基于最小冗余线阵的二维DOA估计方法

针对传感器阵列二维DOA估计中阵元数较多且阵元利用率较低的问题,提出了一种低阵元冗余的二维DOA估计方法.该方法通过在最小冗余线阵基础上添加两个导向阵元的方法,将最小冗余线阵的应用拓展到二维DOA估计.同时该方法利用多个时延的阵元输出共轭循环相关函数构造"伪数据阵",在时空域中等效出两个具有旋转不变性的平行子阵,进而运用DOA矩阵法估计信号二维DOA.该方法不仅避免了最优时延选择问题,继承了DOA矩阵法无需谱峰搜索且无需二维角度参数配对等优点.还用较少的阵元获得了较大的阵列有效孔径.仿真结果表明,该方法与CCDM算法相比具有更好的低信噪比适应能力和稳健性.     

基于频域ICA的波达方向估计方法

为降低阵元数目和提高相干多径入射信号的波达方向(DOA)估计精度。提出了一种基于频域独立分量分析与波束零点形成相结合的DOA估计方法。该方法综合利用阵列结构信息和目标信号的统计独立特性,与常规算法相比,降低了算法对阵元数目和精度的依赖,并能实现对相干多径入射信号的DOA估计。计算机仿真结果表明,该算法具有少阵元、收敛快、误差小的特点。     

Real-valued MVDR beamforming using spherical arrays with frequency invariant characteristic

Complex-valued minimum variance distortionless response (MVDR) beamforming for wideband signals has very high computational amount. In this paper, we design a novel real-valued MVDR beamformer for spherical arrays. The dependence of the array steering matrix on source signal directions and frequencies is decoupled using spherical harmonic decomposition. Then a compensation network is designed to solve the frequency dependence of the array response and to get a new array response only determined by the spherical harmonics of the source directions. All frequency bins of wideband signals can be used together instead of being processed independently. By exploiting the property of the conjugate spherical harmonics, a unitary transform can be found to acquire a real-valued frequency invariant steering matrix (FISM). Based on the FISM, real-valued MVDR (RV-MVDR) is developed to obtain good performance with low computational amount. Simulation results demonstrate the performance of our proposed method for beamforming and direction-of-arrival (DOA) estimation by comparing with the complex-valued and real-weighted MVDR methods.     

编队舰船对各自拖线阵声纳DOA估计性能的影响

拖船辐射噪声是影响拖曳线列阵声纳探测性能的重要因素。就编队舰船辐射噪声对各自拖曳线列阵声纳采用常规波束形成方法、最小方差无失真响应（Minimum Variance Distortionless Response,MVDR）波束形成方法和盲源分离与MVDR波束形成融合方法时方位估计性能的影响,进行了全面的仿真比较。结果表明,编队舰船噪声对常规波束形成方法、MVDR波束形成方法的影响非常严重,必须采取措施对其进行抑制;而盲源分离与MVDR融合方法则表现出了优越的性能,一定程度上抑制了编队舰船噪声对拖曳线列阵声纳方位估计的影响。     

一种实用的盲波束形成和DOA估计方法

基于子空间分解的原理对相关文献的方法进行了改进.用平均时间延迟协方差矩阵的奇异值分解代替传感器数据矩阵的奇异值分解,大大地提高了运算速度,使模型和算法能更有效地应用于实际情况.同时仿真实验和海上实测数据结果表明,该方法能很好地实现方位估计和信号恢复,而且性能更佳.     

宽线性波束形成技术综述

在圆信号的假设条件下,传统的线性波束形成技术仅仅利用了天线阵列观测矢量的协方差矩阵。然而,现代通信领域中的很多人工调制信号具有非圆特性,观测矢量不仅存在协方差矩阵,还存在伪协方差矩阵。宽线性波束形成技术是针对非圆信号环境提出的一类新技术,该类技术通过构造一个包含天线阵列观测矢量及其共轭的扩展观测矢量,建立有利于特定方向信号接收的目标函数及约束,推导出相应的扩展权重矢量。同传统线性波束形成技术相比,宽线性波束形成技术对非圆信号的接收性能有了明显提升,近年来,该类技术成为国内外研究热点之一。本文介绍了圆信号和非圆信号定义,给出了阵列模型并介绍了最小方差无畸变响应波束形成,对各种宽线性波束形成算法进行了综述,并对宽线性波束形成技术的下一步研究方向进行了展望。     

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。     

Nonuniform linear array DOA estimation using EM criterion

In this letter, we address the problem of Direction of Arrival (DOA) estimation with nonuniform linear array in the context of sparse Bayesian learning (SBL) framework. The nonuniform array output is deemed as an incomplete-data observation, and a hypothetical uniform linear array output is treated as an unavailable complete-data observation. Then the Expectation-Maximization (EM) criterion is directly utilized to iteratively maximize the expected value of the complete-data log likelihood under the posterior distribution of the latent variable. The novelties of the proposed method lie in its capability of interpolating the actual received data to a virtual uniform linear array, therefore extending the achievable array aperture. Simulation results manifests the superiority of the proposed method over off-the-shelf algorithms, specially on circumstances such as low SNR, insufficient snapshots, and spatially adjacent sources.     

