2-D unitary matrix pencil method for efficient direction of arrival estimation

In this study, we extended the one-dimensional (1-D) unitary matrix pencil method (UMP) [N. Yilmazer, J. Koh, T.K. Sarkar, Utilization of a unitary transform for efficient computation in the matrix pencil method to find the direction of arrival, IEEE Trans. Antennas Propagat. 54 (1) (2006) 175–181] to two-dimensional case, where 2-D matrix pencil (MP) method are used to find the 2-D poles corresponding to the direction of arrival (DOA), azimuth and elevation angles, of the far field sources impinging on antenna arrays. This technique uses MP method to compute the DOA of the signals using a very efficient computational procedure in which the complexity of the computation can be reduced significantly by using a unitary matrix transformation. This method applies the technique directly to the data without forming a covariance matrix. Using real computations through the unitary transformation for the 2-D matrix pencil method leads to a very efficient computational methodology for real time implementation on a DSP chip. The numerical simulation results are provided to see the performance of the method.     

3D modified unitary matrix pencil method with automatic grouping of unknown parameters of far field signals

In this paper, we propose a 3-dimensional modified unitary matrix pencil (3D MUMP) method for simultaneous estimation of azimuth and elevation angles along with the frequencies of multiple plane wave signals. The unitary transformation is utilized in the 3-dimensional matrix pencil (3D MP) method in order to reduce the computational complexity since it very efficiently converts complex computations into real ones. The proposed method can be considered as an extension to the existing 2-dimensional unitary matrix pencil (2D UMP) method with the following advantages. First, the computationally expensive grouping algorithm is avoided by exploiting the similar eigen-structure property of the matrices (in 3D UMP method) whose eigenvalues yield the required 3D poles. Furthermore, the possibility of wrong groupings is also reduced significantly. Some simulation results are presented at the end to compare the performance of the proposed method with the existing methods.     

高精度低复杂度的无线定位新方法

针对高精度的无线定位算法普遍存在运算量较大的问题,提出了一种二维波束空间矩阵束算法进行波达时间（TOA）和波达方向（DOA）联合估计,能够以较低的复杂度准确定位目标。该算法先通过离散傅里叶变换（DFT）波束形成矩阵将阵元空间的接收数据复数矩阵变换成波束空间的降维实数矩阵,使得运算量大幅度降低;再通过奇异值分解和求矩阵对的广义特征值估计视距信号TOA和DOA,从而确定目标位置。Matlab仿真实验结果证明,这种定位方法的均方根误差最好达到0.4m,运算量不到阵元空间对应算法的1/4,是一种高精度低复杂度的无线定位方法,尤其适用于资源有限的特殊环境（如战场、地震灾区、偏远山区等）中的无线网络定位。     

Matrix pencil method for simultaneously estimating azimuth and elevation angles of arrival along with the frequency of the incoming signals

In this paper we describe a method for simultaneously estimating the direction of arrival (DOA) of the signal along with its unknown frequency. In a typical DOA estimation problem it is often assumed that all the signals are arriving at the antenna array at the same frequency which is assumed to be known. The antenna elements in the array are then placed half wavelength apart at the frequency of operation. However, in practice seldom all the signals arrive at the antenna array at a single pre-specified frequency, but at different frequencies. The question then is what to do when there are signals at multiple frequencies, which are unknown. This paper presents an extension of the matrix pencil method to simultaneously estimate the DOA along with the operating frequency of each of the signals. This novel approach involves approximating the voltages that are induced in a three-dimensional antenna array, by a sum of complex exponentials by jointly estimating the direction of arrival (both azimuth and elevation angles) along with the carrier frequencies of multiple far-field sources impinging on the array by using the three-dimensional matrix pencil method. The matrix pencil method is a direct data domain method for approximating a function by a sum of complex exponentials in the presence of noise. The variances of the estimates computed by the matrix pencil method are quite close to the Cramer–Rao bound. Finally, we illustrate how to carry out the broadband DOA estimation procedure using realistic antenna elements located in a conformal array. Some numerical examples are presented to illustrate the applicability of this methodology in the presence of noise. It is shown that the variance decreases as the SNR increases. The Cramer–Rao bound for the estimators are also provided to illustrate the accuracy and the computational efficiency of this new methodology.     

