A projection approach for robust adaptive beamforming

It is well known that calibration errors can seriously degrade performance in adaptive arrays, particularly when the input signal-to-noise ratio is large. The effect is caused by the perturbation of the presumed steering vector from its optimal value. Although it is not as widely known, similar degradation occurs in sampled matrix inversion processing when the covariance matrix is estimated while the desired signal is present in the snapshot data. Under these conditions, performance loss is due to errors in the estimated covariance matrix and occurs even when the steering vector is known exactly. In the paper, a new method based of modification of the steering vector is proposed to overcome both the problems of perturbation and of sample covariance errors. The method involves projection of the presumed steering vector onto the observed signal-plus-interference subspace. An analysis is also presented illustrating that the sample covariance errors can be viewed as a particular type of perturbation error and a simple approximation is derived for the expected beamformer performance as a function of the number of data snapshots. Both analytical and experimental results are presented that illustrate the advantages of the proposed method     

基于投影对消处理技术的稳健自适应波束形成

针对传统自适应波束形成算法中目标波达方向(Direction of Arrival,DOA)估计不准确引起的波束形成性能下降问题,提出了一种采用投影对消矩阵的稳健自适应波束形成算法.首先,寻找与估计波达方向有最大相关性的特征矢量作为目标信号特征矢量,然后构建对消矩阵消除协方差矩阵中的信号分量,最后通过增加零点约束实现干...     

均匀先验分布Bayesian自适应波束形成方法

针对UUV舷侧阵存在观测信号方向有误差的情况，提出了基于均匀先验分布的Bayesian自适应波束形成方法（UB）。该方法假设期望信号的到达方向是区间内服从均匀先验分布的一个变量，利用含有均匀先验分布信息的阵列接收数据和Bayesian后验分布来估计期望信号的到达方向，最后计算权矢量。仿真结果表明，该方法对观测信号方向误差具有较好的稳健性，其阵列方向图的主波束不受观测方向误差的影响，始终对准信号的到达方向，其输出信干噪比随着输入信噪比、快拍数的增加而稳定增加，随着观测方向误差的增大而保持稳定的恒定值。      

柔性稀疏阵的稳健波束形成

本文将导向矢量失配时的稳健波束形成问题归结为二次锥规划问题,利用高效的内点法求解。该波束形成器成功地应用于存在阵元位置误差的柔性稀疏阵,相对于经典的对角线加载法、特征空间法,在不同的输入信噪比下获得了更好的输出信号干扰加噪声比。仿真结果表明对超稀疏分布的柔性阵,阵元位置误差对输出SINR起决定性影响,而阵列稀疏程度对其影响不大。     

基于谱分析的稳健自适应波束形成算法

针对在导向矢量存在误差情况下,自适应波束形成算法性能下降问题,提出一种基于谱分析的稳健自适应波束形成(SA-RAB)算法。算法利用空域与频域的对称性,根据真实导向矢量与理想导向矢量之间的误差,运用谱分析(SA)技术确定波束主瓣宽度,最后利用二阶锥规划(SOCP)技术在主瓣宽度内形成平顶响应,并在副瓣区域内进行干扰抑制。仿真结果表明:该算法可有效地抑制干扰,并输出理想的信号干扰噪声比(SINR),且提高了波束形成针对导向矢量误差的稳健性,验证了算法的有效性和优越性。     

Efficient robust adaptive beamforming for cyclostationary signals

This paper deals with the problem of robust adaptive array beamforming for cyclostationary signals. By exploiting the signal cyclostationarity, the LS-SCORE algorithm presented in a paper by Agee et al. (1990) has been shown to be effective in performing adaptive beamforming without requiring the direction vector of the desired signal. However, this algorithm suffers from severe performance degradation even if there is a small mismatch in the cycle frequency of the desired signal. In this paper, we first evaluate the performance of the LS-SCORE algorithm in the presence of cycle frequency error (CFE). An analytical formula is derived to show the behavior of the performance degradation due to CFE. An efficient method is then developed based on the fact that the array output power approaches its maximum as the CFE is reduced. We formulate the problem as an optimization problem for reducing the CFE iteratively to achieve robust adaptive beamforming against the CFE. Simulation examples for confirming the theoretical analysis and showing the effectiveness of the proposed method are provided     

一种改进均方误差性能的新型鲁棒性波束形成算法

结合信干噪比最大化和均方误差最小化两个优化目标,提出一种新型的鲁棒性波束形成算法.该方法考虑信号估计误差,在传统的最小方差的代价函数中引入信号协方差矩阵的估计误差,并在波达角估计误差的约束下,将鲁棒性波束形成器转换成基于支持向量机形式的波束形成器,通过一种高效的新型支持向量机训练算法计算阵列权值;然后以均方误差最小化为目标来修正阵列权值.仿真结果表明:该方法降低了波束形成器对信号估计误差的敏感度,提高了其抑制非平稳干扰的能力,且具有更好的均方误差性能.     

