基于改进PSO的对角加载波束形成算法

当协方差矩阵和导向矢量估计存在误差时,Capon波束形成算法性能急剧下降.对角加载能够提升Capon波束形成算法对误差的鲁棒性,但是最优加载因子的确定是当前的难题.提出一种基于改进粒子群优化(IPSO)算法的对角加载波束形成算法,首先将加载因子与协方差矩阵特征值谱联系起来,利用协方差矩阵特征值谱的分布特性确定最优加载因...     

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。     

一种空间色噪声环境下的鲁棒自适应波束

该文提出了一种基于对角加载的鲁棒自适应波束形成算法,以提高空间色噪声环境中自适应波束对方向矢量误差的鲁棒性。该算法首先利用噪声协方差矩阵对阵列相关矩阵进行预白化,同时定义了一个与噪声矩阵相对应的椭圆方向矢量模糊集,然后,通过在该模糊集内进行最坏情况性能优化来确定对角加载因子。和现有的通过迭代求解加载因子的方法不同,该文给出了最优加载因子的近似解析表达式,降低了运算量,揭示了哪些因素可以影响最优加载因子,以及如何影响。仿真结果表明,在空间色噪声环境中,该算法具有很好的鲁棒性,并且,给出的加载因子表达式是其真实最优解的一个准确近似。     

最差性能最优通用信号模型稳健波束形成算法

针对空间分布散射信号源的稳健波束形成问题,提出了一种新的通用信号模型稳健波束形成算法,不仅得到了封闭形式的最优加权矢量,而且获得了最优的性能改善.其中分析了与传统对角加载的关系,给出了最优加载量的计算方法,并得出具有最优负加载的解才可以获得最优的性能改善.最后的仿真分析验证了所提出算法的正确性和有效性,而且发现最优加权矢量只取决于给定的接收数据和未知的失配量,与失配约束参数的选择无关,而失配约束参数只是参与最优权计算的辅助参数.     

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扰抑制.与传统对角加载类稳健波束形成算法相比,所提算法不受对角加载因子的影响,且在信干噪比较大时仍然具有良好的抗干扰性能.仿真对比实验验证了所提算法的有效性.     

一种改进的多级维纳滤波器误差加载算法

对角加载是目前广泛采用的提高自适应波束形成稳健性的技术之一.在分析多级维纳滤波器结构实现空时自适应处理算法的基础上,提出了一种改进的能够等价递推实现对角加载的误差加载算法.该算法在每一级递推计算中均对加载值进行一次修正,有效克服了传统方法的最终对角加载结果总是小于期望的加载值的缺点,避免了加载不足,其每级迭代计算过程只增加了两次实数乘法和加法运算.仿真结果证明了该算法的有效性.     

一种稳健自适应波束形成的变量对角加载方法

在设计稳健的自适应波束形成算法来消除阵列流形中的非确定性方面已经投入了很大的努力.这些不确定性可能由波达方向(DOA)的不确定性、阵列结构不理想、远近效应、相互耦合和其他的失配以及建立模型错误造成.提出了一种可供选择实现的包含椭球不确定约束导引矢量的线性约束最小方差(LCMV)的波束形成方法,更详细地说,真实的导引矢量是根据预测导引矢量采用将椭球约束施加到估计的导引矢量的递归最陡下降算法来估计的,对角加载技术必须满足椭球约束,其主要缺点是如何通过对非确定性约束的认识来得到最优的对角加载值不是很明确.通过这个问题的解决证明了该方法的可行性,而且对角加载技术是通过变量加载集成到自适应机制中而不是固定对角加载或Ad Hoc技术.     

迭代对角加载采样矩阵求逆鲁棒自适应波束形成

为有效克服导向矢量大失配误差对自适应波束形成器的影响,该文提出了一种迭代对角加载采样矩阵求逆鲁棒自适应波束形成算法。该算法对传统对角加载算法进行了迭代运算,基于Capon波束形成器的最优权矢量与假定导向矢量的基本关系,将每一步得到的权矢量,对应反解出一个比导向矢量假定值更为准确的导向矢量,并替代假定值,最终逼近真实的期望信号导向矢量。提出的方法在迭代过程中只需一步递推,无需对导向矢量建立不确定集,避免了在每步迭代中运用拉格朗日数值法或凸优化法,且明显提高了波束形成器的输出信干噪比。仿真结果验证了算法的正确性和有效性。     

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.     

On Diagonal Loading for Robust Adaptive Beamforming Based on Worst‐Case Performance Optimization

Robust adaptive beamforming based on worst‐case performance optimization is investigated in this paper. It improves robustness against steering vector mismatches by the approach of diagonal loading. A closed‐form solution to optimal loading is derived after some approximations. Besides reducing the computational complexity, it shows how different factors affect the optimal loading. Based on this solution, a performance analysis of the beamformer is carried out. As a consequence, approximated closed‐form expressions of the source‐of‐interest power estimation and the output signal‐to‐interference‐plus‐noise ratio are presented in order to predict its performance. Numerical examples show that the proposed closed‐form expressions are very close to their actual values.     