基于MATLAB的阵列信号处理模型构建和仿真方法

文章介绍如何使用MATLAB构建阵列信号处理系统模型,实现前向平滑的协方差矩阵产生方法,波达方向估计的子空间方法,最小功率波束合成器的权值求解算法和基本系统参数的仿真。各种复杂的阵列信号处理仿真都可以在此基础上完成。     

基于宽带聚焦矩阵和高阶累积量的OFDM信号的来波方向估计

为了解决正交频分复用(OFDM)宽带信号处理的问题,研究了基于宽带聚焦矩阵和高阶累积量的波达方向(DOA)估计方法。前者是通过傅里叶变换将宽带阵列数据分解为若干窄带信号,再利用一种聚焦矩阵将不同频带下的方向矩阵变换到同一参考频率下,然后用多重信号分类(MUSIC)算法来估计DOA;高阶累积量算法是通过聚焦操作,把各个窄带频率处的阵列输出矢量变换到聚焦频率处,然后求其累积量矩阵。对各个累积量矩阵进行加权平均并特征值分解,再应用MUSIC算法估计DOA。理论分析和仿真结果表明,两种方法都能够精确地估计OFDM信号的DOA,四阶累积量方法的空间分辨率比聚焦矩阵方法有所提高。四阶累积量算法扩展了阵列孔径,信噪比(SNR)较低的时候也有很好的适应性。     

基于盲波束形成的CDMA信号伪码估计

为解决低信噪比(SNR)条件下CDMA信号伪码盲估计问题,提出结合盲波束形成和奇异值分解的盲估计方法。在信号波达方向和信道同步信息未知的情况下,估计各用户的扩频序列,完成多用户检测,并给出高斯噪声下盲多用户检测的完整数学表达式。理论分析和仿真结果表明,该方法通过天线阵列的分集和扩频增益能在低SNR下完成伪码估计,且其多用户检测性能相对传统单天线检测算法有较大提高。     

DOA estimation based on the database retrieval technique with nonuniform quantization and clustering

A direction-of-arrival (DOA) estimation method using an array antenna has been developed based on a database retrieval technique. This method uses a database consisting of a set of correlation matrices of the array output vectors for various combinations of the quantized angles and signal powers. When a correlation matrix is estimated from an observed output vector, several correlation matrices close to the estimated one are searched out from the database, and the DOA is estimated based on the retrieved data. This method gives an accurate estimation, but the use of uniform quantization step size requires a large amount of storage space. In this paper, the relation between the quantization step size and the estimation accuracy is analyzed, and a nonuniform quantization scheme is developed to reduce the database size without sacrificing the estimation accuracy. A clustering technique is also introduced to alleviate the performance degradation caused by the retrieval of data which have similar correlation matrices but have much different angles. We show by simulations that the nonuniform quantization reduces the database size and the clustering improves the estimation accuracy, and that the proposed method is applicable to the array of three elements at the present.     

Covariance sparsity-aware DOA estimation for nonuniform noise

This paper reformulates the problem of direction-of-arrival (DOA) estimation for unknown nonuniform noise by exploiting a sparse representation of the array covariance vectors. In the proposed covariance sparsity-aware DOA estimator, the unknown noise variances can be eliminated by a linear transformation, and DOA estimation is reduced to a sparse reconstruction problem with nonnegativity constraint. The proposed method not only obtains an extended-aperture array with increased degrees of freedom which enables us to handle more sources than sensors, but also provides superiority in performance and robustness against nonuniform noise. Numerical examples under different conditions demonstrate the effectiveness of the proposed method.     

