谐波恢复中也有类似的结构信息,利用TIA算法同样可恢复谐波信号。所求的波达方向和恢复的谐波频率均不需要配对算法支持,能实现自动配对。仿真结果证实了所提算法的有效性。所提的TIA算法是上述非酉联合对角化算法的进一步推广,其右对角化因子矩阵具有多样性,可解决更广泛的一类问题。
4.在任意平面阵列下提出一种二维波达方向跟踪算法。由于引入了辅助变量,使得该算法可用于色噪声下的波达方向跟踪。该方法采用秩1更新结构,构造两个无约束的代价函数,求其递归最小二乘解获得信号子空间。为了简化运算,将推导过程进行了两次近似,运算复杂度显著降低。最后对跟踪结果正交化,获得了良好的正交性。理论上计算并对比了本章算法和经典的EIV-PAST算法的计算量。仿真实验在快变化和慢变化两种情况下对所提算法和EIV-PAST算法的跟踪结果进行了观察。对两种算法跟踪的信号子空间的误差、子空间夹角、正交性误差进行了对比。
The adaptive beamforming and parameters estimation of spatial signal are two important research fields of array signal processing. They finds wide applications in many fields such as radar, sonar, seismic exploration, electronic surveillance, radio astronomy, etc. this dissertation aims at the problems of adaptive beamforming and estimation of signal parameters in different practical environments, provide several useful algorithms and verifies them by computer simulations. The main contents of this dissertation can be summarized as follows:
1. Based on the data matrix, a novel algorithm termed bi-iteration algorithm (BIA) is proposed to implement the two-dimension (2-D) adaptive beamforming. The long weight vector of the Standard Capon beamforming (SCB) can be written as a kronecker product of two short weight vectors. The proposed BIA is used to deduce the two short weight vectors. Then the long weight vector can be reconstructed through the two short weight vectors by Kronecker product. Since the dimension of data matrix we use is much lower than the dimension of correlation matrix in the standard capon beamforming, we can obtain a better estimation of data matrix than correlation matrix in the case of less snapshots. Hence our BIA shows a better performance in the case of small snapshots. Moreover, the inverse of the high-dimensional correlation matrix is avoided by using the data matrix, so the computational complexity of BIA is remarkably reduced. A set of experiments is presented to compare the proposed BIA with the well known Capon beamforming in the performance of convergence speed, the beampattern, the output SINR versus input SNR, input snapshots and so on.
2. A non-unitary joint diagonalization algorithm is proposed to estimate the 2-D frequencies embedded in additive Gaussian white noise. By exploiting the rotational invariance property of the 2-D data matrix, we establish four matrices which possess diagonal structures. Moreover, we expand the data matrix in temporal domain and derive a set of diagonal structure matrices. Consequently the 2-D frequencies are estimated by accomplishing the joint diagonalization of the group of data matrices. It is worth mentioning that the estimated 2-D frequencies can be paired automatically. The proposed algorithm eliminates the error propagation of the multistage decomposition algorithm because each iteration poses a typical least square problem with a unique closed solution. Hence the estimation accuracy is increased. The asymptotical convergence of the proposed algorithm is also proved.
3. We develop a generalized version of non-unitary joint diagonalization algorithm, and then propose an algorithm to resolve the problems of direction of arrival and harmonic retrieval. The underlying rotational invariance among signal subspace induced by an array of sensors with a displacement invariance structure is exploited, a set of spatio-temporal correlation matrices possessing diagonal structures are introduced. To make the computational complexity lower and the convergence speed faster, we deduce a dimension-reduction matrix dealing with the set of correlation matrices. Using the structure information of the dimension-reduction correlation matrices, a novel cost function is proposed based on nonlinear least squares. Afterwards, a new approach termed tri-iterative algorithm (TIA) is derived for solving the cost function and estimating the direction of arrival of sources and the harmonic frequencies. Simulation results demonstrate the effectiveness of the proposed algorithm.
4. An algorithm is proposed to track the azimuth and elevation angle in the case of arbitrary plane antenna array. Introducing instrumental variable can make this algorithm used in colored noise environment. It makes use of the rank one update model to construct two unconstrained cost functions, then the signal subspace can be obtained through the recursive least square solutions of the cost functions. Two proper approximations are made in the deduction to reduce the computational complexity. The tracked signal subspace are orthonormalized to obtain better performance. The computational complexity between the proposed algorithm and the well known EIV-PAST algorithm is detailly analyzed and compared. Simulations are conducted to show the tracking performance of the proposed algorithm and the EIV-PAST algorithm. Specially, we compare the error of signal subspace, the subspace angle and the error of orthonormality.
1.提出一种基于数据域的二维自适应波束形成算法,将标准的Capon波束形成(Standard Capon Beamforming, SCB)算法的高维权向量分解成两个低维权向量的Kronecker积的形式,通过双迭代算法求解两个低维权向量,再还原成所需的高维权向量。由于使用的数据矩阵维数比SCB算法的相关矩阵维数大大降低,相同情况下用较小的快拍可得到较优的采样数据矩阵的估计,使得所给算法在小快拍下性能优于标准的SCB波束形成算法。分解的两个权向量维数明显降低,使用数据矩阵也避免了大维数采样协方差矩阵的求逆,计算量显著减小。理论上详细分析并对比了所提算法和经典Capon法的计算量。仿真实验对所给算法的收敛速度、方向图、信噪比及快拍变化下的输出信干噪比、指向误差变化下的输出信干噪比等性能进行了研究。
2.针对高斯白噪声环境,提出一种非酉联合对角化算法估计二维频率。利用二维数据的旋转不变性,构造四个具有对角结构的数据矩阵,在时域进一步扩展,形成一组对角结构的数据矩阵。通过数据矩阵组的联合对角化,实现二维频率的估计,所得二维频率能自动配对。相对最近提出的多阶段分解与重构算法,该方法每步迭代具有精确的最小二乘闭式解,消除了多阶段算法的累积误差,提高了估计精度。在理论上证明了所提算法的渐近收敛性。
3.推广了上述非酉联合对角化算法,并将其应用到波达方向估计和谐波恢复中。利用平移不变阵列信号子空间的旋转不变性,构造一组具有对角结构的空时相关矩阵。为抑制噪声、减少计算量并加快收敛速度,对矩阵组降维处理,利用降维后相关矩阵组的结构信息,基于非线性最小二乘建立二次代价函数,提出一种新的三迭代算法(TIA)求解波达方向。谐波恢复中也有类似的结构信息,利用TIA算法同样可恢复谐波信号。所求的波达方向和恢复的谐波频率均不需要配对算法支持,能实现自动配对。仿真结果证实了所提算法的有效性。所提的TIA算法是上述非酉联合对角化算法的进一步推广,其右对角化因子矩阵具有多样性,可解决更广泛的一类问题。
4.在任意平面阵列下提出一种二维波达方向跟踪算法。由于引入了辅助变量,使得该算法可用于色噪声下的波达方向跟踪。该方法采用秩1更新结构,构造两个无约束的代价函数,求其递归最小二乘解获得信号子空间。为了简化运算,将推导过程进行了两次近似,运算复杂度显著降低。最后对跟踪结果正交化,获得了良好的正交性。理论上计算并对比了本章算法和经典的EIV-PAST算法的计算量。仿真实验在快变化和慢变化两种情况下对所提算法和EIV-PAST算法的跟踪结果进行了观察。对两种算法跟踪的信号子空间的误差、子空间夹角、正交性误差进行了对比。
The adaptive beamforming and parameters estimation of spatial signal are two important research fields of array signal processing. They finds wide applications in many fields such as radar, sonar, seismic exploration, electronic surveillance, radio astronomy, etc. this dissertation aims at the problems of adaptive beamforming and estimation of signal parameters in different practical environments, provide several useful algorithms and verifies them by computer simulations. The main contents of this dissertation can be summarized as follows:
1. Based on the data matrix, a novel algorithm termed bi-iteration algorithm (BIA) is proposed to implement the two-dimension (2-D) adaptive beamforming. The long weight vector of the Standard Capon beamforming (SCB) can be written as a kronecker product of two short weight vectors. The proposed BIA is used to deduce the two short weight vectors. Then the long weight vector can be reconstructed through the two short weight vectors by Kronecker product. Since the dimension of data matrix we use is much lower than the dimension of correlation matrix in the standard capon beamforming, we can obtain a better estimation of data matrix than correlation matrix in the case of less snapshots. Hence our BIA shows a better performance in the case of small snapshots. Moreover, the inverse of the high-dimensional correlation matrix is avoided by using the data matrix, so the computational complexity of BIA is remarkably reduced. A set of experiments is presented to compare the proposed BIA with the well known Capon beamforming in the performance of convergence speed, the beampattern, the output SINR versus input SNR, input snapshots and so on.
2. A non-unitary joint diagonalization algorithm is proposed to estimate the 2-D frequencies embedded in additive Gaussian white noise. By exploiting the rotational invariance property of the 2-D data matrix, we establish four matrices which possess diagonal structures. Moreover, we expand the data matrix in temporal domain and derive a set of diagonal structure matrices. Consequently the 2-D frequencies are estimated by accomplishing the joint diagonalization of the group of data matrices. It is worth mentioning that the estimated 2-D frequencies can be paired automatically. The proposed algorithm eliminates the error propagation of the multistage decomposition algorithm because each iteration poses a typical least square problem with a unique closed solution. Hence the estimation accuracy is increased. The asymptotical convergence of the proposed algorithm is also proved.
3. We develop a generalized version of non-unitary joint diagonalization algorithm, and then propose an algorithm to resolve the problems of direction of arrival and harmonic retrieval. The underlying rotational invariance among signal subspace induced by an array of sensors with a displacement invariance structure is exploited, a set of spatio-temporal correlation matrices possessing diagonal structures are introduced. To make the computational complexity lower and the convergence speed faster, we deduce a dimension-reduction matrix dealing with the set of correlation matrices. Using the structure information of the dimension-reduction correlation matrices, a novel cost function is proposed based on nonlinear least squares. Afterwards, a new approach termed tri-iterative algorithm (TIA) is derived for solving the cost function and estimating the direction of arrival of sources and the harmonic frequencies. Simulation results demonstrate the effectiveness of the proposed algorithm.
4. An algorithm is proposed to track the azimuth and elevation angle in the case of arbitrary plane antenna array. Introducing instrumental variable can make this algorithm used in colored noise environment. It makes use of the rank one update model to construct two unconstrained cost functions, then the signal subspace can be obtained through the recursive least square solutions of the cost functions. Two proper approximations are made in the deduction to reduce the computational complexity. The tracked signal subspace are orthonormalized to obtain better performance. The computational complexity between the proposed algorithm and the well known EIV-PAST algorithm is detailly analyzed and compared. Simulations are conducted to show the tracking performance of the proposed algorithm and the EIV-PAST algorithm. Specially, we compare the error of signal subspace, the subspace angle and the error of orthonormality.
引文
[1]张贤达,保铮.通信信号处理.国防工业出版社. 2002年.
[2]Jian L, Stoica P, Zhisong W. On robust capon beamforming and diagonal loading. IEEE Trans on Signal Processing, 2003, 51(7), pp. 1702-1715.
[3]游鸿,黄建国,徐贵民.基于MVDR的阵列信号波束域预处理算法.系统工程与电子技术. 2008, 30(1), pp. 64-67.
