大规模阵列Kronecker稳健波束形成器

Kronecker robust adaptive beamformer for large array

针对大规模阵列对样本需求量大、计算复杂度高的问题,提出一种应用于大规模阵列的Kronecker自适应稳健波束形成器。首先,将期望信号导向矢量分解成两个导向矢量的Kronecker乘积,将原始导向矢量的失配问题转化为两个低维导向矢量的失配问题;然后,基于最坏情况性能最优原理建立双二次代价函数,并利用双迭代算法求解该代价函数,每次迭代过程只需求解两个低维的二阶锥规划问题。理论分析和仿真实验结果表明,与传统全维稳健算法相比,所提方法能够有效降低计算复杂度和样本需求量,与现有的降维稳健算法相比,由于具有更多自由度,所提方法具有更高的输出信干噪比。

To solve the problems of the requirement of a large number of samples and high computational complexity for large array,a Kronecker robust adaptive beamformer is proposed in this paper.Firstly,the steering vector of the desired signal is decomposed into the Kronecker product of two low-dimension steering vectors,and the original steering vector mismatch problem is transformed into the corresponding two low-dimension steering vectors mismatch problem.Secondly,the bi-quadratic cost function is established based on the worst-case performance optimization principle,which is then solved by using the bi-iterative algorithm(BIA).Only two low-dimension second-order cone programming(SOCP)problems need to be solved in per iteration.Theoretic analysis and simulations results show that compared with the conventional full-dimension robust algorithms,the samples required and computational complexity are reduced efficiently in the proposed approach.In addition,the higher output signal to interference plus noise ratio(SINR)is obtained for the higher degrees of freedom(DoFs)compared with the existing reduced-dimension robust algorithms.