Covariance matrix based fast smoothed sparse DOA estimation with partly calibrated array

To solve the problem of direction-of-arrival (DOA) estimation for partly calibrated array, a new gain-phase error matrix estimation scheme and a smoothed sparse signal reconstruction method tailored for the complex-valued covariance matrix are proposed. In the proposed method, DOA estimation is achieved by employing the structure of the covariance matrix for the error matrix estimation and the complex-valued gradient matrix based fast non-convexity data reconstruction. The proposed method has much faster computational speed than other sparse DOA estimation methods with partly calibrated array. In addition, simulation results show that it performs well and is independent of the errors.     

高斯白噪声下L阵二维波达方向快速估计

针对L型阵列,提出一种在高斯白噪声环境下的二维波达方向( DOA)快速估计方法。首先利用阵列结构特点构建两个互协方差矩阵,同时实现了噪声分量的有效抑制,再依据协方差矩阵的性质构造了波达方向矩阵。对该矩阵进行一次特征分解即可分别得到包含方位角和俯仰角信息的方向矢量和方向元素,实现二维DOA估计。该算法避免了传统算法的谱峰搜索或大矩阵构造及其特征分解过程,计算量小,且参数自动配对。仿真结果表明,该算法在低性噪比和少快拍下的估计精度与2 D ESPRIT算法近似,但计算复杂度大幅降低,适用于实时性高的工程应用背景。     

转弯畸变拖曳阵声呐鲁棒Capon波束形成方法

针对柔性拖曳阵转弯机动过程,在积累时间内阵形变化导致自适应波束形成性能下降的问题,该文提出一种基于时变阵形聚焦和降维处理的低复杂度鲁棒波束形成方法。首先,基于阵列Water-Pulley模型估计时变阵形,以基准阵形为参考采用预成导向方法对阵形进行聚焦,消除数据中阵形模型偏差;然后,以阵形聚焦后数据协方差矩阵的共轭梯度方向矢量构成降维矩阵,构造大孔径阵列降维鲁棒Capon波束形成器。仿真结果表明：所提方法能够提高转弯机动拖曳阵波束形成输出信号-干扰噪声比(SINR)。海试验证表明：该方法能够提高测向空间谱上目标输出信噪比,提高大孔径阵列机动状态下对弱目标检测能力,同时能区分目标左右舷。     

A NEW ARRAY STRUCTURE FOR ESTIMATING 2-D DIRECTION-OF-ARRIVAL:Y-SHAPED ARRAY

This paper presents a new array structure for estimating two-dimensional (2-D) direction-of-arrivals (DOAs). The structure is called Y-shaped array, which has 10% better accuracy potential than the newly-developed L-shaped array. A great merit is its ability to estimate 2-D DOAs whichever directions the arriving signals come from, compared with L-shaped array whose performance depends on DOAs. Simulation results are given to demonstrate the performance of the new array.     

DOA estimation of noncircular signals for coprime linear array via locally reduced-dimensional Capon

We investigate the issue of direction of arrival (DOA) estimation of noncircular signals for coprime linear array (CLA). The noncircular property enhances the degree of freedom and improves angle estimation performance, but it leads to a more complex angle ambiguity problem. To eliminate ambiguity, we theoretically prove that the actual DOAs of noncircular signals can be uniquely estimated by finding the coincide results from the two decomposed subarrays based on the coprimeness. We propose a locally reduced-dimensional (RD) Capon algorithm for DOA estimation of noncircular signals for CLA. The RD processing is used in the proposed algorithm to avoid two dimensional (2D) spectral peak search, and coprimeness is employed to avoid the global spectral peak search. The proposed algorithm requires one-dimensional locally spectral peak search, and it has very low computational complexity. Furthermore, the proposed algorithm needs no prior knowledge of the number of sources. We also derive the Crámer-Rao bound of DOA estimation of noncircular signals in CLA. Numerical simulation results demonstrate the effectiveness and superiority of the algorithm.     

Robust spatial time-frequency distribution matrix estimation with application to direction-of-arrival estimation

We consider the problem of estimating spatial time-frequency distribution (STFD) matrices in the presence of impulsive noise. STFD matrices are widely used in sensor array processing for direction-of-arrival estimation and blind source separation of non-stationary sources. Conventional methods fail when the noise is non-Gaussian or impulsive. We propose robust techniques for STFD estimation which are based on pre-processing, robust position based estimation and robust covariance based estimation. The proposed methods are compared in terms of direction-of-arrival estimation performance.     

