导向矢量和协方差矩阵联合迭代估计的稳健波束形成算法

针对自适应波束形成器在目标导向矢量存在约束偏差时性能急剧下降的问题,该文提出一种目标导向矢量和干扰噪声协方差矩阵联合迭代估计的稳健波束形成算法。该算法首先采用稀疏重构的方法得到目标导向矢量的初始值,并通过从采样协方差矩阵中剔除目标信号估计值完成干扰加噪声协方差矩阵的初始化;然后在建立导向矢量误差优化模型的基础上,采用凸优化方法对目标导向矢量和干扰加噪声协方差矩阵联合迭代求解。最后利用目标导向矢量和干扰加噪声协方差矩阵的稳态估计值获得自适应权矢量。仿真结果表明该算法提高了波束形成器在目标导向矢量约束偏差时的输出信干噪比。     

一种小快拍情况下的稳健的波束形成方法

小快拍情况下采样协方差矩阵没有收敛,假设的白噪声表现为色噪声,从而导致了自适应波束形成畸变.本文从采样协方差矩阵特征值分析出发,提出了一种自适应对其修正的方法,相当于对采样协方差矩阵进行扰动,改善了采样协方差矩阵的收敛性,从而提高了波束形成的性能和稳健性.最后性能分析和计算机仿真验证了所提方法的有效性和优越性.     

基于矩阵加权的稳健波束形成方法

提出了一种基于约束主瓣幅度波动范围的矩阵加权波束形成方法,该方法相比已有方法可以有效控制方向图的主瓣形状、旁瓣电平以及零点深度.存在噪声和系统误差时,该方法对于信号功率的估计具有更好的稳健性.通过约束主瓣幅度波动范围波束形成方法求得加权矩阵的协方差矩阵,对该协方差矩阵做特征值分解求得加权矩阵,通过划分特征值大小来确定最小规模的加权矩阵,该加权矩阵可以在不损失方向图形状和信号功率估计性能的条件下有效降低系统实现复杂度.     

基于空时二维协方差矩阵修正的波束形成算法

在实际应用环境中,信源和阵列传感器等存在误差,假设期望信号的导向矢量与真实信源导向矢量的失配会导致阵列波束形成器把期望信号当作干扰来加以抑制。针对信号匹配误差导致自适应波束形成性能下降的问题,提出了一种基于空时二维协方差矩阵修正的波束形成算法,利用空时结构对宽带幅相误差校正的特性,对空时二维协方差矩阵进行重构,并对修正协方差矩阵进行特征值分解,分离出信号加干扰子空间,将失配导向矢量投影可使期望信号与噪声子空间严格正交,最后求解算法最优权值。算法有效改善了波束形成的输出信噪比,计算机仿真验证了理论分析的正确性和算法的稳健性。     

基于嵌套阵列的稀疏表示稳健波束形成方法

针对波束形成中目标方位失配以及噪声加干扰的协方差矩阵非精确重构造成的波束形成方法性能下降的问题,提出一种基于嵌套阵列的稀疏表示稳健波束形成方法。在该方法中,计算嵌套阵的采样协方差矩阵,通过差合作阵处理得到一孔径扩展的虚拟均匀线列阵;基于稀疏表示的方法来估计目标以及干扰的准确方位信息;进一步利用得到的方位信息构造导向矢量,通过最小二乘方法计算干扰信号的精确功率值;最后重构干扰加噪声协方差矩阵,通过波束形成实现干扰抑制。数值仿真表明,所提方法有效提升了干扰加噪声协方差矩阵重构精确度,在不同信噪比和快拍数条件下,输出信噪比都能逼近最优信干噪比,验证了该算法的有效性。     

一种低快拍情况下稳健的波束形成算法

针对低快拍情况下自适应波束形成器输出方向图存在主峰偏移,副瓣电平较高的问题,该文提出一种低快拍情况下稳健的波束形成算法。算法通过对线性约束最小方差(LCMV)波束形成器的输入信号协方差矩阵进行指数修正,改善了采用协方差矩阵求逆（SMI）算法的LCMV波束形成器输入信号协方差矩阵小特征值在低快拍情况下的发散程度,从而可在减小主峰偏移的同时,有效地解决副瓣电平较高的问题;最后通过分析波束形成器输出信干噪比(SINR)随修正指数变化的情况,给出算法修正指数的取值范围。计算机仿真结果验证了该算法的有效性和优越性。      

一种样本污染且快拍数有限情况下的稳健的波束形成方法

样本污染情况下,期望信号方向上也可能形成零陷,导致输出信号干扰噪声比严重下降;快拍数有限情况下,假设的白噪声表现为色噪声,从而加剧了自适应波束形成畸变。基于此,该文从采样协方差矩阵特征值分析出发,提出了一种自适应修正方法,相当于对采样协方差矩阵进行扰动,从而提高了波束形成的性能和稳健性。性能分析和计算机仿真验证了所提方法的可行性和有效性。     

