非均匀噪声背景下混合信号DOA估计算法

针对非均匀噪声背景下非相关信源与相干信源并存时波达方向(DOA)估计问题，提出了基于迭代最小二乘和空间差分平滑的混合信号DOA估计算法。首先，该算法利用迭代最小二乘方法得到噪声协方差矩阵估计，然后对数据协方差矩阵进行“去噪”处理，利用子空间旋转不变技术实现非相关信源DOA估计；其次，基于空间差分法消除非相关信号并构造新矩阵进行前后向空间平滑，利用求根MUSIC算法估计相干信源DOA。相比于传统算法，该算法能估计更多的信源数，在低信噪比情况下DOA估计性能更优越。仿真实验结果验证了该算法的有效性。      

一种新的波达方向和多普勒频率估计算法

基于均匀圆形阵列,提出了一种同时估计空间非相干信号源方位角、仰角和多普勒频率的快速算法。该方法对均匀圆阵的输出信号进行模式空间转换,使得阵列流形具有类似于均匀线阵的形式,然后通过构造相应的数据矩阵得到传播算子的最小二乘（LS）估计,并由传播算子构造出一个特殊的低维矩阵,其特征值给出多普勒频率估计,特征向量舍有阵列流形的信息。结合模式空间阵列流形的性质,给出了一种DOA估计的总体最小二乘算法,在低信噪比条件下可提高测向精度。该方法不需要谱峰搜索和参数配对,具有运算量小的优点。计算机仿真验证了该方法的有效性。     

混合信号情况下信源数与DOA联合估计算法

针对非相关信源与相干信源共存情况,提出了一种基于矩阵重构的信源数与波达方向（direction of arrival,DOA）联合估计算法.该算法首先利用特征值的二阶统计量（second order statistic of eigenvalues,SORTE）法和子空间旋转不变技术（estimated signal parameter via rotational invariance techniques,ESPRIT）实现非相关信源数与DOA估计;然后基于空间差分法消除非相关信号并构造新矩阵,利用构造矩阵进行前向空间平滑,实现对相干信源解相干;最后利用SORTE法检测相干信源数,结合求根多重信号分类（multiple signal classification,MUSIC）算法估计相干信源DOA.与传统的差分平滑方法相比,该算法在可估计信源数与低信噪比情况下DOA估计性能等方面优于传统算法.数值仿真实验结果验证了该算法的有效性.     

基于均匀线阵的混合源波达方向估计方法

文中提出了一种基于均匀线阵的混合源波达方向DOA估计的改进方法。该方法首先利用传统MUSIC方法估计出非相干信号源的DOA,然后接收数据协方差矩阵进行差分消除不相关源和噪声的影响,对其进行特殊的空间平滑去相干,从而利用重建的数据协方差矩阵估计相干源的DOA。此方法的特点是分别估计不相关信号和相干信号的DOA。优点是算法在估计出多于阵元数信号的前提下具有较高的DOA估计精度和稳健性。仿真结果表明此方法的估计性能优于空间差分平滑算法。     

Two-dimensional direction of arrival estimation in the presence of uncorrelated and coherent signals

In this paper, a novel two-dimensional direction of arrival (2-D DOA) estimation method is proposed based on a new array configuration when uncorrelated and coherent signals coexist. The DOAs of uncorrelated signals are estimated using the non-zero eigenvalues and corresponding eigenvectors of the DOA matrix (DOAM) combined with our proposed criterion. Meanwhile, we can form a new matrix without the information of uncorrelated signals. Then the coherent signals are resolved with the redefined DOAM that is constructed by the smoothed matrices of the new matrix. Simulation results demonstrate the effectiveness and efficiency of the proposed method. Other arrays that contain multiple identical central-symmetric subarrays (e.g. uniform rectangular arrays) can also be applied with our method.     

基于空时自适应处理的低空风切变风速估计方法

针对机载气象雷达在探测低空风切变时,有用信号会淹没在强杂波背景中的问题,该文提出一种基于空时自适应处理(STAP)的低空风切变风速估计方法。该方法首先利用空时插值原理校正机载前视阵地杂波的距离依赖性,获得多个独立同分布(IID)样本后估计地杂波协方差矩阵,然后构造适用于分布式低空风切变目标的空时自适应处理器,在自适应抑制地杂波的同时积累低空风切变信号,最终实现风场速度的精确估计。仿真结果表明,在高杂噪比、低信噪比的情况下,该方法可有效地自适应抑制地杂波并精确地估计风场速度。     

ICA-based direction-of-arrival estimation of uncorrelated and coherent signals with uniform linear array

A novel approach for direction-of-arrival (DOA) estimation of uncorrelated and coherent signals with uniform linear array is proposed in this paper. First, the mixing matrix, which contains all azimuth information of signal sources, is estimated by independent component analysis. Afterward, several parameter equations are established upon the new mixing matrix. Finally, all DOAs of coherent and uncorrelated signals are estimated by solving these equations. Compared with traditional methods, the proposed method has higher angle resolution and estimation accuracy. Moreover, the signal number resolved by our approach can exceed the number of array elements. Simulation results have demonstrated the efficiency of the proposed method.     