单电磁矢量传感器DOA和极化的同时估计

针对单个电磁矢量传感器，提出基于四阶累积量的极化域虚拟ESPRIT二维波达方向(DOA)和极化参数同时估计算法。该方法借助累积量操作提取信号在极化域的旋转不变结构，并利用虚拟ESPRIT方法同时估计信号的二维DOA和极化参数，它可以分辨多个极化域可分的非高斯信号。给出的仿真结果验证了算法的可行性和有效性。     

面阵中基于传播算子的二维DOA估计算法

考虑面阵中的二维波达方向（DOA）估计问题,提出了一种基于传播算子（PM）的二维DOA估计算法。该算法利用4个子面阵接收数据的互相关矩阵构造新的数据矩阵,利用线性运算代替特征分解得到旋转不变关系矩阵,由该关系矩阵得到方位角与俯仰角的参数估计。所提算法的波达方向估计性能优于传统的面阵二维ESPRIT算法,且降低了计算复杂度,提高了阵列接收数据的利用率和算法的抗干扰能力。算法可以实现二维角度的自动配对。仿真实验证明了算法的有效性。     

基于SVD的二维子空间拟合DOA估计

改进传统子空间拟合波达方向(DOA)估计方法,以快拍数据矩阵的奇异值分解代替接收数据协方差矩阵的特征值分解,用奇异值和奇异值矢量进行信源数估计,避免协方差矩阵估计,减少运算量和矩阵估计误差。根据已有子空间拟合的一维修正变化投影(MVP)算法原理,推导出二维MVP算法实现步骤,对基于均匀圆阵的接收信号进行二维DOA估计。     

一种强干扰环境下的离格稀疏贝叶斯DOA估计方法

强干扰的环境下,基于传感器阵列的波达方向(Direction of arrival,DOA)估计是阵列信号处理中的重要问题。虽然对于网格点目标现有方法的DOA估计精度较高,但对于离格点目标现有方法的DOA估计性能会严重下降。本文提出一种离格情况下的DOA估计方法,首先设计一种鲁棒的正交零陷矩阵滤波法(Robust orthogonal matrix filter with nulling,ROMFN),它结合了正交零陷滤波法(Orthogonal matrix filter with nulling,OMFN)和最差性能下的鲁棒自适应波束形成,在对离格点目标达到滤波效果的同时只需设计较少的网格点。此外,新的矩阵滤波法保留了高斯白噪声的特性,避免了噪声白化的预处理过程。其次基于离格点稀疏贝叶斯推断(Off-grid sparse Bayesian inference,OGSBI)和ROMFN,形成一种强干扰下DOA估计的新方法。与现有方法相比,仿真结果表明该方法可以在不同的网格间距、不同的信噪比和干噪比下获得更高的估计精度。     

互耦效应下多组相干源的波达方位估计算法

对于实际环境中存在的多径现象和阵元间的互耦效应,提出一种互耦效应下针对相干源的波达方向估计算法。首先,通过波达方向矩阵法利用二阶矩求出互耦效应下的广义导向矢量;然后对广义导向矢量进行 子空间平滑,通过矩阵变换得到一个线性约束下的规划问题,实现相干源方位和互耦系数的级联估计。该算法只需利用二阶矩求得广义导向矢量,相比常规的四阶累积量方法,减少了计算量;本文算法在解互耦和解相干过程中都没有损失阵列孔径,极大提高了阵元利用效率。计算机仿真结果验证了该算法的有效性。     

基于辅助阵元的二维波达方向估计算法

;针对任意平面阵列,提出了一种基于辅助阵元的二维波这方向估计算法.首先利用附加的一个辅助阵元及信号的空、时域信息,构造时空旋转矩阵实现对仰角的分离估计,再利用得到的仰角信息通过一维搜索获取方位角.与传统基于子空间的二维波达方向估计算法相比,该方法不需要进行二维谱峰搜索与参数配对,对阵元的幅相误差具有较强的鲁棒性,并具有...     