基于PM的波达方向、频率的快速估计方法

提出了一种基于传播算子的波达方向、频率快速联合估计方法,该算法不需要估计阵列接收信号的高维空、时协方差矩阵及其相应的奇异值或特征分解,具有更低的运算复杂度,算法能适合于非均匀阵列结构且估计出的空时参数能自动配对.最后计算机仿真证实了方法的有效性.     

一种新的信号子空间宽带测向算法

宽带信号测向是信号处理研究的难点课题之一,经典的旋转信号子空间（RSS）变换算法需要知道和真实来波方向接近的方向初始值。不合适的方向初始值将会降低估计器的收敛能力,引起估计偏差,从而使算法的估计精度受预估计的影响很大。本文提出的新算法首先推导出不同频段不同方向角的方向矢量之间的关系,然后再利用信号子空间、噪声子空间和阵列流型之间的关系构造一个特殊矩阵,最后通过求条件数来判断来波方向。所以新算法不需要构造聚焦矩阵,不需预知初始值,从而提高了算法的稳健性,降低了运算量,且新算法同时利用了信号子空间和噪声子空间,减少了噪声子空间扰动对算法精度的影响。计算机仿真验证了新算法的有效性。     

基于共形天线阵的免搜索来波方向估计算法研究

运算量大,受阵列拓扑结构限制是传统基于子空间分解的来波方向(DOA)估计算法的主要问题,这些问题大大限制了其在实际工程中的应用。针对共形天线阵的特殊性,结合实际的共形阵,研究了几种可以应用于任意阵列结构的免搜索DOA估计算法,并比较了这些算法在共形阵上的性能。理论分析和数值仿真表明:在一定条件下这些算法均可在共形阵上取得较优的估计性能。     

基于对数螺旋阵的波达方向估计

系统渡达方向(DOA)的估计能力受天线阵的几何结构影响很大.提出了一种新的二维天线阵列流形--对数螺旋阵,该阵列流形具有天线单元数少、天线孔径大、分辨力高的特点.通过粒子群优化方法(PSO)确定了无栅瓣的阵结构参数,计算了此阵的天线方向图并与矩形阵、圆环阵做了比较,利用MUSIC方法进行了二维空间谱估计,仿真结果表明:利用该对数螺旋多天线阵可以实现高分辨二维DOA估计.     

Nested planar array: configuration design,optimal array and DOA estimation

Nested array enables to enhance localisation resolution and achieve under-determined direction of arrival (DOA) estimation. In this paper, we improve the traditional nested planar array to achieve more degrees of freedom (DOFs) and better angle estimation performance. The closed-form expressions for sensor positions of the improved array are given and the optimal array configuration for largest available DOFs is derived. Meanwhile, a computationally efficient DOA estimation algorithm is proposed. Specifically, we utilise two dimensional Discrete Fourier Transform (2D DFT) method to obtain the coarse DOA estimates; Subsequently, we achieve the fine DOA estimates by 2D spatial smoothing multiple signals classification (SS-MUSIC) algorithm. The proposed algorithm enjoys the same estimation accuracy as SS-MUSIC algorithm but with lower complexity because the coarse DOA estimates enable to shrink the range of spectral search. In addition, estimation of the number of signals is not required by 2D DFT method. Extensive simulation results testify the effectiveness of the proposed algorithm.     

声矢量阵远程定向技术(一)--新的协方差矩阵生成方法

MUSIC等子空间类DOA(direction of arrival)估计算法,以其较高的分辨能力和相对较小的计算量而颇受关注．但如果将其简单引用到声矢量阵中,将矢量传感器(AVS)的振速信息仅仅作为独立的阵元来处理,则并没有充分利用AVS 中声压和振速的相干性,以及由此带来的抗各向同性噪声能力．基于AVS中声压和振速的相干性原理,提出了一种新的声矢量阵协方差矩阵牛成方法．该方法完全利用了AVS的平均声强抗噪原理,具有较强的抗各向同性噪声能力,可将子空间类DOA估计方法与声矢量阵技术更为有效地结合起来,实现远程高分辩DOA估计．理论分析和基于湖试数据的仿真实验证明了新方法的有效性．     

有效扩展阵列孔径的伪快拍构造

许多通信信号具有循环平稳特性。针对广义信号模型和窄带信号模型，利用前后向循环相关函数分别构造出一种新的伪快拍，由此获得了一个孔径扩展一倍的虚拟阵列。对这两种伪快拍应用传统的MUSIC算法时，可分别比SC-SSF和cy-clic-MUSIC算法检测多一倍的信号和获得更高的估计分辨率。仿真结果证明了该结论。     

