共轭增强ESPRIT算法

提出了共轭增强ESPRIT（CA-ESPRIT）算法，利用阵列输出的延迟相关函数及其共轭形成伪阵列输出，从而得到伪协方差矩阵，对其进行特征分解，用ESPRIT算法得到波迭方向。仿真实验表明，新算法可对多于阵元数的信号进行测向，其测角精度和分辨能力优于ESPRIT算法，运行时间小于有相同阵列扩展能力的MUSIC—like算法。     

一种基于共轭矩阵的阵列扩展技术

提出了一种新的基于共轭矩阵的阵列扩展技术,该技术可以对阵列的信号子空间进行扩展,从而可以估计更多的信号波达方向(DOA)。该方法首先对接收的阵列信号进行二阶统计量预处理,利用自相关函数的性质获得导向矩阵的共轭矩阵,然后构造出一个新的伪快拍扩展矩阵,通过对该矩阵运用MUSIC-like算法可以比传统的MUSIC-like算法估计出的波达方向个数多50％,理论分析和仿真实验证明了本文结论。     

用于阵列处理的信号子空间法

介绍一种阵列处理中采用的自适应干扰对消新算法。该算法将信号子空间法与基于目标函数（输出信号干扰比的一种尺度）的自适应法结合在一起，其最大特点是：与其它信号子空间法相比的一种尺度无需阵列校准，从而具有经济性、灵活性等优点。     

基于内插阵列变换的非圆信号MUSIC算法

针对非圆信号的波达方向(DOA)估计问题,提出一种基于内插阵列变换的非圆信号MUSIC算法(VIA-NC-MUSIC算法)。利用真实阵列流型与虚拟阵列流型之间的变换矩阵,将真实协方差矩阵变换为虚拟协方差矩阵,再对虚拟协方差矩阵进行奇异值分解(SVD),利用信号子空间与噪声子空间的正交性,得出算法的空间谱函数。仿真实验表明:存在阵元位置误差的情况下,新算法通过对阵元位置校准数据进行内插阵列变换(VIA),取得与阵元位置校准的非圆信号MUSIC算法(NC-MUSIC算法)相当的估计性能,保持了高估计精度、阵列扩展能力等优点。     

基于虚拟阵列的非圆信号DOA估计

针对传统的子空间类波达方向（Direction of Arrival,DOA）估计算法只适用于入射信号个数少于天线数的局限性,利用现代通信系统中常用的非圆信号实值特性,提出了一种虚拟阵列多重信号分类法（Virtual Array Based Multiple Signal Classification,VA-MUSIC）。该方法通过对阵列输出信号进行共轭重构和合并,获得虚拟阵列来增加阵列的有效孔径。更进一步,结合空间平滑技术有效地解决了相干信号的DOA估计问题。与传统的MUSIC算法相比,新算法不仅可以增加最大可估计信源数,而且在DOA估计精度、信号源角分辨能力等方面均有明显的优势。计算机仿真验证了该算法的有效性和优越性。     

用于信号波达方向估计的共轭ESPRIT算法及其性能分析

针对阵列天线的波达方向估计问题,给出了一种新颖的ESPRIT算法--共轭ESPRIT算法(C-ESPRIT).此算法利用阵列单元输出信号的共轭信息,得到整个阵列输出的协方差矩阵,通过对此矩阵的特征分解,并构造运算矩阵,最终求得信号的波达方向.仿真实验表明,C-ESPRIT算法不仅克服了传统ESPRIT算法在阵列单元数与信源数相同时不能可靠测向的不足,并且对信号波达方向的分辨能力和测角精度也显著高于传统的ESPRIT算法,因此,C-ESPRIT算法具有更广泛的适应性和更优越的测向性能.     

基于虚拟阵列空间平滑的相干信号DOA估计

 针对相干共轭循环平稳信号的DOA估计问题,提出了一种基于虚拟阵列前后向空间平滑的方法。该方法利用特定阵元输出间共轭循环相关函数的多时延采样,构造出与真实线阵阵元具有一一对应关系的虚拟阵列,并将空间平滑技术应用于该虚拟阵列实现了相干信号DOA估计。理论分析和仿真结果均表明,与FBSS-CCMUSIC算法和CCHAM算法相比,本文方法避免了最优时延选择问题,并获得了更高的DOA估计精度,同时,对信号不同入射方向也具有较好的稳健性。此外,该方法也可推广用于具有循环平稳特性的信号。     

线性调频信号2-D到达角估计的虚拟阵元法

通过原始数据的共轭重排构造了虚拟阵列，利用实际阵列与虚拟阵列的互Wigner-Ville分布建立了新的空间时频分布，并给出了基于子空间投影和L-阵列的线性调频信号2-D到达角估计算法。理论分析和仿真实验证明了算法的有效性。     