[4]苏保伟,王永良,周良柱.基于LCMV线性约束的自适应方向图控制.电子与信息学报. 2008, 30(3), pp. 282-285.
[5]Griffiths L J, Jim C W. An alternative approach to linearly constrained adaptive beamforming. IEEE Trans on Antennas Propagation, 1982, 30(1), pp. 24-27.
[6]Yeh C C. Coherent interference suppression by an antenna array of arbitrary geometry. IEEE Trans on Antennas Propagation, 1989, 37(10), pp. 1317-1322.
[7]Shahbazpanahi S, Gershman A B, Luo Z Q. Robust adaptive beamforming for general-rank signal models. IEEE Trans on Signal Processing, 2003, 51(9), pp. 2257-2269.
[8]Lee T S. Coherent interference suppression with complementally transformed adaptive beamformer. IEEE Trans on Antennas Propagation, 1998, 46(5), pp.609-617.
[9]Koford J S, Groner G F. The use of an adaptive threshold element to design a linear optimal pattern classifier. IEEE Trans on Inform Theory, 1966, 12(1), pp. 42-50.
[10]Steinbush K, Widrow B. A critical comparision of two kinds of adaptive classification networks. IEEE Trans on Electron. Comput., 1965, 14(10), pp. 737-740.
[11]Widrow B. Adaptive filtersⅠ: fundamentals. Stanford Electronic Laboratories, Stanford, CA, Rept. SEL, 1966, 12, pp. 66-126.
[12]Reed L S, Mallett J D. Rapid convergence rate in adaptive arrays. IEEE Trans on Aerospace Electronics System, 1974, 10(6), pp. 853-863.
[13]Miller T W. Introduction to adaptive arrays. John Wiley and Sons Inc., 1980.
[14]Gabriel W F. Using spectral estimation techniques in adaptive processing antenna systems. IEEE Trans on Antennas Propagation, 1986, 34(3), pp. 291-300.
[15]Adams R N, Horowitz L L, Senne K D. Adaptive main beam nulling for narrow beam antenna arrays. IEEE Trans on Aerospace Electronics System, 1980, 16, pp. 509-516.
[16]Sng Y H, Er M H, Soh Y C. Partially adaptive array design using DOA estimation and null steering. IEEE Proc. Radar, Sonar Navig., stem, 1995, 142(1), pp. 1-5.
[17]Van Veen B D, Roberts R A. Partially adaptive beamformer design via output power minimization. IEEE Trans on Acoust, Speech, Signal Processing, 1987, 35, pp. 1524-1532.
[18]Er M H, Cantoni A. Derivative constraints for broad-band element space antenna array processor. IEEE Trans on Acoust, Speech, Signal Processing, 1983, 31(12), pp. 1378-1393.
[19]Buckley K M, Griffths L J. An adaptive generalized sidelobe canceller with derivative constraints. IEEE Trans on Antennas Propagation, 1986, 34(3), pp. 311-319.
[20]Lee C, Lee J H. An efficient technique for eigenspace-based adaptive interference cancellation. IEEE Trans on Antennas Propagation, 1998, 46(5), pp. 694-699.
[21]Chang L, Yeh C C. Effect of pointing errors on the performance of the projection beamforming. IEEE Trans on Antennas Propagation, 1993, 41(8), pp. 1045-1056.
[22]Carlson B D. Covariance matrix estimation errors and diagonal loading in adaptive arrays. IEEE Trans on Aerospace Electronics System, 1988, 24(4), pp. 397-401.
[23]Guerci J R. Theory and application of covariance matrix taper for robust adaptive beamforming. IEEE Trans on Signal Processing, 1999, 47(4), pp. 977-985.
[24]Li J, Stoica P, Wang Zhisong. Doubly constrained robust capon beamforming. IEEE Trans on Signal Processing, 2004, 52(9), pp. 2407-2423.
[25]王安义,保铮,张林让.阵列估计误差和扰动误差引起方向图畸变的校正.西安电子科技大学学报. 1999, 26(5), pp. 614-618.
[26]程春悦,吕英华.一种稳健快速的波束形成算法.重庆邮电学院学报. 2005, 17(3), pp. 260-263.
[27]Shahram S A B, Luo ZhiQuan, Kon Max Wong. Robust adaptive beamforming for general-rank signal model. IEEE Trans on Signal Processing, 2003, 51(9), pp. 2257-2269.
[28]郭庆华,廖桂生.一种稳健的自适应波束形成器.电子与信息学报. 2004, 26(1), pp. 146-150.
[29]Lee C C, Lee J H. Eigenspace-based adaptive array-beamforming with robust capabilities. IEEE Trans on Antennas Propagation, 1997, 45(2), pp. 1711-1716.
[30]许志勇,保铮,廖桂生.一种非均匀邻接子阵结构及其部分自适应处理性能分析.电子学报. 1997, 25(9), pp. 20-24.
[31]Krim H, Viberg M. Two decades of array signal processing research. IEEE Trans on Signal Processing, 1996, 13(4), pp. 67-94.
[32]罗景青,保铮.雷达阵列信号处理技术的新进展(一).现代雷达. 1993, 15(2), pp. 110-120.
[33]孙超,李斌.加权子空间拟和算法-理论与应用.西北工业大学出版社. 1994年.
[34]Burg J P. Maximum entropy spectral analysis. Proc. of the 37-th meeting of the Annual Int. SEG Meeting, Oklahoma City, 1967.
[35]Capon J. High resolution frequency-wavenumber spectrum analysis. Proc. IEEE 1969, 57(4), pp. 1408-1418.
[36]于宏毅,保铮.循环平稳信号波达方向估计的算法和性能研究.西安电子科技大学博士论文. 1999年.
[37]Pisarrenko V F. The retrieval of harmonic from a covariance function. J. R. Astr. Soci, 1973, 33, pp. 347-366.
[38]Schmidt R O. Multiple emitter location and signal parameter estimation. IEEE Trans on Antennas Propagation, 1986, 34(3), pp. 276-280.
[39]Schmidt R O. Multiple emitter location and signal parameter estimation. In proc. RADC Spectrum Estimation Workshop, 1979, pp. 243-258.
[40]Paulraj A, Roy R, Kailath T. A Subspace Rotational Approach to Signal Parameter Estimation. Proc. IEEE, 1986, 7, pp. 1044-1045.
[41]Roy R, Paulraj A, Kailath T. ESPRIT-A subspace rotation approach to estimation ofparameters of cisoids in noise. IEEE Trans on Acoust, Speech, Signal Processing, 1986, 34(10), pp. 1340-1342.
[42]Swindlehurst A L, Ottersten B. Multiple invariance ESPRIT. IEEE Transactions on Signal Processing, 1992, 40(4), pp. 867-881.
[43]Haardt M, Nossek J A. Unitary ESPRIT - how to obtain increased estimation accuracy with a reduced computational burden. IEEE Trans on Signal Processing, 1995, 43(5), pp. 1232-1242.
[44]Tayem N, Kwon H M. Conjugate ESPRIT (C-ESPRIT). IEEE Trans on Antennas Propagation, 2004, 52(10), pp. 2618-2624.
[45]Roy R, Kailath T. ESPRIT-Estimation of signal parameters via rotational invariance techniques. IEEE Trans on Acoust, Speech, Signal Processing, 1989, 37(7), pp. 984-995.
[46]Viberg M, Otterstem B. Sensor array processing based on subspace fitting. IEEE Trans on Signal Processing, 1991, 39(5), pp. 1110-1121.
[47]Otterstem B, Viberg M, Kailath T. Analysis of subspace fitting and ML techniques for parameter estimation from sensor array data. IEEE Trans on Signal Processing, 1992, 40(3), pp. 590-600.
[48]Stoica P, Nehorai A. MUSIC, maximum likelihood, and Cramer-Rao bound. IEEE Trans on Acoust, Speech, Signal Processing, 1989, 37(5), pp. 720-741.
[49]Zoltowski M D, Kautz G M, Silverstein S D. Beamspace root-MUSIC. IEEE Trans on Signal Processing, 1993, 41(1), pp. 344-364.
[50]Xu G, Silverstein S D, Roy R H. Beamspace ESPRIT. IEEE Trans on Signal Processing, 1994, 42(1), pp. 349-356.
[51]Yeh C C. Simple computation of projection matrix for bearing estimations. Proc. Inst. Elect. Eng. F, 1987, 134(2), pp. 146-150.
[52]Yeh C C. Projection approach to bearing estimation, IEEE Trans on Acoust, Speech, Signal Processing, 1986, 34(5), pp. 1347-1349.
[53]Stoica P, S?derstr?m T. Statistical analysis of a subspace method for bearing estimation without eigen decomposition. Proc. Inst. Elect. Eng. F, 1986, 139(4), pp. 301-305.
[54]Marcos S, Benidir M. On a high resolution array processing method non based on the eigen analysis approach. Proc. IEEE Int. Conf. Acoust, Speech, Signal Processing, Albuquerque, NM, 1990, 4, pp. 2955–2958.
[55]Marcos S, Marsal A, Benidir M. The propagator method for source bearing Estimation. Signal Processing, 1995, 42(2), pp. 121-138.
[56]Eriksson A, Stoica P, S?derstr?m T. On-line subspace algorithms for tracking moving sources. IEEE Trans on Signal Processing, 1994, 42(9), pp. 2319-2330.
[57]Xin J, Sano A. Computationally efficient subspace-based method for direction of arrival estimation without eigen decomposition. IEEE Trans on Signal processing, 2004, 52(4), pp. 876-893.
[58]Swindlehurst A, Kailath T. Azimuth/elevation direction finding using regular array geometries. IEEE Trans on Signal Processing, 2005, 53(10), pp. 3651-3660.
[59]Van der Veen A J, Ober P, Deprettere E. Azimuth and elevation computation in high resolution DOA estimation. IEEE Trans on Signal Processing, 1992, 40(7), pp. 1828-1832.
[60]Lemma A N, Van der Veen A J, Deprettere E. Analysis of ESPRIT based joint angle-frequency estimation. Proc. IEEE ICASSP, 2000, pp. 3053-3056.
[61]Lemma A N, Van der Veen A J, Deprettere E. Analysis of joint angle-frequency estimation using ESPRIT. IEEE Trans on Signal Processing, 2003, 51(5), pp. 1264-1283.
[62]Yimin Zhang, Weifeng Mu, Amin M G. Subspace analysis of spatial Time-Frequency distributions matrices. IEEE Trans on Signal Processing, 2001, 49(4), pp. 747-758.
[63]Moeness G, Amin. Polarimetric Time-Frequency MUSIC in coherent signal environment. IEEE signal processing letters, 2003.
[64]Mathews C P. Eigen-structure techniques for 2-D angle estimation with uniform circular arrays. IEEE Trans on Signal Processing, 1994, 42(9), pp. 2395-2401.
[65]Mathews C P. Performance analysis of the UCA-ESPRIT algorithm for circular ring arrays. IEEE Trans on Signal Processing, 1994, 42(9), pp. 2535-2540.
[66]Yin Q, Newcomb R. Estimation of 2-D angles-of-arrival for cohere signals. In proc. ICASSP, Nanjing, China, 1989, pp. 541-544.