Joint DOA and channel estimation with data detection based on 2D unitary ESPRIT in massive MIMO systems

We propose a novel method for joint two-dimensional (2D) direction-of-arrival (DOA) and channel estimation with data detection for uniform rectangular arrays (URAs) for the massive multiple-input multiple-output (MIMO) systems. The conventional DOA estimation algorithms usually assume that the channel impulse responses are known exactly. However, the large number of antennas in a massive MIMO system can lead to a challenge in estimating accurate corresponding channel impulse responses. In contrast, a joint DOA and channel estimation scheme is proposed, which first estimates the channel impulse responses for the links between the transmitters and antenna elements using training sequences. After that, the DOAs of the waves are estimated based on a unitary ESPRIT algorithm using previous channel impulse response estimates instead of accurate channel impulse responses and then, the enhanced channel impulse response estimates can be obtained. The proposed estimator enjoys closed-form expressions, and thus it bypasses the search and pairing processes. In addition, a low-complexity approach toward data detection is presented by reducing the dimension of the inversion matrix in massive MIMO systems. Different cases for the proposed method are analyzed by changing the number of antennas. Experimental results demonstrate the validity of the proposed method.     

基于分数阶傅里叶变换的宽带LFM相干信号二维DOA估计

在分数阶傅里叶(FRF)域从离散角度推导了二维DOA估计数学模型,并在此模型基础上提出基于分数阶Fourier变换的二维相干信号DOA估计新算法.该方法利用分数阶傅里叶变换良好的能量聚集性,在分数阶傅里叶(FRF)域构造前后向空间平滑DOA矩阵.通过对DOA矩阵进行特征值分解,估计信号子空间和噪声子空间,由信号子空间的特征值和特征向量得到宽带LFM相干信号的二维到达角,避免了二维谱峰搜索和交叉项,也无需参数配对.理论推导与实验仿真验证了算法的有效性.     

一种改进的卫星空时DOA矩阵算法

提出一种改进的基于信号空时特征结构的高分辨二维波达方向(DOA)估计方法——时空DOA矩阵方法。该方法在保持原时空DOA矩阵方法无需二维谱峰搜索和参数配对等优点的基础上,通过构造X轴上的平移不变子阵列,产生2个DOA矩阵,利用这2个DOA矩阵的角度兼并曲线的差异,解决了原时空DOA矩阵方法的角度兼并问题。由于2个子阵可以重复利用阵元,该方法基本无冗余阵元和孔径损失。仿真结果证明了该方法的有效性。     

一种改进的铅笔画的生成方法

给出了一种改进的根据真实2D图像自动生成相应的非真实感铅笔画的方法。首先将彩色图像进行霓虹处理,再进行反相计算和灰度化,就可以产生铅笔画中的轮廓效果。其次,为了更好地产生铅笔画的光照效果及其局部走势纹理,采用线积分卷积(LIC)的方法来生成类似的效果,并且用适当的图像分割方法来获取进行LIC处理的有意义的区域。实验结果表明,本文的方法与以往的方法相比,能生成具有不同风格的效果,并且生成的速度更快。     

基于FFT的子孔径MUSIC波达方向估计

针对现 有的很多波达方向估计算法涉及到数据协方差矩阵的估计及其特征分解,甚至是求逆,导致 运算复杂度高的问题,提出了基于快速傅里叶变换的子孔径MUSIC波达方向估计算法 。首先将等距线阵的接收数据矢量均匀划分为4个子矢量,然后对各个子矢量分别求FFT。将 FFT的结果相干积累,并找到最大峰值点。最后,利用子矢量FFT的结果中与最大峰值点对应 的数据构造新的降维矢量,借助MUSIC算法进行波达方向估计。该方法避免了直接接收数据 的协方差矩阵估计和特征分解,有效地降低了运算量和计算复杂度,在阵元数和快拍数都较 多的情况下优越性尤为明显。计算机仿真验证了所提方法的有效性和优越性。     

DOA矩阵类方法中角度兼并问题的分析与改进

简要介绍波达方向(DOA)矩阵法,首先分析了这类方法存在的角度兼并问题,并对一些改进算法进行了比较和分析。然后给出了一种解决方法,该方法首先判断有无兼并发生,当发生角度兼并时,分别估计无兼并信号和兼并信号的二维方向。兼并信号的估计是把DOA矩阵法和MUSIC算法相结合,利用附加的一维搜索方法得到。与DOA矩阵法相比,无角度兼并发生时计算量没有任何增加,有兼并发生时计算量会有限增加。计算机仿真结果验证了本文方法的有效性。     