Further Study on Robust Adaptive Beamforming With Optimum Diagonal Loading

Significant effort has gone into designing robust adaptive beamforming algorithms to improve robustness against uncertainties in array manifold. These uncertainties may be caused by uncertainty in direction-of-arrival (DOA), imperfect array calibration, near-far effect, mutual coupling, and other mismatch and modeling errors. A diagonal loading technique is obligatory to fulfil the uncertainty constraint where the diagonal loading level is amended to satisfy the constrained value. The major drawback of diagonal loading techniques is that it is not clear how to get the optimum value of diagonal loading level based on the recognized level of uncertainty constraint. In this paper, an alternative realization of the robust adaptive linearly constrained minimum variance beamforming with ellipsoidal uncertainty constraint on the steering vector is developed. The diagonal loading technique is integrated into the adaptive update schemes by means of optimum variable loading technique which provides loading-on-demand mechanism rather than fixed, continuous or ad hoc loading. We additionally enrich the proposed robust adaptive beamformers by imposing a cooperative quadratic constraint on the weight vector norm to overcome noise enhancement at low SNR. Several numerical simulations with DOA mismatch, moving jamming, and mutual coupling are carried out to explore the performance of the proposed schemes and compare their performance with other traditional and robust beamformers     

基于可变对角载入的鲁棒自适应波束形成算法

针对传统算法对方向向量偏差敏感的缺点,提出了一种基于可变对角载入的鲁棒自适应波束形成算法.为了提高算法的鲁棒性,采用非线性约束条件下的最优化阵列输出功率对信号方向向量进行优化求解,且优化解中的参量能够准确求出.为了减少计算量,采用递推算法求逆矩阵并利用泰勒级数展开,推导出基于可变对角载入的权重向量公式.该算法可有效地抑制方向向量偏差所带来的影响,降低了计算量易于实时实现,提高了系统的鲁棒性,改善了阵列输出的信干噪比,使其更接近最优值.仿真结果表明,该算法相对传统算法可以获得更好的性能.     

Robust Adaptive Beamforming Under Quadratic Constraint with Recursive Method Implementation

When adaptive arrays are applied to practical problems, the performances of the conventional adaptive beamforming algorithms are known to degrade substantially in the presence of even slight mismatches between the actual and presumed array responses to the desired signal. Similar types of performance degradation can occur because of data nonstationarity and small training sample size, when the signal steering vector is known exactly. In this paper, to account for mismatches, we propose robust adaptive beamforming algorithm for implementing a quadratic inequality constraint with recursive method updating, which is based on explicit modeling of uncertainties in the desired signal array response and data covariance matrix. We show that the proposed algorithm belongs to the class of diagonal loading approaches, but diagonal loading terms can be precisely calculated based on the given level of uncertainties in the signal array response and data covariance matrix. The variable diagonal loading term is added at each recursive step, which leads to a simpler closed-form algorithm. Our proposed robust recursive algorithm improves the overall robustness against the signal steering vector mismatches and small training sample size, enhances the array system performance under random perturbations in sensor parameters and makes the mean output array SINR consistently close to the optimal one. Moreover, the proposed robust adaptive beamforming can be efficiently computed at a low complexity cost compared with the conventional adaptive beamforming algorithms. Computer simulation results demonstrate excellent performance of our proposed algorithm as compared with the existing adaptive beamforming algorithms.     

Robust adaptive beamforming using worst-case performance optimization: a solution to the signal mismatch problem

Adaptive beamforming methods are known to degrade if some of underlying assumptions on the environment, sources, or sensor array become violated. In particular, if the desired signal is present in training snapshots, the adaptive array performance may be quite sensitive even to slight mismatches between the presumed and actual signal steering vectors (spatial signatures). Such mismatches can occur as a result of environmental nonstationarities, look direction errors, imperfect array calibration, distorted antenna shape, as well as distortions caused by medium inhomogeneities, near-far mismatch, source spreading, and local scattering. The similar type of performance degradation can occur when the signal steering vector is known exactly but the training sample size is small. In this paper, we develop a new approach to robust adaptive beamforming in the presence of an arbitrary unknown signal steering vector mismatch. Our approach is based on the optimization of worst-case performance. It turns out that the natural formulation of this adaptive beamforming problem involves minimization of a quadratic function subject to infinitely many nonconvex quadratic constraints. We show that this (originally intractable) problem can be reformulated in a convex form as the so-called second-order cone (SOC) program and solved efficiently (in polynomial time) using the well-established interior point method. It is also shown that the proposed technique can be interpreted in terms of diagonal loading where the optimal value of the diagonal loading factor is computed based on the known level of uncertainty of the signal steering vector. Computer simulations with several frequently encountered types of signal steering vector mismatches show better performance of our robust beamformer as compared with existing adaptive beamforming algorithms.     

线性干扰参数约束的稳健LSMI波束形成算法

 针对稳健的加载样本矩阵求逆(LSMI)波束形成算法,给出了一种新的求解方法,获得了加载电平的准确计算公式,而且得出最优加载量为负值,且与约束参数的选取无关.为了改善LSMI波束形成算法的抗干扰性能,提出利用线性干扰参数约束(LJC)来实现,其中对LJC-LSMI波束形成算法进行了建模和求解,得到了最优加权矢量的表达式,并给出了具体的求解方法.仿真分析验证了算法的正确性和有效性,结果表明LJC-LSMI相对于LSMI具有较强的干扰抑制能力,相对于线性约束最小功率(LCMP)波束形成算法具有稳健的波束指向性能.     

Automatic computation of diagonal loading factor for robust adaptive beamforming based on Gaussian distribution

One of the most well-known robust adaptive beamforming techniques is diagonal loading (DL). The main drawback of this approach is that the loading factor is hard to be reliably determined. Recently, the general linear combination (GLC)-based robust Capon beamformer is presented to fully automatically determine the loading factor only from the given data. In this paper, we propose an enhanced GLC-based beamforming technique based on the Gaussian assumption. It provides an analytic solution of the loading factor, which can be calculated more efficiently. The simulations show that it decreases the computational load without the degradation of performance.