[67]Yeh C C, Lee J H, Chen Y M. Estimation two dimensional angles of arrival in coherent source enviroment. IEEE Trans on ASSP, 1989, 37(1), pp. 153-155.
[68]Kuduvall C R, Rangayyan R M. An algorithm for direct computation of 2-D linear prediction coefficients. IEEE Trans on Signal Processing, 1993, 42(2), pp. 996-999.
[69]Michael D. Zoltowski D, Cherian P. Real-time frequency and 2-D angle estimation with sub-nyquist spatio-temporal sampling. IEEE Trans on signal processing, 1994, 42(10), pp. 2781-2794.
[70]Michael D, Zoltowski D, Mathews C P. imation two dimensional angles of arrival in coherent source enviroment. IEEE Trans on Acoust, Speech, Signal Processing,1989, 37(1), pp. 153-155.
[71]Michael D, Zoltowski D, Cherian P. Real-time frequency and 2-D angle estimation with sub-nyquist spatio-sampling. Proc, ICASSP, 1993, pp. 117-120.
[72]Michael D, Zoltowski D, Kautz G M, Silverstein S D. Multifrequency beam space Root-MUSIC: an experimental evaluation. SPIE Advanced signal processing algorithms, architectures, and implementations, 1991, 1566, pp. 452-462.
[73]殷勤业,邹理和, Robert W Newcomb.一种高分辨二维信号参量估计方法.通信学报. 1991, 12(4), pp. 1-7.
[74]金梁,殷勤业.时空DOA矩阵方法.电子学报. 2000, 28(6), pp . 8-12.
[75]姚敏立,金梁,殷勤业.基于最小冗余线阵的共轭循环ESPRIT算法.电子科学学刊. 1999, 21(5), pp. 577-584.
[76]陈建峰,黄建国.阵列误差校正的综合校正补偿法.声学学报. 1998, 23(6), pp. 515-521.
[77]贾永康,保铮.时空二维信号模型下的波达方向估计方法及其性能分析.电子学报. 1997, 25(9), pp. 69-73.
[78]黄德双,保铮.前后项线性预测最小二乘方法实现高分辨空间谱估计方法研究.通信学报. 1992, 13(4), pp. 25-32.
[79]李有明.稳健的超分辨阵列信号处理及快速算法研究.西安电子科技大学博士论文. 1994年.
[80]张群飞,黄建国,保铮.用子空间旋转不变法同时估计水下多目标的距离和方位.声学学报. 1999, 24(4), pp. 400-406.
[81]廖桂生,保铮,王波.基于高阶累积量的盲高分辨DOA估计及其性能分析.通信学报. 1996, 17(4), pp. 9-14.
[82]王布宏,王永良,陈辉.一种新的相干信源DOA估计算法:加权空间平滑协方差矩阵的Toeplitz矩阵拟和.电子学报. 2003, 31(9), pp. 1394-1397.
[83]王布宏,王永良,陈辉.相干信源波达方向估计的广义最大似然算法.电子与信息学报. 2004, 26(2), pp. 225-232.
[84]陈辉,王永良.幂迭代子空间追踪算法.二十一世纪我国雷达发展研讨会论文集. 2000. 9, pp. 186-190.
[85]王永良,陈辉,万山虎.一种有效的超分辨空间谱估计方法虚拟阵列变换法.中国科学. 1999, 26(6), pp. 518-524.
[86]淦华东.自适应子空间估计及其在目标方位跟踪中的应用.西北工业大学硕士学位论文. 2005.
[87]Golub G H, Loan C F V. Matrix Computation. Baltimore, MD: Johns Hopkins Univ. Press, 1989.
[88]Moonen M, Dooren P V, Vandewalle J. Parallel and adaptive resolution direction finding. Proc. SPIE Advanced Algorithms and Architectures for Signal Processing. 1992, 1770, pp. 219-230.
[89]Moonen M, Dooren P V, Vandewalle J. Updating singular value decompositions: a parallel implementation. Proc. SPIE Advanced Algorithms and Architectures for Signal Processing. San Diego, 1989, 1152, pp. 80-91.
[90]Moonen M, Dooren P V, Vandewalle J. A singular value decomposition updating algorithm for subspace tracking. SIAM J. Matrix and Appl., 1992, 13(4), pp. 1015-1038.
[91]Moonen M, Dooren P V, Vandewalle J. A systolic array for SVD updating. SIAM J. Matrix and Appl., 1993, 14(2), pp. 353-371.
[92]Fuhrmann D R. An algorithm for subspace computation with applications in signal processing. SIAM J. Matrix and Appl., 1988, 9, pp. 213-220.
[93]Ammann L P. Robust singular value decompositions-A new approach to projection pursuit. J. Amer. Stat. Assoc., 1993, 88, pp. 505-514.
[94]Dowling E M, Ammann L P, Shamsunder S. A TQR-iteration based adaptive SVD for real time angle and frequency tracking. IEEE Trans on Signal Processing. 1994, 42(4), pp. 914-926.
[95]Chan T F. Alternative to the SVD: rank revealing QR factorization. Proc. SPIE Advanced Algorithms and Architectures for Signal Processing. San Diego, 1986, 696, pp. 31-37.
[96]Chan T F. Rank revealing QR factorization, Linear Algebra Application. 1987, 88-89, pp. 67-82.
[97]Fargues M P. Tracking moving sources using the rank-revealing QR factorization. Proceeding of the 25th Asilomar Conf. Sig. Syst, and Compt, 1991, pp.292-295.
[98]Stewart G W. A Jacobi-like algorithm for computing the Schur decomposition of non-Hermitian matrix. SIAM J. Sci Stat. Comput., 1985, 6, pp. 853-864.
[99]Stewart G W. An updating algorithm for subspace tracking, IEEE Trans on Signal Processing, 1992, 40(6), pp. l535- 1541.
[100]Adams G, Griffin M F, Stewert G W. Direction of arrival estimation using rank revealing URV decomposition. Proc. IEEE Int. Conf. Acoust., Speech Signal Processing, Toronto, Canada, 1991, pp. 1385-1388.
[101]Griffin M F, Stewert G W. Updating MUSIC and root-MUSIC with the rank revealing URV decomposition. Proceeding of 25th Asilomar Conf. Circuits. Syst. Comput., 1991, pp. 277-281.
[102]Golub G H. Some modified matrix eigenvalue problems. SIMA Rev, 1973, 15, pp. 318-334.
[103]Karasalo I. Estimating the covariance matrix by signal subspace averaging. IEEE Trans. Audio Speech Signal Processing, 1986, 34(1), pp. 8-12.
[104]Degroat R D. Noniterative subspace tracking. IEEE Trans on Signal Processing, 1992, 40(3), pp. 571-577.
[105]Bunch J R, Nielsen C P, Sorensen D C. Rank-one modification of the symmetric eigenproblem. Numer. Math., 1978, 31, pp. 31-38.
[106]Yu K B. Recursive updating the eigenvalue decomposition of a covariance matrix. IEEE Trans on Signal Processing, 1991, 39(5), pp. 1136-1145.
[107]Champagne B, Liu Q G. A new family of EVD tracking algorithms using Gives rotations. Proc. IEEE Int. Conf. Acoust., Speech Signal Processing, Atlanta, GA, 1996, 5, pp. 2539-2542.
[108]Owsley N L. Adaptive data orthogonalization. Proc. Conf. ICASSP-78, 1978, pp. 109-112.
[109]Thompson P A. An adaptive spectral analysis technique for unbiased frequency estimation in the presence of white noise. Proc. 13th Asilomar Conf. circuits. Syst. Comput., Pacific Grove, 1980, pp. 529-533.
[110]Yang J F, Keveh M. Adaptive eigensubspace algorithms for direction or frequency estimation and tracking. IEEE Trans on Acoust., Speech Signal Processing. 1988, 36(2), pp. 241-251.
[111]Sarkar T K, Dianat S A, Chen H. Adaptive spectral estimation by the conjugate gradient method. IEEE Trans on Acoust., Speech Signal Processing. 1986, 34, pp. 272-284.
[112]Fu W, Dowling E M. Conjugate gradient eigenstructure tracking for adaptive spectral estimation. IEEE Trans on Signal Processing. 1995, 43(5), pp. 1151-1160.
[113]Yang B. Projection approximation subspace tracking. IEEE Trans on Signal Processing, 1995, 43(1), pp. 95-107.
[114]Yang B. An extension of PASTd algorithm to both rank and subspace tracking. IEEE Signal Processing Letters, 1995, 2(9), pp. 179-182.
[115]Abed-Meraim K. Fast Orthonormal PAST algorithm. IEEE Signal Processing Letters, 2000, 7(3), pp. 60-62.
[116]Badeau R. Fast approximated power iteration subspace tracking. IEEE Trans on Signal Processing, 2005, 53(8), pp. 2931-2941.
[117]Chan Shing-Chow. A robust past algorithm for subspace tracking in impulsivenoise. IEEE Trans on Signal Processing, 2006, 54(1), pp. 105-116.
[118]Viberg M, Stoica P, Ottersten B. Array processing in correlated noise fields based on instrumental variables and subspace fitting. IEEE Trans on Signal Processing, 1995, 43, pp. 1187-1199.
[119]Gustafsson T. Instrumental variable subspace tracking using projection approximation. IEEE Trans on Signal Processing, 1998, 46(3), pp. 669-681.
[120] Soderstrom T, Stoica P. System Identification. London, U.K.:Prentice-Hall, 1999.
[121]周云钟,子空间跟踪及其在阵列信号处理中的应用,电子科技大学博士论文. 1999年。
[122]Peter Strobach. Bi-Iteration SVD subspace tracking algorithm. IEEE Trans on Signal Processing, 1997, 45(5), pp. 1222-1240.
[123]Ouyang Shan, Hua Yingbo. Bi-iteration least-square method for subspace tracking. IEEE Trans on Signal Processing, 2005, 53(8), pp. 2984-2996.
[1]Searle S R. Matrix algebra Useful for statistics. New York, John Wiley & Sons, 1982.
[2]Golub G H, Loan C F V. Matrix Computation. Baltimore, MD: Johns Hopkins Univ. Press, 1989.
[3]Schmidt R O. Multiple emitter location and signal parameter estimation. IEEE Trans on Antennas Propagation, 1986, 34(3), pp. 276-280.
[4]Paulraj A, Roy R, Kailath T. A Subspace Rotational Approach to Signal Parameter Estimation. Proc. IEEE, 1986, 7, pp. 1044-1045.
[5]Roy R, Paulraj A, Kailath T. ESPRIT-A subspace rotation approach to estimation of parameters of cisoids in noise. IEEE Trans on Acoust, Speech, Signal Processing, 1986, 34(10), pp. 1340-1342.
[6]Ottersten B, Viberg M, Kailath T. Performance analysis of the total least squares ESPRIT algorithm. IEEE Trans on Signal Processing, 1991, 39(5), pp. 1122-1135.
[1]张贤达,保铮.通信信号处理.国防工业出版社. 2002年.
[2]任超,吴嗣亮,王菊.基于空时二维结构的低旁瓣宽带波束形成.系统工程与电子技术. 2008, 30(2), pp. 414-417.
[3]Van Veen B D, Buckley K M. Beamforming: A versatile approach to spatial filtering. IEEE Trans on Acoust, Speech, Signal Processing, 1998, 5(2), pp. 4-24.
