基于低秩和互相关技术的鲁棒自适应波束形成算法设计
Design of Robust Adaptive Beamforming Algorithms Based on Low-Rank and Cross-Correlation Techniques
摘要：
这项工作提出了成本效益的低秩技术设计强大的自适应波束形成（RAB）算法。所提出的算法是基于利用阵列观测数据和波束形成器的输出之间的互相关。首先，我们构造了一个一般的线性方程，考虑在大尺寸的解决方案产生的导向矢量失配。然后，我们采用的思想，全正交化方法（FOM），正交Krylov子空间为基础的方法，迭代估计的导向矢量失配在降维子空间，从而在建议的正交Krylov子空间投影失配估计（OKSPME）方法。我们还设计了基于随机梯度（SG）和共轭梯度（CG）技术的自适应算法，以低复杂度更新波束形成权重，并避免任何昂贵的矩阵求逆。所提出的低秩和失配估计技术的主要优点是在处理高维子空间或大型传感器阵列时具有成本效益。仿真结果表明，在所有比较的RAB方法中，该波束形成器的输出信号与干扰加噪声比（SINR）表现出优异的性能。
英文摘要：
This work presents cost-effective low-rank techniques for designing robust adaptive beamforming (RAB) algorithms. The proposed algorithms are based on the exploitation of the cross-correlation between the array observation data and the output of the beamformer. Firstly, we construct a general linear equation considered in large dimensions whose solution yields the steering vector mismatch. Then, we employ the idea of the full orthogonalization method (FOM), an orthogonal Krylov subspace based method, to iteratively estimate the steering vector mismatch in a reduced-dimensional subspace, resulting in the proposed orthogonal Krylov subspace projection mismatch estimation (OKSPME) method. We also devise adaptive algorithms based on stochastic gradient (SG) and conjugate gradient (CG) techniques to update the beamforming weights with low complexity and avoid any costly matrix inversion. The main advantages of the proposed low-rank and mismatch estimation techniques are their cost-effectiveness when dealing with high dimension subspaces or large sensor arrays. Simulations results show excellent performance in terms of the output signal-to-interference-plus-noise ratio (SINR) of the beamformer among all the compared RAB methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