Parameter estimation of the 2D‐GTD model and RCS reconstruction based on an improved 2D‐ESPRIT algorithm

The two‐dimensional estimating signal parameter via rotational invariance techniques (2D‐ESPRIT) algorithm is a classical method to estimate parameters of the two‐dimensional geometric theory of diffraction (2D‐GTD) model. While as signal‐to‐noise‐ratio (SNR) decreases, the parameter estimation performance of 2D‐ESPRIT algorithm is severely influenced. To solve this problem, a performance‐enhanced 2D‐ESPRIT algorithm is proposed in this article. The improved 2D‐ESPRIT algorithm combines the conjugate data with the original back‐scattered data and obtains a novel covariance matrix by squaring the original total covariance matrix. Simulation results indicate that the improved algorithm has a better noise robustness and a more stable parameter estimation performance than the classical ESPRIT algorithm and the classical TLS‐2D‐ESPRIT algorithm. To further validate the superiority of the improved 2D‐ESPRIT algorithm, reconstructed radar cross section (RCS) is presented in this article. Compared with the classical 2D‐ESPRIT algorithm, the proposed algorithm presents higher RCS fitting precision. Furthermore, the impacts of other factors on parameter estimation, such as matrix pencil parameters and paring parameters, are also studied in this article.     

The generalized eigendecomposition approach to the blind source separation problem

This paper proposes a novel formulation of the generalized eigendecomposition (GED) approach to blind source separation (BSS) problems. The generalized eigendecomposition algorithms consider the estimation of a pair of correlation matrices (a matrix pencil) using observed sensor signals. Each of various algorithms proposed in the literature uses a different approach to form the pencil. This study proposes a linear algebra formulation which exploits the definition of congruent matrix pencils and shows that the solution and its constraints are independent of the way the matrix pencil is computed. Also an iterative eigendecomposition algorithm, that updates separation parameters on a sample-by-sample basis, is developed. It comprises of: (1) performing standard eigendecompositions based on power and deflation techniques; (2) computing a transformation matrix using spectral factorization. Another issue discussed in this work is the influence of the length of the data segment used to estimate the pencil. The algorithm is applied to artificially mixed audio data and it is shown that the separation performance depends on the eigenvalue spread. The latter varies with the number of samples used to estimate the eigenvalues.     

A sparse direction-of-arrival estimation algorithm for MIMO radar in the presence of gain-phase errors

In this paper, the problem of direction-of-arrival (DOA) estimation for monostatic multiple-input multiple-output (MIMO) radar with gain-phase errors is addressed, by using a sparse DOA estimation algorithm with fourth-order cumulants (FOC) based error matrix estimation. Useful cumulants are designed and extracted to estimate the gain and the phase errors in the transmit array and the receive array, thus a reliable error matrix is obtained. Then the proposed algorithm reduces the gain-phase error matrix to a low dimensional one. Finally, with the updated gain-phase error matrix, the FOC-based reweighted sparse representation framework is introduced to achieve accurate DOA estimation. Thanks to the fourth-order cumulants based gain-phase error matrix estimation, and the reweighted sparse representation framework, the proposed algorithm performs well for both white and colored Gaussian noises, and provides higher angular resolution and better angle estimation performance than reduced-dimension MUSIC (RD-MUSIC), adaptive sparse representation (adaptive-SR) and ESPRIT-based algorithms. Simulation results verify the effectiveness and advantages of the proposed method.     

基于COLD阵列的联合稀疏重构信号DOA估计方法

针对窄带和宽带两种情形,提出了一种基于同点正交磁环偶极子矢量天线（Co-centered orthogonal loop and dipole,COLD）阵列的联合稀疏重构信号波达方向（Direction-of-arrival,DOA）估计方法。该方法首先构造极化-空间域协方差矩阵,并对其第一列进行稀疏表示,在此基础上利用COLD阵列可视为相互垂直的磁环阵列和偶极子阵列这一特点,采用l2-范数约束下的凸优化（l1-范数）联合稀疏重构技术实现信号DOA估计。仿真实验表明,该方法较之现有方法具有分辨力高、估计精度高等优点。