一种基于协方差矩阵重构的波束形成算法

针对Capon波束形成在误差条件下敏感性问题,提出一种基于协方差矩阵重构的鲁棒波束形成算法。算法将信号集中出现的空域划分为干扰区域和信号区域,接着将两个区域划分为若干相互独立不重叠的部分,对干扰区域积分,构造出干扰协方差矩阵;再利用采样协方差矩阵特征分解后的最小特征值重构出噪声协方差矩阵;最后对期望信号导向矢量误差进行环不确定集建模,并在期望信号导向矢量环不确定集上进行Capon谱积分来估计期望信号协方差矩阵,根据其主特征矢量获取期望信号导向矢量。仿真表明,与传统鲁棒波束形成算法相比,此方法在不同快拍数以及输入信噪比条件下,性能更加优异且稳定,同时计算量较小。     

基于稀疏和低秩恢复的稳健DOA估计方法

该文针对有限次采样导致传统波达方向角(DOA)估计算法存在较大估计误差的问题,提出一种基于稀疏低秩分解(SLRD)的稳健DOA估计方法。首先,基于低秩矩阵分解方法,将接收信号协方差矩阵建模为低秩无噪协方差及稀疏噪声协方差矩阵之和;而后基于低秩恢复理论,构造关于信号和噪声协方差矩阵的凸优化问题;再者构建关于采样协方差矩阵估计误差的凸模型,并将此凸集显式包含进凸优化问题以改善信号协方差矩阵估计性能进而提高DOA估计精度及稳健性;最后基于所得最优无噪声协方差矩阵,利用最小方差无畸变响应(MVDR)方法实现DOA估计。此外,基于采样协方差矩阵估计误差服从渐进正态分布的统计特性,该文推导了一种误差参数因子选取准则以较好重构无噪声协方差矩阵。数值仿真表明,与传统常规波束形成(CBF)、最小方差无畸变响应(MVDR)、传统多重信号分类(MUSIC)及基于稀疏低秩分解的增强拉格朗日乘子(SLD-ALM)算法相比,有限次采样条件下所提算法具有较高DOA估计精度及较好稳健性能。     

对角加载对信号源数检测性能的改善

对角加载技术可抑制小特征值对自适应权值的影响来加速自适应波束形成器的收敛性以及抑制导向矢量误差的影响避免信号相消,该技术通常用于稳健的波束形成算法.基于对角加载技术,本文提出了一种信号源数目判定的改进方法,通过对角加载数据协方差阵,可以平滑小快拍数和空间色噪声时的噪声特征值分散程度从而减轻其对信号源数目估计的影响,证明了该估计器的强一致性,分析了加载量对信号源数目估计的影响.最后通过仿真以及实测数据比较了本文方法和已有方法的性能,验证了所提方法的有效性.     

一种新的相干信号波束形成方法

存在相干信号情况下的常规波束形成器,由于相干信号的相对信号强度估计误差、天线阵导向矢量扰动误差和采样协方差矩阵估计误差的存在,会产生波束畸变及信号相消,输出信干噪比会明显下降。该文提出一种基于量子信号处理中内积成形算法的波束形成器,并分析了波束形成器的性能,通过仿真比较验证了新的波束形成器的有效性。分析和实验表明,新波束形成器作为一种鲁棒性波束形成器,在上述应用环境中,通过适当调整影响因子,可以得到比传统波束形成器更稳定的性能输出。     

四元数域干扰加噪声协方差矩阵重构鲁棒自适应波束形成

基于电磁矢量传感器阵列的四元数Capon波束形成器较传统的复数域Capon波束形成器有更好的性能。但是该方法在存在指向误差和极化失配的情况下性能急剧下降,甚至会出现信号相消现象。本文将协方差矩阵重构方法推广于四元数Capon波束形成中,通过利用Q-Capon的极化-角度谱估计得到干扰和噪声的功率来对干扰加噪声协方差矩阵进行重构,避免了对角加载方法中对对角加载因子的求解,而且能够有效克服指向误差与极化失配带来的性能下降。计算机仿真表明,该方法相较于其他四元数域的方法有着更好的性能。      

一种新的基于特征值分析的盲信噪比估计方法

对自相关矩阵的特征值分解进行公式推导,基于此提出了一种采用协方差矩阵进行特征值分析的盲信噪比估计方法。在加性高斯白噪声环境下的仿真结果表明,该方法的估计偏差及标准差等性能均优于基于自相关矩阵特征值分析的盲信噪比估计方法。     

Eigenvalue Beamforming Using a Multirank MVDR Beamformer and Subspace Selection

We derive eigenvalue beamformers to resolve an unknown signal of interest whose spatial signature lies in a known subspace, but whose orientation in that subspace is otherwise unknown. The unknown orientation may be fixed, in which case the signal covariance is rank-1, or it may be random, in which case the signal covariance is multirank. We present a systematic treatment of such signal models and explain their relevance for modeling signal uncertainties. We then present a multirank generalization of the MVDR beamformer. The idea is to minimize the power at the output of a matrix beamformer, while enforcing a data dependent distortionless constraint in the signal subspace, which we design based on the type of signal we wish to resolve. We show that the eigenvalues of an error covariance matrix are fundamental for resolving signals of interest. Signals with rank-1 covariances are resolved by the largest eigenvalues of the error covariance, while signals with multirank covariances are resolved by the smallest eigenvalues. Thus, the beamformers we design are eigenvalue beamformers, which extract signal information from eigen-modes of an error covariance. We address the tradeoff between angular resolution of eigenvalue beamformers and the fraction of the signal power they capture.     