宽带频率-方向不变恒定主瓣波束形成

常规波束形成主瓣随频率和指向变化,为了实现频率方向二维主瓣恒定,本文基于约束优化的方法,提出了一种宽带频率-方向不变恒定主瓣波束形成算法,并将之应用到宽带信号DOA估计中。首先在参考频率上约束方向不变主瓣恒定,然后在不同指向上约束频率不变主瓣恒定,并将约束问题表示为标准的二阶锥规划形式,运用SeDuMi求解最优权向量。仿真实验表明,本文提出的算法能够实现不同频率不同指向上波束主瓣恒定,与频率不变波束相比,采用频率-方向不变波束的DOA估计方法降低了信噪比分辨门限,提高了估计精度。     

Joint frequency, 2-D DOA,and polarization estimation using parallel factor analysis

This paper proposes a new algorithm for joint frequency, two-dimensional (2-D) directions-of-arrival (DOA), and polarization estimation using parallel factor (PARAFAC) analysis model and cumulant. The proposed algorithm designs a new array configuration, and extends the PARAFAC analysis model from the common data-domain and subspace-domain to the cumulant one, and forms three-way arrays by using the three cumulant matrices obtained from the properly chosen dipole outputs, and analyzes the uniqueness of low-...     

稳健的全阵波束形成方法

由于相干干扰和角度估计误差影响波束形成性能,对此提出一种稳健的波束形成方法。采用加权空间平滑算法去除相干性;对期望信号施加约束,保证一定角度估计误差范围内算法的稳健性;进行全阵波束形成变换,避免阵列孔径损失。仿真结果表明,在相干干扰环境中,该算法的方向图在期望信号方向有明显主瓣,有利于接收期望信号;在一定的角度估计误差范围内,该算法可保持比较高的且比较平坦的输出信干噪比;随着输入信噪比的增加,输出信干噪比几乎线性增大。     

基于Bayesian方法的鲁棒约束最小二乘恒模算法

在实际通信环境中,信号方向向量偏差会导致线性约束最小二乘恒模算法(LSCMA)的性能急剧下降,针对这一问题,提出了基于Bayesian方法的鲁棒约束LSCMA算法并对其性能进行了分析。该算法根据接收到的采样信号和信号的先验信息对实际信号方向向量进行估计,进而推导出权重向量的迭代公式,降低了信号波达方向的不确定性,对信号方向向量偏差具有较强的鲁棒性,从而可以保证阵列输出的信干噪比接近最优值。本文采用递推方法来计算逆矩阵,大大地降低了计算复杂度,能够满足实时处理的要求。仿真实验结果与理论分析表明,与线性约束LSCMA算法相比,所提鲁棒自适应波束形成算法具有更好的性能,且能适应实际复杂的通信环境。     

基于改进的AR模型的逆波束形成方法研究

逆波束形成是利用各阵元接收信号间的互功率谱估计声场平面波强度的理论,较之常规波束形成,它具有更高的方位分辨能力和噪声干扰抑制能力。在分析逆波束形成理论及其傅立叶类求解算法的基础上,提出了基于改进的AR模型的求解方法。该方法将参数模型引入到逆波束形成的求解当中,通过引入前向后向平均,克服了信号协方差矩阵在小信噪比和快拍数较少情况下估计不准的问题,从而减轻了旁瓣的起伏,有助于实现对弱目标信号的检测与估计。通过大量计算机仿真实验,证实了所提算法的可行性和优越性。     

六角形阵快速傅立叶变换波束形成算法

在六角形阵阵元数较多时,传统的频域相移求和波束形成方法要求的运算量很大.为此提出一种采用六角形快速傅立叶变换HFFT(Hexagonal Fast Fourier Transform)的波束形成算法.使用六角形傅立叶变换HDFT(Hexagonal Discrete Fourier Transform)完成六角形阵的波束形成,由于HDFT存在快速算法HFFT,因此能够显著降低波束形成的运算量.首先在各个通道上做FFT,将信号变换到频域,然后转角重排,再对各个阵元上相同的频点做HFFT,得到频域常规波束形成输出.理论分析表明,对于窄带信号的六角形阵波束形成,所提出的算法所需的计算量比传统的相移求和方法降低了95%以上.仿真和试验结果表明,提出的算法在不影响阵列处理性能的同时,显著降低了波束形成所需的计算量,易于工程实现.     

互质线阵中一种基于共轭增广的DOA估计算法

论文开展互质线阵下的空间谱估计研究。通过利用信号二阶统计量的共轭增广特性,提出互质阵下基于共轭增广的酉旋转不变性进行信号参数估计 (Conjugate augmented unitary estimation of signal parameters via rotational invariance technique, CA-UESPRIT)波达方向(Direction of arrival, DOA)估计算法。该算法先利用不同时长间隔下接收信号的二阶统计量,构造共轭增广虚拟阵列以扩展阵列孔径和提高空间自由度。然后采用基于互质特性的联合UESPRIT算法实现DOA估计。相比于传统互质线阵下的联合UESPRIT算法,CA-UESPRIT算法DOA估计性能更优。此外,通过酉变换可以将ESPRIT算法的协方差矩阵从复数域转化到实数域,降低了复杂度的同时保证了测向精度。仿真结果证实了所提算法的有效性。