[4]Jian L, Stoica P, Zhisong W. On robust capon beamforming and diagonal loading. IEEE Trans on Signal Processing, 2003, 51(7), pp. 1702-1715.
[5]游鸿,黄建国,徐贵民.基于MVDR的阵列信号波束域预处理算法.系统工程与电子技术. 2008, 30(1), pp. 64-67.
[6]苏保伟,王永良,周良柱.基于LCMV线性约束的自适应方向图控制.电子与信息学报. 2008, 30(3), pp. 282-285.
[7]Griffiths L J, Jim C W. An alternative approach to linearly constrained adaptive beamforming. IEEE Trans on Antennas Propagation, 1982, 30(1), pp. 24-27.
[8]Yeh C C. Coherent interference suppression by an antenna array of arbitrary geometry. IEEE Trans on Antennas Propagation, 1989, 37(10), pp. 1317-1322.
[9]Shahbazpanahi S, Gershman A B, Luo Z Q. Robust adaptive beamforming for general-rank signal models. IEEE Trans on Signal Processing, 2003, 51(9), pp. 2257-2269.
[10]Lee T S. Coherent interference suppression with complementally transformed adaptive beamformer. IEEE Trans on Antennas Propagation, 1998, 46(5), pp. 609-617.
[11]Capon J. High resolution frequency-wavenumber spectrum analysis. Proc. IEEE 1969, 57(4), pp. 1408-1418.
[12]Lacoss R T. Data adaptive spectral analysis methods. Geophys, 1971, 36(4), pp. 661-675.
[13]Godara L G. Error analysis of the optimal antenna array processors. IEEE Trans onAerospace and Electronic System, 1986, 22(3), pp. 395-409.
[14]Seligson C D. Comments on high resolution frequency-wavenumber spectrum anlysis. Proc. IEEE, 1970, 58, pp. 947-949.
[15]Wax M, Anu Y. Performance analysis of the minimum variance beamforming. IEEE Trans on Signal Processing, 1996, 44(4), pp. 928-937.
[16]Viberg M, Swindlehurst A L. Analysis of the combined effects of finite samples and model errors on array processing performance. IEEE Trans on Signal Processing, 1994, 42(11), pp. 3073-3083.
[17]G. H. Golub and C. F. V. Loan, Matrix computation. Baltimore, MD: Johns Hopkins Univ. Press, 1989.
[18]Melvin W L. A STAP overview. IEEE Trans on Aerospace and Electronic System, 2004, 19(1), pp. 19-35.
[19]张贤达.矩阵分析与应用.清华大学出版社. 2004年.
[1]Clark M P, Scharf L L. Two-dimension Modal Analysis Based on Maximum Likelihood. IEEE Trans on Signal Processing, 1994, 42(6), pp. 1443-1451.
[2]Rao D V B, Kung S Y. A State-space Approach for the 2-D Harmonic Retrieval Problem. Proceedings of ICASSP, Adelaide, Australia, 1984, pp. 4101-4104.
[3]Hua Y. Estimating Two-dimension Frequencies by Matrix Enhancement and Matrix Pencil. IEEE Trans on Signal Processing, 1992, 40(9), pp. 2267-2280.
[4]Vanpoucke F, Moone M, Berthoumieu Y. An Efficient Subspace Algorithm for 2-D Harmonic Retrieval. Proceedings of ICASSP, Adelaide, Australia, 1994, pp. 461-464.
[5]谢民,付佗,高梅国.均匀圆形阵列的联合二维角度-频率估计.北京理工大学学报. 2007, 27(7), pp. 630-635.
[6]Rouquette S, Najim M. Estimation of Frequencies and Damping Factors by Two-dimensional ESPRIT type Methods. IEEE Trans on Signal Processing, 2001, 49(1), pp. 237-245.
[7]张贤达.矩阵分析与应用.清华大学出版社. 2004年.
[8]Weiss A J, Friedlander B. Array processing using joint diagonalization. Signal Processing, 1996, 87(1), pp. 1-12.
[9]Cardoso J F, Souloumiac A. Blind beamforming for non-Gaussian signals. IEE Proceedings-F, 1993, 140(6), pp. 362-370.
[10]Cardoso J F, Souloumiac A. Jacobi angles for simultaneous diagonalization. SIAM J. Matrix Anal. Appl, 1996, 17(1), pp. 161-164.
[11]Belouchrani A, Abed-Meraim K, Cardoso J F. A blind source separation technique using second-order statistics. IEEE Trans on Signal Processing, 1997, 45(2), pp. 1136-1155.
[12]Yeredor A. Non-orthogonal Joint Diagonalization in the Least-Squares Sense with Application in Blind Source Separation. IEEE Trans on Signal Processing, 2002, 50(7), pp. 1545-1553.
[13]Zhou Yi, Feng Dazheng, Liu Jianqiang. A Novel Algorithm for Two-dimensionalFrequency Estimation. Signal Processing, 2007, 87(1), pp. 1-12.
[14]Sidiropoulos N D, Giannakis G B, Bro R. Parallel Factor Analysis in Sensor Array Processing. IEEE Trans on Signal Processing, 2000, 48(8), pp. 2377-2388.
[15]J.J.Hopfield. Neural networks and physical systems with emergent collective computational abilities. Pro.Natl.Acad.Sci.USA, 1982, 79, pp. 2554-2558.
[16]J. P. LaSalle. The Stability of Dynamical Systems. Philadelphia, PA: SIAM Press, 1976.
[17]Ziehe A, Laskov P. A Fast Algorithm for Joint Diagnolization with Non-orthogonal Transformation and its Application to Blind Signal Separation. J Mach. Learn. Res., 2004, 5, pp. 777-800.
[1]Krim H, Viberg M. Two decades of array signal processing research. IEEE Trans on Signal Processing, 1996, 13(4), pp. 67-94.
[2] Veen B D, Buckley K M. Beamforming: a versatile approach to spatial filtering. IEEE Audio Speech Signal Processing, 1988, 5(2) , pp. 1-24.
[3]Kay S M, Marple S L. Spectrum analysis– a modern perspective. Proc. of IEEE, 1981, pp. 11.
[4]Burg J P. Maximum entropy spectral analysis. Proc. of the 37-th meeting of the Annual Int. SEG Meeting, Oklahoma City, OK, 1967.
[5]Capon J. High-resolution frequency-wavenumber spectrum analysis. Proc. IEEE, 1969, 57(8) , pp. 1408-1418.
[6]Schmidt R O. Multiple emitter location and signal parameter estimation. IEEE Trans on Antennas Propagation, 1986, 34(3) , pp. 276-280.
[7]Stoica P, Nehorai A. MUSIC, maximum likelihood, and Cramer-Rao bound. IEEE Trans on Audio Speech Signal Processing, 1989, 37(7) , pp. 720-741.
[8]Viberg M, Ottersten B, Kailath T. Detection and estimation in sensor arrays using weighted subspace fitting. IEEE Trans on Signal Processing, 1991, 39(11), pp. 2436-2449.
[9]Paulraj A, Roy R, Kailath T. A Subspace Rotational Approach to Signal Parameter Estimation. Proc. IEEE, 1986, 7, pp. 1044-1045.
[10]Roy R, Paulraj A, Kailath T. ESPRIT-A subspace rotation approach to estimation of parameters of cisoids in noise. IEEE Trans on Acoust, Speech, Signal Processing,1986, 34(10), pp. 1340-1342.
[11]Stoica P, Nehorai A. Performance comparison of subspace rotation and MUSIC methods for direction estimation. IEEE Trans on Signal Processing, 1991, 39(2), pp. 446-453.
[12]Rao B D, Hari K V S. Performance analysis of ESPRIT and TAM in determining the direction of arrival of plane waves in noise. IEEE Trans on Acoust, Speech, Signal Processing, 1989, 40(12), pp. 1990-1995.
[13]Roy R, Kailath T. ESPRIT-Estimation of signal parameters via rotational invariance techniques. IEEE Trans on Acoust, Speech, Signal Processing, 1989, 37(7), pp. 984-995.
[14]Ottersten B, Viberg M, Kailath T. Performance analysis of the total least squares ESPRIT algorithm. IEEE Trans on Signal Processing, 1991, 39(5), pp. 1122-1135.
[15]Steinhardt A O. Harmonic retrieval of analog signals. IEEE Trans on Acoust, Speech, Signal Processing, 1988, 36(1), pp. 134-136.
[16]Furhmann D, Liu B. Rotational search methods for adaptive Pisarenko harmonic retrieval. IEEE Trans on Acoust, Speech, Signal Processing, 1986, 34(12), pp. 1550-1565.
[17]Reddy V, Egardt B, Kailath T. A least-squares-types algorithm for adaptive implementation of Pisarenko’s harmonic retrieval method. IEEE Trans on Acoust, Speech, Signal Processing, 1982, 30(6), pp. 399-405.
[18]Micka O J, Weiss A J. Estimating frequencies of exponentials in noise using joint diagonalization. IEEE Transactions on Signal Processing, 1999, 47(2), pp. 341-348.
[19]Zhou Y, Feng D Z, Liu J Q. A novel algorithm for two-dimensional frequency estimation. Signal Processing, 2007, 87(1), pp. 1-12.
[20]Yeredor A. Non-orthogonal Joint Diagonalization in the Least-Squares Sense with Application in Blind Source Separation. IEEE Trans on Signal Processing, 2002, 50(7), pp. 1545-1553.
[21]Vollgraf R, Obermayer K. Quadratic optimization for simultaneous matrix diagonalization. IEEE Trans on Signal Processing, 2006, 54(9), pp. 3270-3278.
[22]Vanpoucke F, Moonen M, Berthoumieu Y. An efficient subspace algorithm for 2-D harmonic retrieval. Proc. IEEE ICASSP, Adelaide, Australia, 1994, pp. 461-464.
[23]Feng Da-Zheng, Zhang Xian-Da, Bao-Zheng. An efficient multistage decomposition approach for independent components. Signal processing, 2003, 83(1), pp. 181-197.
[24]Feng Dazheng, Zhang Xianda, Bao Zheng. A neural network learning for adaptivelyextracting cross-correlation features between two high-dimension data streams. IEEE Trans on Neural Networks, 2004, 15(6), pp. 1541-1553.
[25]Ziehe A, Laskov P, Nolte G. A fast algorithm for joint diagnolization with non-orthogonal transformation and its application to blind signal separation. J Mach. Learn. Res, 2004, 5(7), pp. 777-800.
[1]殷勤业,邹理和, Newcomb R.一种高分辨二维信号参数估计方法-波达方向矩阵.通信学报. 1991, 12(4), pp. 1-7.
[2] Challa R N, Shamsunder S. Higher-order subspace based algorithms for passive localization of near-field sources. Proc. 29-th Asilomar Conf. Signals System Computer, Pacific Grove. CA. 1995, pp. 777-781.
[3]吴湘霖,俞卞章,宋祖勋.基于空时扩展虚拟阵列的高分辨多参数联合估计.计算机工程与应用. 2005, (1), pp. 19-22.
[4]Schmidt R O. Multiple emitter location and signal parameter estimation . IEEE Trans on Antennas Propagation , 1986, 34(3), pp. 276-280.