针对相干信号DOA估计的改进MUSIC算法

针对相干信号波达方向(Direction of Arrival, DOA)估计,提出了一种改进的多重信号分类(Multiple Signal Classification, MUSIC)算法。首先,利用信号协方差矩阵的两个最大特征值所对应的特征向量,构造出两个Toeplitz矩阵;然后,利用前后向空间平滑思想得到这两个矩阵的无偏估计并求和;最后,利用MUSIC算法从中估计出相干信号DOA。和已有方法相比,该方法无需损失阵列孔径且具有更优的DOA估计性能。     

一种新颖的DOA估计算法

子空间类波达方向(Direction Of Arrival, DOA)估计算法的关键在于得到高质量的信号子空间估计。该文利用矩阵伪逆的双正交性,针对源信号不相关而其本身是色信号的情况,给出了一种新颖的DOA估计算法,它不需要知道噪声统计特性。该算法利用一组空时相关矩阵的结构化信息,能稳健而精确地估计出信号子空间,从而得到DOA的精确估计。仿真实验证实了所给算法的有效性。     

宽带协方差矩阵的多字典联合稀疏表示DOA估计

为了直接处理相干宽带信号和提高其波达方向估计的分辨率,提出一种基于宽带协方差矩阵的多字典联合稀疏分解估计方法。首先,利用多个频率点处的过完备基对其协方差矩阵进行稀疏表示,然后形成多个字典的多测量矢量稀疏表示模型,最后通过多字典稀疏表示系数的联合稀疏约束以求解稀疏反问题的形式实现宽带信号的波达方向估计。对于均匀线阵结构,多字典协方差矩阵稀疏表示系数的联合稀疏性使其不再受空域采样条件的限制,既可通过增大阵元间距提高分辨率,而又无空域混叠现象。通过对噪声功率的预估计抑制噪声,提高了波达方向估计的稳健性。另外,该方法与信号协方差矩阵的秩无关,对相干信号和不相干信号都适用。仿真实验验证了该方法的有效性。      

A novel approach for DOA estimation in unknown correlated noise fields

The key of the subspace-based Direction Of Arrival （DOA） estimation lies in the estimation of signal subspace with high quality. In the case of uncorrelated signals while the signals are temporally correlated, a novel approach for the estimation of DOA in unknown correlated noise fields is proposed in this paper. The approach is based on the biorthogonality between a matrix and its Moore-Penrose pseudo inverse, and made no assumption on the spatial covariance matrix of the noise. The approach exploits the structural information of a set of spatio-temporal correlation matrices, and it can give a robust and precise estimation of signal subspace, so a precise estimation of DOA is obtained. Its performances are confirmed by computer simulation results.     

一种基于波束空间的非相关信号源DOA估计方法

针对非相关信号源的到达角估计,本文基于波束空间信号输出协方差矩阵的对角阵特性,建立了信号到达角估计的最小化代价函数.采用DFT波束形成矩阵,借助于矩阵分解变换,保持了阵列流形的Vandermonde结构,并由此将代价函数转换为二次函数形式,得到信号源到达角的精确估计.仿真结果表明该算法具有更快的运算速度,更低的分辨力门限及高的估计精度.     

Positive-definite Toeplitz completion in DOA estimation fornonuniform linear antenna arrays. I. Fully augmentable arrays

This paper considers the problem of direction-of arrival (DOA) estimation for multiple uncorrelated plane waves incident on so-called “fully augmentable” sparse linear arrays. In situations where a decision is made on the number of existing signal sources (m) prior to the estimation stage, we investigate the conditions under which DOA estimation accuracy is effective (in the maximum-likelihood sense). In the case where m is less than the number of antenna sensors (M), a new approach called “MUSIC-maximum-entropy equalization” is proposed to improve DOA estimation performance in the “preasymptotic region” of finite sample size (N) and signal-to-noise ratio. A full-sized positive definite (p.d.) Toeplitz matrix is constructed from the M×M direct data covariance matrix, and then, alternating projections are applied to find a p.d. Toeplitz matrix with m-variate signal eigensubspace (“signal subspace truncations”). When m⩾M, Cramer-Rao bound analysis suggests that the minimal useful sample size N is rather large, even for arbitrarily strong signals. It is demonstrated that the well-known direct augmentation approach (DAA) cannot approach the accuracy of the corresponding Cramer-Rao bound, even asymptotically (as N→∞) and, therefore, needs to be improved. We present a new estimation method whereby signal subspace truncation of the DAA augmented matrix is used for initialization and is followed by a local maximum-likelihood optimization routine. The accuracy of this method is demonstrated to be asymptotically optimal for the various superior scenarios (m⩾M) presented     