U-ESPRIT波达估计算法及其实现

介绍了"酉-利用旋转不变技术估计信号参数(U-ESPRIT)"算法的模型,U-ESPRIT算法是基于子空间分解的谱估计算法。在"利用旋转不变技术估计信号参数(ESPRIT)"算法的基础上,通过引入观测数据的复共轭信息,经过数学变换,使阵列输出信号的协方差矩阵由复矩阵变成实对称矩阵,减小了计算量。最后介绍了U-ESPRIT算法的工程实现,并进行了实际测试,实验结果表明该方法估计精度高,实现简单。     

共轭传播算子测向算法

针对一维信号提出了共轭传播算子测向算法(COP),新算法利用了阵列输出数据的共轭,通过对阵列孔径的扩展,可对多于阵元数的信号进行测向,其分辨力和测角精度优于OPM(正交传播算子测向算法)和MUSIC 算法.分析了新算法的均方误差性能和计算复杂度,得到了均方误差的解析表达式.仿真实验验证了COP算法的优良性能,均方误差的理论值与仿真值相符.     

一种适用于小样本的迭代多重信号分类算法

当样本数不足时,由采样协方差矩阵特征分解得到的噪声子空间偏离其真实值,使得多重信号分类(MUSIC)算法目标角度(DOA)估计性能下降。为了解决这个问题,该文提出了一种迭代算法通过校正信号子空间来提高MUSIC算法性能。该方法首先利用采样协方差矩阵特征分解得到的噪声子空间粗略估计目标角度;其次基于信源的稀疏性和导向矢量的低秩特性,由上一步得到的目标角度以及其邻域角度对应的导向矢量构造一个新的信号子空间;最后通过解一个优化问题来校正信号子空间。仿真结果表明,该算法有效地提高了子空间估计精度。基于新的信号子空间实现MUSIC DOA估计可以使得性能得到改善,且在低样本数下改善尤为明显。     

Fast DOA estimation algorithm using pseudocovariance matrix

This paper proposes a new direction of arrival (DOA) estimation algorithm that can rapidly estimate the DOAs of incidence signals using a pseudocovariance matrix even under coherent interference environments. The conventional multiple signal classification (MUSIC) algorithm, which should estimate a covariance matrix, cannot perform a DOA estimation until it acquires the covariance matrix. In addition, the MUSIC algorithm cannot be used under rapidly changing or correlated interference environments. In contrast, the proposed algorithm can obtain a bearing response after acquiring the pseudocovariance matrix based on a single snapshot. Signal incidence angles can then be accurately estimated by combining the bearing response and the location of pattern s. Accordingly, the proposed algorithm can rapidly estimate the DOAs of signals even when they are correlated.     

基于时间反转的MIMO雷达实值MUSIC算法

针对时间反转(TR)多输入多输出(MIMO)雷达多重信号分类(MUSIC)算法计算量庞大的问题,提出一种基于时间反转的MIMO雷达实值MUSIC算法。首先,通过采用降维思想对TR MIMO回波信号进行降维处理,来减少计算量;然后,为将协方差矩阵转化到实数域,构造酉变换矩阵进行实值变换;最后,分解出实值协方差矩阵的噪声子空间,构造谱函数估计信号波达角。相对于传统的MUSIC算法,该算法借助实值变换剔除了复数运算,极大地降低了计算量,而且不需要空间平滑降低阵列孔径就具有解相干的能力。仿真结果证实了算法的正确性。     

TST-MUSIC for joint DOA-delay estimation

A multiple signal classification (MUSIC)-based approach known as the time-space-time MUSIC (TST-MUSIC) is proposed to jointly estimate the directions of arrival (DOAs) and the propagation delays of a wireless multiray channel. The MUSIC algorithm for the DOA estimation is referred to as the spatial-MUSIC (S-MUSIC) algorithm. On the other hand, the temporal-MUSIC (T-MUSIC), which estimates the propagation delays, is introduced as well. Making use of the space-time characteristics of the multiray channel, the proposed algorithm-in a tree structure-combines the techniques of temporal filtering and of spatial beamforming with three one-dimensional (1-D) MUSIC algorithms, i.e., one S-MUSIC and two T-MUSIC algorithms. The incoming rays are thus grouped, isolated, and estimated. At the same time, the pairing of the estimated DOAs and delays is automatically determined. Furthermore, the proposed approach can resolve the incoming rays with very close DOAs or delays, and the number of antennas required by the TST-MUSIC algorithm can be made less than that of the incoming rays     