[5]Roy R, Paulraj A, Kailath T. ESPRIT-A subspace rotation approach to estimation of parameters of cisoids in noise. IEEE Trans on Acoust., Speech, Signal Processing, 1986, 34(10), pp. 1340-1342.
[6]王永良,陈辉,彭应宁,万群.空间谱估计理论与算法.清华大学出版社. 2004年.
[7]Mathew G , Reddy V U, Dasgupta S. Adaptive estimation of eigensubspace. IEEE Trans on Signal Processing, 1995, 43(2) , pp. 401-411.
[8]Zhang JianKang. An adaptive quasi-Newton algorithm for eigensubspace estimation. IEEE Trans on Signal Processing, 2000, 48(12) , pp. 3328-3333.
[9]马长征,吴伟陵.自适应拟牛顿子空间跟踪算法分析.电子与信息学报. 2003, 25(2), pp. 200-205.
[10]Sastry K C. Adaptive algorithms for estimating the complete covariance eigenstructure. ICASSP, 1986, 34(10) , pp. 1401-1403.
[11]粟欣,李立萍,陈天麒.信号时变DOA的递推跟踪.中国电子学会电子对抗分会第十一届学术年会论文集(上):pp. 177-182.
[12]周云钟,陈天麒,黄香馥.高分辨率信号DOA估计的跟踪算法.电子科学学刊. 1997, 19(3), pp. 300-305.
[13]Xu G, Cho Y, Kailath T. Application of fast subspace decomposition to signalprocessing and communication problems. IEEE Trans on Signal Processing, 1994, 42(6) , pp. 1453-1461.
[14]李有明.稳健的超分辨阵列信号处理及快速算法研究.西安电子科技大学博士学位论文. 1994.
[15]Chan T F. Alternative to the SVD: rank revealing QR factorization. Proc. SPIE Advanced Algorithms and Architectures for Signal Processing. San Diego, 1986, 696, pp. 31-37.
[16]Chan T F. Rank revealing QR factorization. Linear Algebra Application, 1987, 88-89, pp. 67-82.
[17]Fargues M P. Tracking moving sources using the rank-revealing QR factorization. Proceeding of the 25th Asilomar Conf. Sig. Syst, and Compt, 1991, pp.292-295.
[18]Stewart G W. A Jacobi-like algorithm for computing the Schur decomposition of non-Hermitian matrix. SIAM J. Sci Stat. Comput., 1985, 6, pp. 853-864.
[19]Stewart G W. An updating algorithm for subspace tracking. IEEE Trans on Signal Processing, 1992, 40(6), pp. l535- 1541.
[20]Adams G, Griffin M F, Stewert G W. Direction of arrival estimation using rank revealing URV decomposition. Proc. IEEE Int. Conf. Acoust., Speech Signal Processing, Toronto, Canada, 1991, pp. 1385-1388.
[21]Griffin M F, Stewert G W. Updating MUSIC and root-MUSIC with the rank revealing URV decomposition. Proceeding of 25th Asilomar Conf . Circuits. Syst. Comput., 1991, pp. 277-281.
[22]陈辉,王永良.秩-1子空间跟踪算法.电子与信息学报. 2002, 24(5), pp. 626-629.
[23]Golub G H. Some modified matrix eigenvalue problems. SIMA Rev, 1973, 15, pp. 318-334.
[24]Karasalo I. Estimating the covariance matrix by signal subspace averaging. IEEE Trans on Audio Speech Signal Processing, 1986, 34(1), pp. 8-12.
[25]Degroat R D. Noniterative subspace tracking. IEEE Trans on Signal Processing, 1992, 40(3), pp. 571-577.
[26]Bunch J R, Nielsen C P, Sorensen D C. Rank-one modification of the symmetric eigenproblem. Numer. Math., 1978, 31, pp. 31-38.
[27]Yu K B. Recursive updating the eigenvalue decomppsition of a covariance matrix. IEEE Trans on Signal Processing, 1991, 39(5), pp. 1136-1145.
[28]Yang J F, Keveh M. Adaptive eigensubspace algorithm for direction or frequency estimation and tracking. IEEE Trans on Acoust Speech Signal Processing, 1988,36(2), pp. 241-251.
[29]Yang B. Projection approximation subspace tracking. IEEE Trans on Signal Processing, 1995, 43(1), pp. 95-107.
[30]Viberg M, Stoica P, Ottersten B. Array processing in correlated noise fields based on instrumental variables and subspace fitting. IEEE Trans on Signal Processing, 1995, 43, pp. 1187-1199.
[31]Stoica P, Jasson M. Estimating optimal weights for instrumental variable methods. Digital Signal Processing, 2001, 11(3), pp. 252 -268.
[32]Gustafsson T. Instrumental variable subspace tracking using projection approximation. IEEE Trans on Signal Processing, 1998, 46(3), pp. 669-681.
[33] Soderstrom T, Stoica P. System Identification. London, U.K.:Prentice-Hall, 1999.
[34]Stoica P, Jasson M. Estimating optimal weights for instrumental variable methods. Digital Signal Processing, 2001, 11(3), pp. 252-268.
[35]吴湘霖.非理想系统假设下阵列信号多信源多参数联合估计.西北工业大学博士学位论文. 2005年.
[36]Belouchrani A, Abed-Meraim K, Cardoso J F, Moulines E.A Blind Source Separation Technique Using Second-order Statistics. IEEE Trans on Signal Processing, 1997, 45(2), pp. 434-444.
[37]Roland B, Gael R, Bertrand D. Sliding window adaptive SVD algorithms. IEEE Trans on Signal Processing, 2004, 52(1), pp. 1-10.
[2]Jian L, Stoica P, Zhisong W. On robust capon beamforming and diagonal loading. IEEE Trans on Signal Processing, 2003, 51(7), pp. 1702-1715.
[3]游鸿,黄建国,徐贵民.基于MVDR的阵列信号波束域预处理算法.系统工程与电子技术. 2008, 30(1), pp. 64-67.
[4]苏保伟,王永良,周良柱.基于LCMV线性约束的自适应方向图控制.电子与信息学报. 2008, 30(3), pp. 282-285.
[5]Griffiths L J, Jim C W. An alternative approach to linearly constrained adaptive beamforming. IEEE Trans on Antennas Propagation, 1982, 30(1), pp. 24-27.
[6]Yeh C C. Coherent interference suppression by an antenna array of arbitrary geometry. IEEE Trans on Antennas Propagation, 1989, 37(10), pp. 1317-1322.
[7]Shahbazpanahi S, Gershman A B, Luo Z Q. Robust adaptive beamforming for general-rank signal models. IEEE Trans on Signal Processing, 2003, 51(9), pp. 2257-2269.
[8]Lee T S. Coherent interference suppression with complementally transformed adaptive beamformer. IEEE Trans on Antennas Propagation, 1998, 46(5), pp.609-617.
[9]Koford J S, Groner G F. The use of an adaptive threshold element to design a linear optimal pattern classifier. IEEE Trans on Inform Theory, 1966, 12(1), pp. 42-50.
[10]Steinbush K, Widrow B. A critical comparision of two kinds of adaptive classification networks. IEEE Trans on Electron. Comput., 1965, 14(10), pp. 737-740.
[11]Widrow B. Adaptive filtersⅠ: fundamentals. Stanford Electronic Laboratories, Stanford, CA, Rept. SEL, 1966, 12, pp. 66-126.
[12]Reed L S, Mallett J D. Rapid convergence rate in adaptive arrays. IEEE Trans on Aerospace Electronics System, 1974, 10(6), pp. 853-863.
[13]Miller T W. Introduction to adaptive arrays. John Wiley and Sons Inc., 1980.
[14]Gabriel W F. Using spectral estimation techniques in adaptive processing antenna systems. IEEE Trans on Antennas Propagation, 1986, 34(3), pp. 291-300.
[15]Adams R N, Horowitz L L, Senne K D. Adaptive main beam nulling for narrow beam antenna arrays. IEEE Trans on Aerospace Electronics System, 1980, 16, pp. 509-516.
[16]Sng Y H, Er M H, Soh Y C. Partially adaptive array design using DOA estimation and null steering. IEEE Proc. Radar, Sonar Navig., stem, 1995, 142(1), pp. 1-5.
[17]Van Veen B D, Roberts R A. Partially adaptive beamformer design via output power minimization. IEEE Trans on Acoust, Speech, Signal Processing, 1987, 35, pp. 1524-1532.
[18]Er M H, Cantoni A. Derivative constraints for broad-band element space antenna array processor. IEEE Trans on Acoust, Speech, Signal Processing, 1983, 31(12), pp. 1378-1393.
[19]Buckley K M, Griffths L J. An adaptive generalized sidelobe canceller with derivative constraints. IEEE Trans on Antennas Propagation, 1986, 34(3), pp. 311-319.
[20]Lee C, Lee J H. An efficient technique for eigenspace-based adaptive interference cancellation. IEEE Trans on Antennas Propagation, 1998, 46(5), pp. 694-699.
[21]Chang L, Yeh C C. Effect of pointing errors on the performance of the projection beamforming. IEEE Trans on Antennas Propagation, 1993, 41(8), pp. 1045-1056.
[22]Carlson B D. Covariance matrix estimation errors and diagonal loading in adaptive arrays. IEEE Trans on Aerospace Electronics System, 1988, 24(4), pp. 397-401.
[23]Guerci J R. Theory and application of covariance matrix taper for robust adaptive beamforming. IEEE Trans on Signal Processing, 1999, 47(4), pp. 977-985.
[24]Li J, Stoica P, Wang Zhisong. Doubly constrained robust capon beamforming. IEEE Trans on Signal Processing, 2004, 52(9), pp. 2407-2423.
[25]王安义,保铮,张林让.阵列估计误差和扰动误差引起方向图畸变的校正.西安电子科技大学学报. 1999, 26(5), pp. 614-618.
[26]程春悦,吕英华.一种稳健快速的波束形成算法.重庆邮电学院学报. 2005, 17(3), pp. 260-263.
[27]Shahram S A B, Luo ZhiQuan, Kon Max Wong. Robust adaptive beamforming for general-rank signal model. IEEE Trans on Signal Processing, 2003, 51(9), pp. 2257-2269.
[28]郭庆华,廖桂生.一种稳健的自适应波束形成器.电子与信息学报. 2004, 26(1), pp. 146-150.
[29]Lee C C, Lee J H. Eigenspace-based adaptive array-beamforming with robust capabilities. IEEE Trans on Antennas Propagation, 1997, 45(2), pp. 1711-1716.
[30]许志勇,保铮,廖桂生.一种非均匀邻接子阵结构及其部分自适应处理性能分析.电子学报. 1997, 25(9), pp. 20-24.
[31]Krim H, Viberg M. Two decades of array signal processing research. IEEE Trans on Signal Processing, 1996, 13(4), pp. 67-94.
[32]罗景青,保铮.雷达阵列信号处理技术的新进展(一).现代雷达. 1993, 15(2), pp. 110-120.
[33]孙超,李斌.加权子空间拟和算法-理论与应用.西北工业大学出版社. 1994年.
[34]Burg J P. Maximum entropy spectral analysis. Proc. of the 37-th meeting of the Annual Int. SEG Meeting, Oklahoma City, 1967.
[35]Capon J. High resolution frequency-wavenumber spectrum analysis. Proc. IEEE 1969, 57(4), pp. 1408-1418.