冲击噪声背景下基于最小均方归一化误差的波束形成算法

 本文提出一种适用于任意未知统计特性的代数拖尾冲击噪声环境下的MMSNE波束形成算法。算法利用输出信号和参考信号之间的“归一化误差”最小化来求解最优权向量。“归一化误差”定义为接收信号的瞬时自适应无穷范数归一化的形式。与基于最小分数低阶误差波束形成算法相比,MMSNE波束形成算法计算更为简单;不需要噪声特征指数的先验信息或估计;适用于更广的冲击噪声环境;具有更小的估计误差;具有更强的干扰抑制能力。     

Feedback beamforming technique for receiving multipath coherent signals

A novel feedback beamformer is proposed for receiving multipath coherent signals. First, a subtraction-based minimum variance distortionless response beamformer is utilized to obtain an estimation of the desired signal. Then its output is fed back to the original full aperture array where a minimum mean square error (MMSE) beamformer is applied. Due to multipath-matched feature of the MMSE beamformer, the novel feedback beamformer can combine multipath signals without aperture loss. Two feedback schemes are proposed and their effectiveness is verified via simulation.     

Statistical analysis of beam-space estimation fordirection-of-arrivals

The authors analyze the beam-space performance of the DOA estimation for several subspace processing algorithms in a unified fashion based on a finite amount of measurements. The mean-squared errors (MSE) of the beam-space DOA estimation for these algorithms are expressed; in a single formula using physical parameters like array manifold, beamformer matrix. The important properties of beam-space performance are revealed; for instance, increasing beam-space dimension does not always reduce MSE for all algorithms. Also, the criteria for designing minimum mean-squared error beamformers are discussed     

基于加权最大范数的SAR自聚焦方法

基于最大似然估计的特征向量分解自聚焦算法利用最大特征值对应的特征向量实现对相位误差的估计。该方法虽然具备精确和稳健的性能,但需要对协方差矩阵进行特征分解,导致实际数据在处理中运算量巨大,对内存要求也很高,难以在实时合成孔径雷达(SAR)成像处理中应用。该文提出一种基于加权最大范数的自聚焦方法,通过求解二范数最大化的优化函数对目标特征向量进行估计,避免了特征值的分解过程,有效提升了运算效率;利用信噪比加权的思想,对不同距离单元赋予不同的权值,增强了优质特显点样本对相位误差的估计贡献,有效改善了自聚焦精度。通过实测SAR和ISAR数据处理验证了算法的有效性。     

Transmit beamforming for space-frequency coded MIMO-OFDM systems with spatial correlation feedback

This paper addresses the problem of joint optimization of transmit beamforming and space-frequency (SF) coding for MIMO-OFDM systems with spatial correlation feedback in broadband communications. This problem is challenging in the sense that the transmitter should be designed to beamform across multiple eigenspaces associated with the multipath environment simultaneously. With arbitrary transmit spatial correlation, the performance analysis for SF-coded MIMO-OFDM systems with beamforming is provided, and a general optimization problem for the beamforming design is formulated. Three suboptimal approaches to design the beamformer based on the derived design criteria are proposed: i) eigenvalue selection scheme; ii) eigenspace selection scheme; and iii) per-subcarrier approach based on decoding at each subcarrier. The proposed schemes take into account the multiple eigenspace information associated with the multipath-delay channel. Improvement in the performance over SF coding without beamforming is shown through simulations in terms of bit error rate. The eigenvalue selection scheme provides the best performance among the proposed algorithms. This scheme locates the subspace associated with the largest eigenvalues in the eigenspace of the covariance matrices. With the eigenvalue selection scheme, the performance improvement is about 3 dB over the SF coding without beamforming for highly correlated channels as shown in our simulations.     

A gradient descent implementation of the adaptive robust narrowband constrained LMS beamformer

Classical adaptive beamforming strategies have been shown to exhibit a substantial performance degradation if an error exists in the estimation of the angle of arrival (AOA). The optimization problem can be modified to incorporate an improved constraint allowing for robustness to AOA imperfections. In this paper, we propose a gradient descent implementation of the robust adaptive beamformer whereby a direct relation is established between the Lagrange multiplier and the adaptation step size. This allows for the heuristic approximation of one of the parameters while establishing a valid range for the other. Computer simulations have been used to confirm the findings of this study and illustrate the performance of the developed algorithm.