抑制快速运动强干扰的稳健自适应波束形成方法

针对存在快速运动强干扰时自适应波束形成性能的严重降低,提出了一种稳健的自适应波束形成方法。该方法使用零陷展宽技术修改采样协方差矩阵,使其自动地在干扰方位形成展宽的零陷以抑制运动强干扰,同时采用对角加载技术,以增强此方法对系统误差的稳健性,加载量通过约束扫描向量的误差来确定。仿真结果表明该方法能有效地提高当存在快速运动的强干扰时自适应波束形成的性能。     

A spatial filtering approach to robust adaptive beaming

The problem of controlling the superresolution in adaptive beamformers is treated. A straightforward method is presented that works for both narrowband and broadband arrays. The method is based on forming the blocking matrix in a general sidelobe canceller structure using a spatial FIR filter. The suppression of this spatial filter and the implicit noise of the leaky least-mean-square algorithm together determine the beamformer     

An alternative approach to linearly constrained adaptive beamforming

A beamforming structure is presented which can be used to implement a wide variety of linearly constrained adaptive array processors. The structure is designed for use with arrays which have been time-delay steered such that the desired signal of interest appears approximately in phase at the steered outputs. One major advantage of the new structure is the constraints can be implemented using simple hardware differencing amplifiers. The structure is shown to incorporate algorithms which have been suggested previously for use in adaptive beamforming as well as to include new approaches. It is also particularly useful for studying the effects of steering errors on array performance. Numerical examples illustrating the performance of the structure are presented.     

基于导向矢量估计的鲁棒自适应波束形成算法

针对在导向矢量存在误差情况下自适应波束形成算法性能严重下降的问题,提出一种基于导向矢量估计的鲁棒自适应波束形成(Steering Vector Estimation Based Robust Adaptive Beamforming,SVE-RAB)算法.算法用导向矢量不确定范围估计真实导向矢量,利用范数约束通过二阶锥规划技术提高波束形成的鲁棒性.算法可在导向矢量存在误差的情况下,对期望信号保持最大增益并有效抑制干扰,且有效提高了波束形成输出的信干噪比(Signal to Interference plus Noise Ratio,SINR).仿真结果验证了算法的有效性和优越性.     

几种自适应波束形成方法的对比

波束形成在无线通信、雷达、声呐等阵列系统中具有广泛应用。数字波束形成通常是基于接收信号的阵列响应和协方差矩阵的估计设计。由于天线增益、相位、波达方向(Direction-of-Arrival,DOA)和协方差矩阵估计的误差会导致导向矢量(Steering Vector,SV)产生模型失配,而这种模型失配会导致波束形成性能的下降。针对以上问题,给出了基于精度矩阵收缩估计的方法,采用了线性脊估计结构且用数据驱动和留一交叉验证来选择参数。通过Matlab仿真,研究了当存在模型不确定性时,基于精度矩阵收缩估计的方法以及基于协方差矩阵收缩估计和干扰加噪声协方差矩阵重构等方法的鲁棒性。结果显示,当存在模型失配时,基于精度矩阵收缩的波束形成方法在低信噪比时具有更优的鲁棒性。     

Principles of minimum variance robust adaptive beamforming design

Robustness is typically understood as an ability of adaptive beamforming algorithm to achieve high performance in the situations with imperfect, incomplete, or erroneous knowledge about the source, propagation media, and antenna array. It is also desired to achieve high performance with as little as possible prior information. In the last decade, several fruitful principles to minimum variance distortionless response (MVDR) robust adaptive beamforming (RAB) design have been developed and successfully applied to solve a number of problems in a wide range of applications. Such principles of MVDR RAB design are summarized here in a single paper. Prof. Gershman has actively participated in the development and applications of a number of such MVDR RAB design principles.     