[36]于宏毅,保铮.循环平稳信号波达方向估计的算法和性能研究.西安电子科技大学博士论文. 1999年.
[37]Pisarrenko V F. The retrieval of harmonic from a covariance function. J. R. Astr. Soci, 1973, 33, pp. 347-366.
[38]Schmidt R O. Multiple emitter location and signal parameter estimation. IEEE Trans on Antennas Propagation, 1986, 34(3), pp. 276-280.
[39]Schmidt R O. Multiple emitter location and signal parameter estimation. In proc. RADC Spectrum Estimation Workshop, 1979, pp. 243-258.
[40]Paulraj A, Roy R, Kailath T. A Subspace Rotational Approach to Signal Parameter Estimation. Proc. IEEE, 1986, 7, pp. 1044-1045.
[41]Roy R, Paulraj A, Kailath T. ESPRIT-A subspace rotation approach to estimation ofparameters of cisoids in noise. IEEE Trans on Acoust, Speech, Signal Processing, 1986, 34(10), pp. 1340-1342.
[42]Swindlehurst A L, Ottersten B. Multiple invariance ESPRIT. IEEE Transactions on Signal Processing, 1992, 40(4), pp. 867-881.
[43]Haardt M, Nossek J A. Unitary ESPRIT - how to obtain increased estimation accuracy with a reduced computational burden. IEEE Trans on Signal Processing, 1995, 43(5), pp. 1232-1242.
[44]Tayem N, Kwon H M. Conjugate ESPRIT (C-ESPRIT). IEEE Trans on Antennas Propagation, 2004, 52(10), pp. 2618-2624.
[45]Roy R, Kailath T. ESPRIT-Estimation of signal parameters via rotational invariance techniques. IEEE Trans on Acoust, Speech, Signal Processing, 1989, 37(7), pp. 984-995.
[46]Viberg M, Otterstem B. Sensor array processing based on subspace fitting. IEEE Trans on Signal Processing, 1991, 39(5), pp. 1110-1121.
[47]Otterstem B, Viberg M, Kailath T. Analysis of subspace fitting and ML techniques for parameter estimation from sensor array data. IEEE Trans on Signal Processing, 1992, 40(3), pp. 590-600.
[48]Stoica P, Nehorai A. MUSIC, maximum likelihood, and Cramer-Rao bound. IEEE Trans on Acoust, Speech, Signal Processing, 1989, 37(5), pp. 720-741.
[49]Zoltowski M D, Kautz G M, Silverstein S D. Beamspace root-MUSIC. IEEE Trans on Signal Processing, 1993, 41(1), pp. 344-364.
[50]Xu G, Silverstein S D, Roy R H. Beamspace ESPRIT. IEEE Trans on Signal Processing, 1994, 42(1), pp. 349-356.
[51]Yeh C C. Simple computation of projection matrix for bearing estimations. Proc. Inst. Elect. Eng. F, 1987, 134(2), pp. 146-150.
[52]Yeh C C. Projection approach to bearing estimation, IEEE Trans on Acoust, Speech, Signal Processing, 1986, 34(5), pp. 1347-1349.
[53]Stoica P, S?derstr?m T. Statistical analysis of a subspace method for bearing estimation without eigen decomposition. Proc. Inst. Elect. Eng. F, 1986, 139(4), pp. 301-305.
[54]Marcos S, Benidir M. On a high resolution array processing method non based on the eigen analysis approach. Proc. IEEE Int. Conf. Acoust, Speech, Signal Processing, Albuquerque, NM, 1990, 4, pp. 2955–2958.
[55]Marcos S, Marsal A, Benidir M. The propagator method for source bearing Estimation. Signal Processing, 1995, 42(2), pp. 121-138.
[56]Eriksson A, Stoica P, S?derstr?m T. On-line subspace algorithms for tracking moving sources. IEEE Trans on Signal Processing, 1994, 42(9), pp. 2319-2330.
[57]Xin J, Sano A. Computationally efficient subspace-based method for direction of arrival estimation without eigen decomposition. IEEE Trans on Signal processing, 2004, 52(4), pp. 876-893.
[58]Swindlehurst A, Kailath T. Azimuth/elevation direction finding using regular array geometries. IEEE Trans on Signal Processing, 2005, 53(10), pp. 3651-3660.
[59]Van der Veen A J, Ober P, Deprettere E. Azimuth and elevation computation in high resolution DOA estimation. IEEE Trans on Signal Processing, 1992, 40(7), pp. 1828-1832.
[60]Lemma A N, Van der Veen A J, Deprettere E. Analysis of ESPRIT based joint angle-frequency estimation. Proc. IEEE ICASSP, 2000, pp. 3053-3056.
[61]Lemma A N, Van der Veen A J, Deprettere E. Analysis of joint angle-frequency estimation using ESPRIT. IEEE Trans on Signal Processing, 2003, 51(5), pp. 1264-1283.
[62]Yimin Zhang, Weifeng Mu, Amin M G. Subspace analysis of spatial Time-Frequency distributions matrices. IEEE Trans on Signal Processing, 2001, 49(4), pp. 747-758.
[63]Moeness G, Amin. Polarimetric Time-Frequency MUSIC in coherent signal environment. IEEE signal processing letters, 2003.
[64]Mathews C P. Eigen-structure techniques for 2-D angle estimation with uniform circular arrays. IEEE Trans on Signal Processing, 1994, 42(9), pp. 2395-2401.
[65]Mathews C P. Performance analysis of the UCA-ESPRIT algorithm for circular ring arrays. IEEE Trans on Signal Processing, 1994, 42(9), pp. 2535-2540.
[66]Yin Q, Newcomb R. Estimation of 2-D angles-of-arrival for cohere signals. In proc. ICASSP, Nanjing, China, 1989, pp. 541-544.
[67]Yeh C C, Lee J H, Chen Y M. Estimation two dimensional angles of arrival in coherent source enviroment. IEEE Trans on ASSP, 1989, 37(1), pp. 153-155.
[68]Kuduvall C R, Rangayyan R M. An algorithm for direct computation of 2-D linear prediction coefficients. IEEE Trans on Signal Processing, 1993, 42(2), pp. 996-999.
[69]Michael D. Zoltowski D, Cherian P. Real-time frequency and 2-D angle estimation with sub-nyquist spatio-temporal sampling. IEEE Trans on signal processing, 1994, 42(10), pp. 2781-2794.
[70]Michael D, Zoltowski D, Mathews C P. imation two dimensional angles of arrival in coherent source enviroment. IEEE Trans on Acoust, Speech, Signal Processing,1989, 37(1), pp. 153-155.
[71]Michael D, Zoltowski D, Cherian P. Real-time frequency and 2-D angle estimation with sub-nyquist spatio-sampling. Proc, ICASSP, 1993, pp. 117-120.
[72]Michael D, Zoltowski D, Kautz G M, Silverstein S D. Multifrequency beam space Root-MUSIC: an experimental evaluation. SPIE Advanced signal processing algorithms, architectures, and implementations, 1991, 1566, pp. 452-462.
[73]殷勤业,邹理和, Robert W Newcomb.一种高分辨二维信号参量估计方法.通信学报. 1991, 12(4), pp. 1-7.
[74]金梁,殷勤业.时空DOA矩阵方法.电子学报. 2000, 28(6), pp . 8-12.
[75]姚敏立,金梁,殷勤业.基于最小冗余线阵的共轭循环ESPRIT算法.电子科学学刊. 1999, 21(5), pp. 577-584.
[76]陈建峰,黄建国.阵列误差校正的综合校正补偿法.声学学报. 1998, 23(6), pp. 515-521.
[77]贾永康,保铮.时空二维信号模型下的波达方向估计方法及其性能分析.电子学报. 1997, 25(9), pp. 69-73.
[78]黄德双,保铮.前后项线性预测最小二乘方法实现高分辨空间谱估计方法研究.通信学报. 1992, 13(4), pp. 25-32.
[79]李有明.稳健的超分辨阵列信号处理及快速算法研究.西安电子科技大学博士论文. 1994年.
[80]张群飞,黄建国,保铮.用子空间旋转不变法同时估计水下多目标的距离和方位.声学学报. 1999, 24(4), pp. 400-406.
[81]廖桂生,保铮,王波.基于高阶累积量的盲高分辨DOA估计及其性能分析.通信学报. 1996, 17(4), pp. 9-14.
[82]王布宏,王永良,陈辉.一种新的相干信源DOA估计算法:加权空间平滑协方差矩阵的Toeplitz矩阵拟和.电子学报. 2003, 31(9), pp. 1394-1397.
[83]王布宏,王永良,陈辉.相干信源波达方向估计的广义最大似然算法.电子与信息学报. 2004, 26(2), pp. 225-232.
[84]陈辉,王永良.幂迭代子空间追踪算法.二十一世纪我国雷达发展研讨会论文集. 2000. 9, pp. 186-190.
[85]王永良,陈辉,万山虎.一种有效的超分辨空间谱估计方法虚拟阵列变换法.中国科学. 1999, 26(6), pp. 518-524.
[86]淦华东.自适应子空间估计及其在目标方位跟踪中的应用.西北工业大学硕士学位论文. 2005.
[87]Golub G H, Loan C F V. Matrix Computation. Baltimore, MD: Johns Hopkins Univ. Press, 1989.
[88]Moonen M, Dooren P V, Vandewalle J. Parallel and adaptive resolution direction finding. Proc. SPIE Advanced Algorithms and Architectures for Signal Processing. 1992, 1770, pp. 219-230.
[89]Moonen M, Dooren P V, Vandewalle J. Updating singular value decompositions: a parallel implementation. Proc. SPIE Advanced Algorithms and Architectures for Signal Processing. San Diego, 1989, 1152, pp. 80-91.
[90]Moonen M, Dooren P V, Vandewalle J. A singular value decomposition updating algorithm for subspace tracking. SIAM J. Matrix and Appl., 1992, 13(4), pp. 1015-1038.
[91]Moonen M, Dooren P V, Vandewalle J. A systolic array for SVD updating. SIAM J. Matrix and Appl., 1993, 14(2), pp. 353-371.
[92]Fuhrmann D R. An algorithm for subspace computation with applications in signal processing. SIAM J. Matrix and Appl., 1988, 9, pp. 213-220.
[93]Ammann L P. Robust singular value decompositions-A new approach to projection pursuit. J. Amer. Stat. Assoc., 1993, 88, pp. 505-514.
[94]Dowling E M, Ammann L P, Shamsunder S. A TQR-iteration based adaptive SVD for real time angle and frequency tracking. IEEE Trans on Signal Processing. 1994, 42(4), pp. 914-926.
[95]Chan T F. Alternative to the SVD: rank revealing QR factorization. Proc. SPIE Advanced Algorithms and Architectures for Signal Processing. San Diego, 1986, 696, pp. 31-37.
[96]Chan T F. Rank revealing QR factorization, Linear Algebra Application. 1987, 88-89, pp. 67-82.
[97]Fargues M P. Tracking moving sources using the rank-revealing QR factorization. Proceeding of the 25th Asilomar Conf. Sig. Syst, and Compt, 1991, pp.292-295.
[98]Stewart G W. A Jacobi-like algorithm for computing the Schur decomposition of non-Hermitian matrix. SIAM J. Sci Stat. Comput., 1985, 6, pp. 853-864.