FSF MUSIC for Joint DOA and Frequency Estimation and Its Performance Analysis

In this paper, we present a tree-structured frequency-space-frequency (FSF) multiple signal classification (MUSIC)-based algorithm for joint estimation of the directions of arrival (DOAs) and frequencies in wireless communication systems. The proposed approach is a novel twist of parameter estimation and filtering processes, in which two one-dimensional (1-D) frequency (F)- and one 1-D space (S)-MUSIC algorithms are employed-in a tree structure-to estimate the DOAs and frequencies, respectively. In between every other MUSIC algorithm, a temporal filtering process or a spatial beamforming process, implemented by a set of complementary projection matrices, is incorporated to partition the incoming rays to enhance the estimation accuracy, so that the incoming rays can be well resolved even with very close DOAs or frequencies, using the 1-D MUSIC algorithms. Also, with such a tree-structured estimation scheme, the estimated DOAs and frequencies are automatically paired without extra computational overhead. Furthermore, some statistical analyses of the undesired residue signals propagating between the 1-D MUSIC algorithms and the mean square errors of the parameter estimates are derived to provide further insights into the proposed approach. Simulations show that the new approach can provide comparable performance, but with reduced complexity compared with previous works, and that there is a close match between the derived analytic expressions and simulation results     

Method of solving ambiguity for sparse array via power estimation based on MUSIC algorithm

Sparse linear arrays provide better performance than the filled linear arrays in terms of direction estimation and resolution with reduced size and low cost. However, they are subject to manifold ambiguity. A method based on the Multiple Signal Classification (MUSIC) algorithm to solve the manifold ambiguity of uncorrelated sources for sparse array is proposed in this paper. The method consists of two steps. The first step is to obtain all the directions of arrivals (DOAs), including true and spurious DOAs, using traditional MUSIC. The second step is to estimate the power values of the all DOAs by substituting all the DOAs to a cost function. The well-known Davidson Fletcher Powell (DFP) and Broyden Fletcher Goldfarb Shanno (BFGS) algorithms are used to estimate the power values. The power values of spurious DOAs are very small or tend to zero compared with the values of the true DOAs. The true DOAs are then discriminated easily from the spurious DOAs with the power values. Simulation results demonstrate the effectiveness and the feasibility of the method.     

Novel high-resolution DOA estimation using subspace projection method

The performance of multiple signal classification(MUSIC) algorithm with regard to solving closely spaced direction of arrivals(DOAs) depends strongly upon the signal-to-noise ratio(SNR) and snapshots.In order to solve this problem,a method by reconstructing the spatial spectrum function with both noise subspace and signal subspace is presented in this paper.The key idea is to apply the full information contained in covariance matrix and change the projection weights of steering vector on the noise and signal subspace by their revised eigenvalues,respectively.Comparing with the MUSIC algorithm,it does not increase any computational complexity either,and remarkably,it has the advantages of simultaneously reducing noise and keeping the high-resolution ability under low SNR and small sample sized scenarios.Simulation and experiment results are included to demonstrate the superior performance of the proposed algorithm.     

Direction-of-arrival and polarization estimation with uniform circular arrays in the presence of mutual coupling

A modified Root-MUSIC algorithm is proposed to estimate the directions-of-arrival (DOAs) and the polarization of plane waves, which impinge at a fixed elevation angle, using a diversely polarized uniform circular array (UCA). Special attention is devoted to the presence of mutual coupling effects in antenna arrays. By describing the electromagnetic characteristics on the basis of a phase-mode expansion for the open-circuit voltages and the mutual coupling matrix, the modified Root-multiple signal classification (MUSIC) algorithm takes mutual coupling effects into account by a limited number of phase modes. Finally, the efficiency of the new algorithm is verified based on synthetic antenna data.     

A Tree Structure One-Dimensional Based Algorithm for Estimating the Two-Dimensional Direction of Arrivals and Its Performance Analysis

A tree structure algorithm using one-dimensional (1D) multiple signal classification (MUSIC) algorithm is proposed to estimate the two-dimensional direction of arrivals (2D DOAs) of coherent signals impinging on a uniform rectangular array. The basic idea of the proposed algorithm is to successively apply several times of the 1D spatial smoothing MUSIC algorithm, in tree structure, to estimate the azimuth and the elevation angles independently. To optimally separate the receive signal, constrained spatial beamformers with adjustable null width are exploited in conjunction with the 1D spatial smoothing MUSIC algorithm to decompose the received signal into several signals each lead by its own 2D DOA. Performance analysis is provided to investigate the estimation bias caused by the residue signal propagating in the tree structure.     

Estimation of directions of arrival of multipath signals in CDMAsystems

An algorithm is proposed for estimating the directions of arrival (DOAs) of multipath signals received from multiple users on the uplink of a code-division multiple-access system. The correlation matrices of the received signal before and after code-matched filtering are used to provide unique estimates even when the number of required DOAs exceeds the number of antenna array elements. This scenario is well known to cause conventional direction-finding algorithms (such as MUSIC) to fail. Both intersymbol interference (ISI) and multiple-access interference (MAI) are modeled exactly, and so the algorithm performs much better than those which model ISI and MAI as Gaussian noise