Neural minor component analysis approach to robust constrained beamforming

Since the pioneering work of S.-I. Amari (1977) and E. Oja (1982; 1989; 1992), principal component neural networks and their extensions have become an active adaptive signal processing research field. One of such extensions is minor component analysis (MCA), that proves to be effective in tasks such as robust curve/surface fitting and noise reduction. The aims of the paper are to give a detailed and homogeneous review of one-unit first minor/principal component analysis and to propose an application to robust constrained beamforming. In particular, after a careful presentation of first/minor component analysis algorithms based on a single adaptive neuron, along with relevant convergence/steady-state theorems, it is shown how the adaptive robust constrained beamforming theory by H. Cox et al. (see IEEE Trans. Acoust. Speech. Sig. Process., vol.34, no.3, p.393-8, 1986; vol.35, no.10, p.1365-76, 1987) may be advantageously recast into an MCA setting. Experimental results obtained with a triangular array of microphones introduced in a teleconference context help to assess the usefulness of the proposed theory.     

A new approach to robust beamforming in the presence of steeringvector errors

This correspondence presents a new approach to array beamforming in the presence of steering vector errors, such as steering angle and carrier frequency errors. The basic idea is based on the principle that the output power of an optimized beamformer achieves a local maximum if the steering vector coincides with that of the desired signal. The proposed approach iteratively searches for the correct steering vector by maximizing the array mean output power using a first-order Taylor series approximation of the steering vector in terms of the steering errors. This method has the advantages of easy implementation and suffers no loss in the degrees of freedom for interference rejection. Furthermore, the method is independent of the choice of the phase center coordinate origin contrary to derivative constraint methods. Conditions for the existence of a solution using the proposed method are also established     

基于范数优化的对角加载稳健自适应波束形成

本文提出了一种基于范数优化的对角加载自适应波束形成算法。算法利用p-范数来代替2-范数对误差不确定性进行总体修正,从而解决导向矢量和样本方差矩阵同时存在失配误差时所导致的波束形成器性能下降问题。最优范数p通过遗传算法求得。仿真结果验证了在不同实验条件下,相比于传统的对角加载算法,使用最优p范数比2-范数约束具有更好的性能。     

基于麦克风阵列的宽带健壮自适应波束形成算法

研究了用于麦克风阵列语音增强的宽带健壮自适应波束形成算法。该算法结合频率聚焦技术和对角加载技术。在此基础上,通过优化最坏情况下的波束性能确定对角加载因子,求得了最优加载因子的近似解析表达式。和相关算法相比,使用最坏情况性能优化的算法具有更好的语音增强性能,由于求得了最优加载因子的解析解,还具有运算量低、容易实现等优点。同时,该解析解揭示了哪些因素可以影响最优加载因子,以及如何影响。计算机仿真验证了该结果的正确性和有效性。     

基于分数低阶统计量的线性约束恒模盲自适应波束形成方法

为解决传统恒模波束形成算法在受冲击噪声污染的无线环境中的性能退化问题,本文提出基于分数低阶统计量的线性约束恒模盲自适应波束形成方法,并从理论上分析了算法收敛条件。仿真实验表明,在稳定分布噪声下,新方法具有比传统恒模波束形成方法更好的韧性,而且比线性约束最小平均P范数波束形成方法具有更高的输出信干噪比,即使在强脉冲干扰环境下新方法仍具有较好的信号恢复能力。     

Steering vector optimization using subspace-based constraints for robust adaptive beamforming

Multidimensional Systems and Signal Processing - To address the issue of steering vector mismatch, a robust adaptive beamforming design via steering vector optimization is proposed in this paper....     

Robust adaptive beamforming using a Bayesian steering vector error model

We propose a Bayesian approach to robust adaptive beamforming which entails considering the steering vector of interest as a random variable with some prior distribution. The latter can be tuned in a simple way to reflect how far is the actual steering vector from its presumed value. Two different priors are proposed, namely a Bingham prior distribution and a distribution that directly reveals and depends upon the angle between the true and presumed steering vector. Accordingly, a non-informative prior is assigned to the interference plus noise covariance matrix R$\mathbf{R}$, which can be viewed as a means to introduce diagonal loading in a Bayesian framework. The minimum mean square distance estimate of the steering vector as well as the minimum mean square error estimate of R$\mathbf{R}$ are derived and implemented using a Gibbs sampling strategy. Numerical simulations show that the new beamformers possess a very good rate of convergence even in the presence of steering vector errors.