[99]Stewart G W. An updating algorithm for subspace tracking, IEEE Trans on Signal Processing, 1992, 40(6), pp. l535- 1541.
[100]Adams G, Griffin M F, Stewert G W. Direction of arrival estimation using rank revealing URV decomposition. Proc. IEEE Int. Conf. Acoust., Speech Signal Processing, Toronto, Canada, 1991, pp. 1385-1388.
[101]Griffin M F, Stewert G W. Updating MUSIC and root-MUSIC with the rank revealing URV decomposition. Proceeding of 25th Asilomar Conf. Circuits. Syst. Comput., 1991, pp. 277-281.
[102]Golub G H. Some modified matrix eigenvalue problems. SIMA Rev, 1973, 15, pp. 318-334.
[103]Karasalo I. Estimating the covariance matrix by signal subspace averaging. IEEE Trans. Audio Speech Signal Processing, 1986, 34(1), pp. 8-12.
[104]Degroat R D. Noniterative subspace tracking. IEEE Trans on Signal Processing, 1992, 40(3), pp. 571-577.
[105]Bunch J R, Nielsen C P, Sorensen D C. Rank-one modification of the symmetric eigenproblem. Numer. Math., 1978, 31, pp. 31-38.
[106]Yu K B. Recursive updating the eigenvalue decomposition of a covariance matrix. IEEE Trans on Signal Processing, 1991, 39(5), pp. 1136-1145.
[107]Champagne B, Liu Q G. A new family of EVD tracking algorithms using Gives rotations. Proc. IEEE Int. Conf. Acoust., Speech Signal Processing, Atlanta, GA, 1996, 5, pp. 2539-2542.
[108]Owsley N L. Adaptive data orthogonalization. Proc. Conf. ICASSP-78, 1978, pp. 109-112.
[109]Thompson P A. An adaptive spectral analysis technique for unbiased frequency estimation in the presence of white noise. Proc. 13th Asilomar Conf. circuits. Syst. Comput., Pacific Grove, 1980, pp. 529-533.
[110]Yang J F, Keveh M. Adaptive eigensubspace algorithms for direction or frequency estimation and tracking. IEEE Trans on Acoust., Speech Signal Processing. 1988, 36(2), pp. 241-251.
[111]Sarkar T K, Dianat S A, Chen H. Adaptive spectral estimation by the conjugate gradient method. IEEE Trans on Acoust., Speech Signal Processing. 1986, 34, pp. 272-284.
[112]Fu W, Dowling E M. Conjugate gradient eigenstructure tracking for adaptive spectral estimation. IEEE Trans on Signal Processing. 1995, 43(5), pp. 1151-1160.
[113]Yang B. Projection approximation subspace tracking. IEEE Trans on Signal Processing, 1995, 43(1), pp. 95-107.
[114]Yang B. An extension of PASTd algorithm to both rank and subspace tracking. IEEE Signal Processing Letters, 1995, 2(9), pp. 179-182.
[115]Abed-Meraim K. Fast Orthonormal PAST algorithm. IEEE Signal Processing Letters, 2000, 7(3), pp. 60-62.
[116]Badeau R. Fast approximated power iteration subspace tracking. IEEE Trans on Signal Processing, 2005, 53(8), pp. 2931-2941.
[117]Chan Shing-Chow. A robust past algorithm for subspace tracking in impulsivenoise. IEEE Trans on Signal Processing, 2006, 54(1), pp. 105-116.
[118]Viberg M, Stoica P, Ottersten B. Array processing in correlated noise fields based on instrumental variables and subspace fitting. IEEE Trans on Signal Processing, 1995, 43, pp. 1187-1199.
[119]Gustafsson T. Instrumental variable subspace tracking using projection approximation. IEEE Trans on Signal Processing, 1998, 46(3), pp. 669-681.
[120] Soderstrom T, Stoica P. System Identification. London, U.K.:Prentice-Hall, 1999.
[121]周云钟,子空间跟踪及其在阵列信号处理中的应用,电子科技大学博士论文. 1999年。
[122]Peter Strobach. Bi-Iteration SVD subspace tracking algorithm. IEEE Trans on Signal Processing, 1997, 45(5), pp. 1222-1240.
[123]Ouyang Shan, Hua Yingbo. Bi-iteration least-square method for subspace tracking. IEEE Trans on Signal Processing, 2005, 53(8), pp. 2984-2996.
[1]Searle S R. Matrix algebra Useful for statistics. New York, John Wiley & Sons, 1982.
[2]Golub G H, Loan C F V. Matrix Computation. Baltimore, MD: Johns Hopkins Univ. Press, 1989.
[3]Schmidt R O. Multiple emitter location and signal parameter estimation. IEEE Trans on Antennas Propagation, 1986, 34(3), pp. 276-280.
[4]Paulraj A, Roy R, Kailath T. A Subspace Rotational Approach to Signal Parameter Estimation. Proc. IEEE, 1986, 7, pp. 1044-1045.
[5]Roy R, Paulraj A, Kailath T. ESPRIT-A subspace rotation approach to estimation of parameters of cisoids in noise. IEEE Trans on Acoust, Speech, Signal Processing, 1986, 34(10), pp. 1340-1342.
[6]Ottersten B, Viberg M, Kailath T. Performance analysis of the total least squares ESPRIT algorithm. IEEE Trans on Signal Processing, 1991, 39(5), pp. 1122-1135.
[1]张贤达,保铮.通信信号处理.国防工业出版社. 2002年.
[2]任超,吴嗣亮,王菊.基于空时二维结构的低旁瓣宽带波束形成.系统工程与电子技术. 2008, 30(2), pp. 414-417.
[3]Van Veen B D, Buckley K M. Beamforming: A versatile approach to spatial filtering. IEEE Trans on Acoust, Speech, Signal Processing, 1998, 5(2), pp. 4-24.
[4]Jian L, Stoica P, Zhisong W. On robust capon beamforming and diagonal loading. IEEE Trans on Signal Processing, 2003, 51(7), pp. 1702-1715.
[5]游鸿,黄建国,徐贵民.基于MVDR的阵列信号波束域预处理算法.系统工程与电子技术. 2008, 30(1), pp. 64-67.
[6]苏保伟,王永良,周良柱.基于LCMV线性约束的自适应方向图控制.电子与信息学报. 2008, 30(3), pp. 282-285.
[7]Griffiths L J, Jim C W. An alternative approach to linearly constrained adaptive beamforming. IEEE Trans on Antennas Propagation, 1982, 30(1), pp. 24-27.
[8]Yeh C C. Coherent interference suppression by an antenna array of arbitrary geometry. IEEE Trans on Antennas Propagation, 1989, 37(10), pp. 1317-1322.
[9]Shahbazpanahi S, Gershman A B, Luo Z Q. Robust adaptive beamforming for general-rank signal models. IEEE Trans on Signal Processing, 2003, 51(9), pp. 2257-2269.
[10]Lee T S. Coherent interference suppression with complementally transformed adaptive beamformer. IEEE Trans on Antennas Propagation, 1998, 46(5), pp. 609-617.
[11]Capon J. High resolution frequency-wavenumber spectrum analysis. Proc. IEEE 1969, 57(4), pp. 1408-1418.
[12]Lacoss R T. Data adaptive spectral analysis methods. Geophys, 1971, 36(4), pp. 661-675.
[13]Godara L G. Error analysis of the optimal antenna array processors. IEEE Trans onAerospace and Electronic System, 1986, 22(3), pp. 395-409.
[14]Seligson C D. Comments on high resolution frequency-wavenumber spectrum anlysis. Proc. IEEE, 1970, 58, pp. 947-949.
[15]Wax M, Anu Y. Performance analysis of the minimum variance beamforming. IEEE Trans on Signal Processing, 1996, 44(4), pp. 928-937.
[16]Viberg M, Swindlehurst A L. Analysis of the combined effects of finite samples and model errors on array processing performance. IEEE Trans on Signal Processing, 1994, 42(11), pp. 3073-3083.
[17]G. H. Golub and C. F. V. Loan, Matrix computation. Baltimore, MD: Johns Hopkins Univ. Press, 1989.
[18]Melvin W L. A STAP overview. IEEE Trans on Aerospace and Electronic System, 2004, 19(1), pp. 19-35.
[19]张贤达.矩阵分析与应用.清华大学出版社. 2004年.
[1]Clark M P, Scharf L L. Two-dimension Modal Analysis Based on Maximum Likelihood. IEEE Trans on Signal Processing, 1994, 42(6), pp. 1443-1451.
[2]Rao D V B, Kung S Y. A State-space Approach for the 2-D Harmonic Retrieval Problem. Proceedings of ICASSP, Adelaide, Australia, 1984, pp. 4101-4104.
[3]Hua Y. Estimating Two-dimension Frequencies by Matrix Enhancement and Matrix Pencil. IEEE Trans on Signal Processing, 1992, 40(9), pp. 2267-2280.
[4]Vanpoucke F, Moone M, Berthoumieu Y. An Efficient Subspace Algorithm for 2-D Harmonic Retrieval. Proceedings of ICASSP, Adelaide, Australia, 1994, pp. 461-464.
[5]谢民,付佗,高梅国.均匀圆形阵列的联合二维角度-频率估计.北京理工大学学报. 2007, 27(7), pp. 630-635.
[6]Rouquette S, Najim M. Estimation of Frequencies and Damping Factors by Two-dimensional ESPRIT type Methods. IEEE Trans on Signal Processing, 2001, 49(1), pp. 237-245.
[7]张贤达.矩阵分析与应用.清华大学出版社. 2004年.
[8]Weiss A J, Friedlander B. Array processing using joint diagonalization. Signal Processing, 1996, 87(1), pp. 1-12.
[9]Cardoso J F, Souloumiac A. Blind beamforming for non-Gaussian signals. IEE Proceedings-F, 1993, 140(6), pp. 362-370.
[10]Cardoso J F, Souloumiac A. Jacobi angles for simultaneous diagonalization. SIAM J. Matrix Anal. Appl, 1996, 17(1), pp. 161-164.
[11]Belouchrani A, Abed-Meraim K, Cardoso J F. A blind source separation technique using second-order statistics. IEEE Trans on Signal Processing, 1997, 45(2), pp. 1136-1155.
[12]Yeredor A. Non-orthogonal Joint Diagonalization in the Least-Squares Sense with Application in Blind Source Separation. IEEE Trans on Signal Processing, 2002, 50(7), pp. 1545-1553.
[13]Zhou Yi, Feng Dazheng, Liu Jianqiang. A Novel Algorithm for Two-dimensionalFrequency Estimation. Signal Processing, 2007, 87(1), pp. 1-12.
[14]Sidiropoulos N D, Giannakis G B, Bro R. Parallel Factor Analysis in Sensor Array Processing. IEEE Trans on Signal Processing, 2000, 48(8), pp. 2377-2388.
[15]J.J.Hopfield. Neural networks and physical systems with emergent collective computational abilities. Pro.Natl.Acad.Sci.USA, 1982, 79, pp. 2554-2558.
[16]J. P. LaSalle. The Stability of Dynamical Systems. Philadelphia, PA: SIAM Press, 1976.