Computationally efficient direction-of-arrival estimation of non-circular signal based on subspace rotation technique

In order to solve the problem that the high computational burden of the multiple signal classification algorithm of non-circular signal (NC-MUSIC) in direction-of-arrival (DOA) estimation,a novel computationally efficient DOA estimation algorithm based on subspace rotation technique was proposed.Firstly,the partitioning of noise subspace matrix and the subspace rotation technique (SRT) were used to construct a new reduced-dimension noise subspace.Then,the two-dimensional peak searching was converted to the one-dimensional peak searching on the basis of the separation of variables and the orthogonality between the new reduced-dimension noise subspace and the space spanned by the columns of the extended manifold matrix.The proposed algorithm can enhance the computational efficiency by means of the conversion of the two-dimensional peak searching into the one-dimensional peak searching and the removal of redundant computations.Theoretical analysis and simulation results show that the proposed algorithm can reduce the computational complexity to less than 5% as compared to NC-MUSIC algorithm on the premise of ensuring the accuracy of DOA estimation.Especially,the efficiency advantage of the proposed algorithm is more obvious in scenarios where the large numbers of sensors are required.     

极化分集下的共形阵列波达方向估计

提出了一种基于双馈源共形阵列的极化分集信号盲极化波达方向(Direction of Arrival,DOA)估计方法.通过投影和旋转变换,分析并给出了双馈源共形阵列下正交极化分集信号的等效导向矢量,将信号空间划分为两个正交极化信号子空间,实现了正交极化信号的盲极化分离.在此基础上给出了基于多重信号分类(Multiple Signal Classification,MUSIC)的极化信号的DOA估计算法.该算法无需估计极化参数,能够实现任意共形阵列对正交极化分集信号的DOA估计,最大估计信号数目能够超过阵元个数.仿真实验验证了该算法具有很好的估计性能.     

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。     

改进分离式电磁矢量阵列的两维波达方向估计

针对现有分离式电磁矢量传感器阵列的两维波达方向(Direction of Arrival, DOA)估计存在的两个问题:其一,当入射信号在时域上不具有旋转不变性时,现有算法失效;其二,无法实现阵列的两维孔径扩展导致两维DOA估计精度较差,提出了一种改进的分离式电磁矢量传感器阵列结构．首先利用所提阵列的空域旋转不变性代替时域旋转不变性得到其中一维方向余弦的高精度估计;其次结合矢量叉乘法与相位干涉法得到另一维的方向余弦高精度估计;最后对两维方向余弦进行三角操作得到目标的两维DOA估计．本文算法摆脱了对入射信号形式的依赖,实现了阵列的两维孔径扩展,使得两维DOA估计精度大大提高．仿真结果证明了本文算法的有效性．     

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。     

均匀圆阵中盲联合角度和时延估计方法

提出了均匀圆阵中一种盲联合角度和时延的估计方法（BJADE）。对阵列天线输出的过采样样本的Fourier变换后信号进行分析，表明此信号具有三线性模型特征。该算法先利用三线性交替最小二乘（TALS）算法估计出方向矩阵和时延矩阵，然后利用时延矩阵的Vandermonde特征、基于最小二乘方法进行时延估计；利用方向矩阵结构特点和基于最小二乘方法进行二维角度估计。仿真结果说明BJADE方法具有较好角度和时延估计性能，且在过载情况下仍有较好的性能。该方法无须信道冲激响应，是一种盲、健壮的联合处理方法。     

基于四阶累积量的共形阵列波达方向估计算法

由于共形天线阵列流形的多极化特性（Polarization Diversity）,共形阵列天线的信源方位估计需要与信源的极化状态联合进行。分析总结共形阵列天线波达方向（DOA）估计特点的基础上,针对窄带远场非高斯独立信源,提出了一种共形阵列天线盲极化DOA估计算法。该算法利用四阶累积量对阵列口径的扩展性,结合旋转不变子空间（ESPRIT）算法,在信源极化状态未知条件下实现了共形阵列天线的高分辨DOA估计。所提算法的方位估计不需要天线单元方向图以及信源极化状态的任何信息,适用于多种常用共形载体（锥面、柱面以及球面共形载体）,具有较为广泛的应用环境。以柱面共形阵列天线DOA估计为例,详细推导了算法机理,给出了算法步骤,计算机Monte Carlo仿真实验验证了所提算法的有效性。