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。     

基于非圆信号的二维测向算法

提出了一种基于窄带非圆信号的二维测向算法,利用非圆信号的椭圆协方差矩阵非零特性将阵列有效阵元数和孔径同时扩展,提高多信号检测的能力。进一步通过利用L阵的几何性质和两子阵间的互相关来滤除噪声,提高角度估计精度,并采用快速算法获得信号方位角和俯仰角。最后计算机仿真结果验证了该方法由于利用非圆信号特性比传统测向算法的估计性能更高。     

一种基于循环互相关的非相干源信号方向估计方法

给出了基于循环互相关运算的阵列信号模型,根据该信号模型,运用MUSIC算法得到了对空间非相干源信号方向进行估计的有效方法—CCC-MUSIC算法,定性的分析及仿真实验均说明了所给方法具有较好的抑制噪声和选择信号的能力。该算法将为利用天线阵列提取具有循环平稳特性的源信号方向这一问题提供较好的实现方法。     

Computationally efficient DOA estimation for coprime linear array: a successive signal subspace fitting algorithm

ABSTRACT

In this paper, direction of arrival (DOA) estimation of multiple signals with coprime array is investigated and signal subspace fitting (SSF) method is linked to the coprime array, which achieves a better DOA estimation performance than the traditional uniform array. While the SSF method requires expensive computational cost in the case of multiple signals due to the multidimensional global angular searching, we propose a successive SSF (S-SSF) algorithm from a computationally efficient perspective. In the proposed algorithm, we employ rotational invariance and coprime property to obtain the initial estimates. Then, via a successive scheme, we transform the traditional multidimensional global angular searching problem into one-dimensional partial angular searching one. Consequently, the computational complexity has been significantly reduced. Specifically, the proposed S-SSF algorithm can obtain almost the same DOA estimation performance as SSF but with remarkably lower complexity. Finally, Cramer-Rao Bound (CRB) is provided and numerical simulations demonstrate the effectiveness of the proposed algorithm.     

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。     

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。     

修正MUSIC算法对相关信号源的DOA估计性能

本文介绍了用修正ＭＵＳＩＣ算法提高相关信号源ＤＯＡ估计性能的方法。理论分析表明,采用该方法后,可将相关信号源的相关系数平均降低为原来的６３％,因而可改善ＭＵＳＩＣ算法对相关信号源的ＤＯＡ估计性能。且该方法不影响对非相关信号源ＤＯＡ的估计,计算量也无明显增加。计算机仿真实验结果验证了上述理论分析的正确性。     

A modified high-order cumulant music algorithm for signal DOA estimation

This paper gives a MUSIC signal DOA estimation algorithm based on the modified high-order cumulant matrix which is constructed by the recieved data and their conjugate rearrangements. When the snapshot number is limited, this algorithm can improve the signal DOA estimation performances obviously, and its computational complexity scarcely increases. Finally, some simulation results to verify the theoretical analyses are presented.     

基于信号子空间的多维参数联合估计

旋转不变的子空间中特征值的估计与关联是多维谱估计和阵列信号处理的重要问题.采用平行的交叉偶极子均匀线阵,利用各子阵列输出信号的相关矩阵构造多个旋转不变的子空间,并用其中一个子空间的信号子空间实现了频率、二维到达角和极化的自动配对.整个计算过程只需进行一次矩阵特征值的分解,就可完成多维参量估计与配对,且不存在参量兼并现象,理论分析与计算仿真结果证明了方法的有效性.     

非圆信号快速高精度二维测向算法

针对窄带非圆信号,提出了一种基于共轭梯度迭代搜索的快速高精度二维来波方向估计算法。利用非圆信号椭圆协方差矩阵非零特性,将阵列接收数据矩阵共轭重排,增加了阵列自由度,扩展了阵列有效孔径,提高了多信号检测能力,改善了阵列测向性能。对扩展后的阵列接收数据利用共轭梯度迭代搜索方式求得信号子空间,避免了多次求解样本协方差矩阵及其特征分解过程,显著降低了运算复杂度。仿真实验验证了所提算法的有效性和实用性。     

基于数据共轭重排的修正二维MUSIC算法分析

在阐述二维MUSIC算法基本原理的基础上,通过将接收数据共轭重排的再利用、构造相关矩阵,提出一种基于正交阵列的修正二维MUSIC算法。在快拍次数有限时,此算法可以明显改善信号的波达方向估计性能,特别是相干信号波达方向(DOA)的估计性能,计算机仿真结果证明了该改进算法的有效性和可行性。     

一种不考虑信源数目的修正MUSIC算法

分析了信源数目估计误差下的MUSIC算法性能.为了解决信源数目过估计下出现虚假信号和欠估计下谱峰消失的问题,提出了一种改进算法.该算法通过对采样数据的共轭重排再利用,重新构造相关矩阵来改善测向性能.改进的算法不仅能成功运用于非相关信号源和相关信号源,还具有不用考虑信源数目的优点.计算机仿真结果证明了改进算法的正确性和有效性.     