[17]Ziehe A, Laskov P. A Fast Algorithm for Joint Diagnolization with Non-orthogonal Transformation and its Application to Blind Signal Separation. J Mach. Learn. Res., 2004, 5, pp. 777-800.
[1]Krim H, Viberg M. Two decades of array signal processing research. IEEE Trans on Signal Processing, 1996, 13(4), pp. 67-94.
[2] Veen B D, Buckley K M. Beamforming: a versatile approach to spatial filtering. IEEE Audio Speech Signal Processing, 1988, 5(2) , pp. 1-24.
[3]Kay S M, Marple S L. Spectrum analysis– a modern perspective. Proc. of IEEE, 1981, pp. 11.
[4]Burg J P. Maximum entropy spectral analysis. Proc. of the 37-th meeting of the Annual Int. SEG Meeting, Oklahoma City, OK, 1967.
[5]Capon J. High-resolution frequency-wavenumber spectrum analysis. Proc. IEEE, 1969, 57(8) , pp. 1408-1418.
[6]Schmidt R O. Multiple emitter location and signal parameter estimation. IEEE Trans on Antennas Propagation, 1986, 34(3) , pp. 276-280.
[7]Stoica P, Nehorai A. MUSIC, maximum likelihood, and Cramer-Rao bound. IEEE Trans on Audio Speech Signal Processing, 1989, 37(7) , pp. 720-741.
[8]Viberg M, Ottersten B, Kailath T. Detection and estimation in sensor arrays using weighted subspace fitting. IEEE Trans on Signal Processing, 1991, 39(11), pp. 2436-2449.
[9]Paulraj A, Roy R, Kailath T. A Subspace Rotational Approach to Signal Parameter Estimation. Proc. IEEE, 1986, 7, pp. 1044-1045.
[10]Roy R, Paulraj A, Kailath T. ESPRIT-A subspace rotation approach to estimation of parameters of cisoids in noise. IEEE Trans on Acoust, Speech, Signal Processing,1986, 34(10), pp. 1340-1342.
[11]Stoica P, Nehorai A. Performance comparison of subspace rotation and MUSIC methods for direction estimation. IEEE Trans on Signal Processing, 1991, 39(2), pp. 446-453.
[12]Rao B D, Hari K V S. Performance analysis of ESPRIT and TAM in determining the direction of arrival of plane waves in noise. IEEE Trans on Acoust, Speech, Signal Processing, 1989, 40(12), pp. 1990-1995.
[13]Roy R, Kailath T. ESPRIT-Estimation of signal parameters via rotational invariance techniques. IEEE Trans on Acoust, Speech, Signal Processing, 1989, 37(7), pp. 984-995.
[14]Ottersten B, Viberg M, Kailath T. Performance analysis of the total least squares ESPRIT algorithm. IEEE Trans on Signal Processing, 1991, 39(5), pp. 1122-1135.
[15]Steinhardt A O. Harmonic retrieval of analog signals. IEEE Trans on Acoust, Speech, Signal Processing, 1988, 36(1), pp. 134-136.
[16]Furhmann D, Liu B. Rotational search methods for adaptive Pisarenko harmonic retrieval. IEEE Trans on Acoust, Speech, Signal Processing, 1986, 34(12), pp. 1550-1565.
[17]Reddy V, Egardt B, Kailath T. A least-squares-types algorithm for adaptive implementation of Pisarenko’s harmonic retrieval method. IEEE Trans on Acoust, Speech, Signal Processing, 1982, 30(6), pp. 399-405.
[18]Micka O J, Weiss A J. Estimating frequencies of exponentials in noise using joint diagonalization. IEEE Transactions on Signal Processing, 1999, 47(2), pp. 341-348.
[19]Zhou Y, Feng D Z, Liu J Q. A novel algorithm for two-dimensional frequency estimation. Signal Processing, 2007, 87(1), pp. 1-12.
[20]Yeredor A. Non-orthogonal Joint Diagonalization in the Least-Squares Sense with Application in Blind Source Separation. IEEE Trans on Signal Processing, 2002, 50(7), pp. 1545-1553.
[21]Vollgraf R, Obermayer K. Quadratic optimization for simultaneous matrix diagonalization. IEEE Trans on Signal Processing, 2006, 54(9), pp. 3270-3278.
[22]Vanpoucke F, Moonen M, Berthoumieu Y. An efficient subspace algorithm for 2-D harmonic retrieval. Proc. IEEE ICASSP, Adelaide, Australia, 1994, pp. 461-464.
[23]Feng Da-Zheng, Zhang Xian-Da, Bao-Zheng. An efficient multistage decomposition approach for independent components. Signal processing, 2003, 83(1), pp. 181-197.
[24]Feng Dazheng, Zhang Xianda, Bao Zheng. A neural network learning for adaptivelyextracting cross-correlation features between two high-dimension data streams. IEEE Trans on Neural Networks, 2004, 15(6), pp. 1541-1553.
[25]Ziehe A, Laskov P, Nolte G. A fast algorithm for joint diagnolization with non-orthogonal transformation and its application to blind signal separation. J Mach. Learn. Res, 2004, 5(7), pp. 777-800.
[1]殷勤业,邹理和, Newcomb R.一种高分辨二维信号参数估计方法-波达方向矩阵.通信学报. 1991, 12(4), pp. 1-7.
[2] Challa R N, Shamsunder S. Higher-order subspace based algorithms for passive localization of near-field sources. Proc. 29-th Asilomar Conf. Signals System Computer, Pacific Grove. CA. 1995, pp. 777-781.
[3]吴湘霖,俞卞章,宋祖勋.基于空时扩展虚拟阵列的高分辨多参数联合估计.计算机工程与应用. 2005, (1), pp. 19-22.
[4]Schmidt R O. Multiple emitter location and signal parameter estimation . IEEE Trans on Antennas Propagation , 1986, 34(3), pp. 276-280.
[5]Roy R, Paulraj A, Kailath T. ESPRIT-A subspace rotation approach to estimation of parameters of cisoids in noise. IEEE Trans on Acoust., Speech, Signal Processing, 1986, 34(10), pp. 1340-1342.
[6]王永良,陈辉,彭应宁,万群.空间谱估计理论与算法.清华大学出版社. 2004年.
[7]Mathew G , Reddy V U, Dasgupta S. Adaptive estimation of eigensubspace. IEEE Trans on Signal Processing, 1995, 43(2) , pp. 401-411.
[8]Zhang JianKang. An adaptive quasi-Newton algorithm for eigensubspace estimation. IEEE Trans on Signal Processing, 2000, 48(12) , pp. 3328-3333.
[9]马长征,吴伟陵.自适应拟牛顿子空间跟踪算法分析.电子与信息学报. 2003, 25(2), pp. 200-205.
[10]Sastry K C. Adaptive algorithms for estimating the complete covariance eigenstructure. ICASSP, 1986, 34(10) , pp. 1401-1403.
[11]粟欣,李立萍,陈天麒.信号时变DOA的递推跟踪.中国电子学会电子对抗分会第十一届学术年会论文集(上):pp. 177-182.
[12]周云钟,陈天麒,黄香馥.高分辨率信号DOA估计的跟踪算法.电子科学学刊. 1997, 19(3), pp. 300-305.
[13]Xu G, Cho Y, Kailath T. Application of fast subspace decomposition to signalprocessing and communication problems. IEEE Trans on Signal Processing, 1994, 42(6) , pp. 1453-1461.
[14]李有明.稳健的超分辨阵列信号处理及快速算法研究.西安电子科技大学博士学位论文. 1994.
[15]Chan T F. Alternative to the SVD: rank revealing QR factorization. Proc. SPIE Advanced Algorithms and Architectures for Signal Processing. San Diego, 1986, 696, pp. 31-37.
[16]Chan T F. Rank revealing QR factorization. Linear Algebra Application, 1987, 88-89, pp. 67-82.
[17]Fargues M P. Tracking moving sources using the rank-revealing QR factorization. Proceeding of the 25th Asilomar Conf. Sig. Syst, and Compt, 1991, pp.292-295.
[18]Stewart G W. A Jacobi-like algorithm for computing the Schur decomposition of non-Hermitian matrix. SIAM J. Sci Stat. Comput., 1985, 6, pp. 853-864.
[19]Stewart G W. An updating algorithm for subspace tracking. IEEE Trans on Signal Processing, 1992, 40(6), pp. l535- 1541.
[20]Adams G, Griffin M F, Stewert G W. Direction of arrival estimation using rank revealing URV decomposition. Proc. IEEE Int. Conf. Acoust., Speech Signal Processing, Toronto, Canada, 1991, pp. 1385-1388.
[21]Griffin M F, Stewert G W. Updating MUSIC and root-MUSIC with the rank revealing URV decomposition. Proceeding of 25th Asilomar Conf . Circuits. Syst. Comput., 1991, pp. 277-281.
[22]陈辉,王永良.秩-1子空间跟踪算法.电子与信息学报. 2002, 24(5), pp. 626-629.
[23]Golub G H. Some modified matrix eigenvalue problems. SIMA Rev, 1973, 15, pp. 318-334.
[24]Karasalo I. Estimating the covariance matrix by signal subspace averaging. IEEE Trans on Audio Speech Signal Processing, 1986, 34(1), pp. 8-12.
[25]Degroat R D. Noniterative subspace tracking. IEEE Trans on Signal Processing, 1992, 40(3), pp. 571-577.
[26]Bunch J R, Nielsen C P, Sorensen D C. Rank-one modification of the symmetric eigenproblem. Numer. Math., 1978, 31, pp. 31-38.
[27]Yu K B. Recursive updating the eigenvalue decomppsition of a covariance matrix. IEEE Trans on Signal Processing, 1991, 39(5), pp. 1136-1145.
[28]Yang J F, Keveh M. Adaptive eigensubspace algorithm for direction or frequency estimation and tracking. IEEE Trans on Acoust Speech Signal Processing, 1988,36(2), pp. 241-251.
[29]Yang B. Projection approximation subspace tracking. IEEE Trans on Signal Processing, 1995, 43(1), pp. 95-107.
[30]Viberg M, Stoica P, Ottersten B. Array processing in correlated noise fields based on instrumental variables and subspace fitting. IEEE Trans on Signal Processing, 1995, 43, pp. 1187-1199.
[31]Stoica P, Jasson M. Estimating optimal weights for instrumental variable methods. Digital Signal Processing, 2001, 11(3), pp. 252 -268.
[32]Gustafsson T. Instrumental variable subspace tracking using projection approximation. IEEE Trans on Signal Processing, 1998, 46(3), pp. 669-681.
[33] Soderstrom T, Stoica P. System Identification. London, U.K.:Prentice-Hall, 1999.
[34]Stoica P, Jasson M. Estimating optimal weights for instrumental variable methods. Digital Signal Processing, 2001, 11(3), pp. 252-268.
[35]吴湘霖.非理想系统假设下阵列信号多信源多参数联合估计.西北工业大学博士学位论文. 2005年.
[36]Belouchrani A, Abed-Meraim K, Cardoso J F, Moulines E.A Blind Source Separation Technique Using Second-order Statistics. IEEE Trans on Signal Processing, 1997, 45(2), pp. 434-444.
[37]Roland B, Gael R, Bertrand D. Sliding window adaptive SVD algorithms. IEEE Trans on Signal Processing, 2004, 52(1), pp. 1-10.