基于时空结构的阵列信号三维参数同时估计方法

提出了一种针对空间非相关窄带信号源的中心频率、方位角和俯仰角三维参数的同时估计方法。该方法通过对均匀双平行线阵时域采样构造虚拟阵元，然后结合空域采样数据构造时空DOA矩阵，对DOA矩阵进行特征分解，利用分解得到的特征值和特征向量估计出信号源的三维参数。该方法不需要进行谱峰搜索，运算量小，能实现频率、方位角和俯仰角的同时估计与自动配对，具有较高的分辨率，且能估计出比阵元数多的信号源的参数，给出的计算机仿真结果证明了该方法的有效性。     

基于转换矩阵的GPS相干干扰抑制技术

本文首先采用空间平滑技术消除有用的GPS信号与相干干扰信号之间的相关性,然后构造干扰转换矩阵,从而抑制干扰信号,保留有用信号和噪声。最后根据最大输出信噪比原则,获得最佳波束形成器。仿真分析表明该方法可以有效提高输出信号的信号干扰加噪声比,且对信号的DOA估计不敏感。     

DOA Estimation for Mixed Signals in the Presence of Mutual Coupling

In this paper, a new direction of arrival (DOA) estimation method is proposed in the presence of unknown mutual coupling. The impinging signals are a mixture of signals in different correlation degrees, which are uncorrelated, partially correlated or coherent with each other. The method proceeds in three steps. The noncoherent (uncorrelated or partially correlated) signals are firstly estimated with unknown mutual coupling. Then these estimates are utilized to get the mutual coupling coefficients. Finally, by eliminating the contributions of the noncoherent signals and compensating the mutual coupling effect, only coherent signals remain to be estimated. This method is simple but effective and does not need iterative processing. Simulation results demonstrate the effectiveness and performance of the proposed method.      

基于四阶循环累积量的DOA估计方法

提出了一种新的波达方向估计算法,该算法构造了一个“谱相关DOA矩阵”,并基于此矩阵进行DOA估计。理论分析表明,通过谱相关矩阵的特征值分解,就可以得到源信号的DOA,该方法不需要进行谱峰搜索,同时利用循环平稳特性,可以有效抑制平稳噪声和滤除与信号循环频率不同的干扰信号,从而大大提高了算法的检测能力。另外还给出了相干信号DOA解决方法。     

一种波达方向、频率联合估计快速算法

首先提出了基于PM(propagator method)方法的波达方向(DOA)、频率联合估计快速算法，给出了PM算子的一个估计，由PM算子构造出一特殊的低维矩阵，其特征值给出频率的估计，进而由估计的频率和相应的特征矢量得到DOA的估计。该算法具有参数自动配对，计算量小的优点，易于在工程应用中实时处理。计算机仿真结果证实了算法的有效性。     

A subspace method for direction of arrival estimation ofuncorrelated emitter signals

A novel eigenstructure-based method for direction estimation is presented. The method assumes that the emitter signals are uncorrelated. Ideas from subspace and covariance matching methods are combined to yield a noniterative estimation algorithm when a uniform linear array is employed. The large sample performance of the estimator is analyzed. It is shown that the asymptotic variance of the direction estimates coincides with the relevant Cramer-Rao lower bound (CRB). A compact expression for the CRB is derived for the ease when it is known that the signals are uncorrelated, and it is lower than the CRB that is usually used in the array processing literature (assuming no particular structure for the signal covariance matrix). The difference between the two CRBs can be large in difficult scenarios. This implies that in such scenarios, the proposed methods has significantly better performance than existing subspace methods such as, for example, WSF, MUSIC, and ESPRIT. Numerical examples are provided to illustrate the obtained results     

基于稀疏恢复的循环平稳信号DOA估计

基于信号稀疏恢复的思想,提出了一种新的循环非相关平稳信号DOA估计算法.首先,对阵列的二阶循环互相关矩阵矢量化,并将感兴趣的空间划分成若干段以构造过完备的方向矩阵,从而得到基于Khatri-Rao积的稀疏模型;其次,利用凸优化技术对稀疏模型进行优化求解,并根据恢复得到的稀疏信号中非零元素的位置估计出高精度的DOA值.与传统的循环互相关算法比较,本文算法具有更高的DOA估计精度,同时也适用于信号个数多于阵元个数的场合.理论分析和仿真实验结果都验证了算法的有效性.