基于空间谱估计的多干扰源分辨技术研究

为实现反辐射导弹分辨多个干扰源,根据空间谱估计原理,提出采用均匀圆形阵列天线结构对目标进行测向定位,并运用MUSIC算法估计其到达角,实现空域分辨。建立了基于均匀圆阵的二维DOA估计模型,讨论了角分辨算法在导引头中的具体应用。仿真结果证明了采用该方法能够有效分辨多个干扰源。     

基于均分阵元的空域平滑技术研究

空间谱估计是现代信号处理研究的重要课题,对于非相干信号的DOA估计,采用MUSIC就能取得好的分辨效果,对此算法进行了简要介绍.对于广泛存在的相干信号,直接采用MUSIC则存在很大的模糊性甚至失效,为了准确地进行DOA估计,必须经过预处理解相干.针对传统的空域平滑处理,提出了均分阵元的空间平滑技术,经仿真验证,新方法可行、有效,最后分析了该方法的优势和弊端.     

Computationally efficient MUSIC based DOA estimation algorithm for FMCW radar

This paper proposes low-cost yet high-accuracy direction of arrival (DOA) estimation for the automotive frequency-modulated continuous-wave (FMCW) radar. The existing subspace-based DOA estimation algorithms suffer from either high computational costs or low accuracy. We aim to solve such contradictory relation between complexity and accuracy by using randomized matrix approximation. Specifically, we apply an easily-interpretable randomized low-rank approximation to the covariance matrix (CM) and approximately compute its subspaces. That is, we first approximate CM \begin{document}${\bf{R} }\in {\mathbb{C}}^{M\times M} $\end{document} through three sketch matrices, in the form of \begin{document}$\mathbf{R}\approx \mathbf{Q}\mathbf{B}{\mathbf{Q}}^{\mathrm{H}} $\end{document}. Here the matrix \begin{document}$\mathbf{Q}\in {\mathbb{C}}^{M\times z} $\end{document} contains the orthonormal basis for the range of the sketch matrix \begin{document}$\mathbf{C}\in {\mathbb{C}}^{M\times z} $\end{document} which is extracted from \begin{document}$ \mathbf{R} $\end{document} using randomized uniform column sampling and \begin{document}$ \mathbf{B}\in {\mathbb{C}}^{z\times z} $\end{document} is a weight-matrix reducing the approximation error. Relying on such approximation, we are able to accelerate the subspace computation by the orders of the magnitude without compromising estimation accuracy. Furthermore, we drive a theoretical error bound for the suggested scheme to ensure the accuracy of the approximation. As validated by the simulation results, the DOA estimation accuracy of the proposed algorithm, efficient multiple signal classification (E-MUSIC), is high, closely tracks standard MUSIC, and outperforms the well-known algorithms with tremendously reduced time complexity. Thus, the devised method can realize high-resolution real-time target detection in the emerging multiple input and multiple output (MIMO) automotive radar systems.     

A CLOSED-FORM WIDEBAND DIRECTION-OF-ARRIVAL ESTIMATION WITH CHIRPLET-BASED ADAPTIVE SIGNAL DECOMPOSITION ALGORITHM

The Direction-Of-Arrival (DOA) estimation with Coherent Signal Subspace (CSS)is not easy to cxtend from narrowband to wideband case.Time-frequency analysis is a powerfultechnique to deal with time-variant or non-stationary signal. Its combination with CSS exploresa new field in signal processing, especially the wideband DOA estimation. The chirp function isone of the most fundamental functions in nature. Many nature events can be modeled as chirpletfunction, such as radar system or scismic exploring.Hence,the chirplet-based signal decomposi-     

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。     

一种基于非一致响应圆阵的DOA估计改进算法

由于传统波达方向(DOA)估计算法对接收天线阵列的阵元一致性要求高,即幅度和相位响应要求一致,在工程应用中受到较大限制。针对该问题,结合均匀圆阵的特点,通过引入阵元幅度和相位不一致因子对传统MUSIC算法的模型加以改进,提出了一种改进的MUSIC算法。该算法降低了阵元的一致性要求,可通过迭代求解的方法来完成对多个信源来波方向进行估计。仿真和分析结果表明,提出的算法可有效地进行多个目标的DOA估计。     

Off-grid direction-of-arrival estimation for wideband noncircular sources

Researchers have recently shown an increased interest in estimating the direction-of-arrival (DOA) of wideband noncircular sources, but existing studies have been restricted to subspace-based methods. An off-grid sparse recovery-based algorithm is proposed in this paper to improve the accuracy of existing algorithms in low signal-to-noise ratio situations. The covariance and pseudo covariance matrices can be jointly represented subject to block sparsity constraints by taking advantage of the joint sparsity between signal components and bias. Furthermore, the estimation problem is transformed into a single measurement vector problem utilizing the focused operation, resulting in a significant reduction in computational complexity. The proposed algorithm's error threshold and the Cramer–Rao bound for wideband noncircular DOA estimation are deduced in detail. The proposed algorithm's effectiveness and feasibility are demonstrated by simulation results.     

采用降秩多级维纳滤波器的二维DOA估计快速算法

针对双平行线阵的二维波达方向(DOA)估计问题,为有效降低计算复杂度,提出了一种基于降秩多级维纳滤波器(MSWF)的快速算法。首先利用MSWF的前向递推实现信号子空间的快速估计,无需估计协方差矩阵和特征分解;然后,通过MUSIC算法对方位角和俯仰角的估计进行分维估计,使二维DOA估计退化为两个一维DOA估计问题,且方位角和俯仰角自动配对,进一步降低了运算量。仿真结果表明,该方法的估计精度优于同样基于双平行线阵提出的波达方向矩阵法(DOAM),俯仰角兼并时同样适用,计算复杂度低,适用于实时性要求高的应用背景。