子阵级LCMV循环优化自适应波束形成算法研究  

Research on sub-array LCMV cyclic optimization adaptive beamforming algorithm

作　　者：肖宇彤 周渊平[1] 肖骏 周鑫 Xiao Yutong;Zhou Yuanping;Xiao Jun;Zhou Xin(School of Electronic & Information Engineering,Sichuan University,Chengdu 610065,China)

机构地区：[1]四川大学电子信息学院

出　　处：《电子技术应用》2019年第7期67-71,71,共5页Application of Electronic Technique

基　　金：国家自然科学基金项目(61831004)

摘　　要：在传统LCMV波束形成器以及子阵空间部分自适应阵的基础上,提出了一种新颖的降维方法。首先将大规模阵列按照子阵划分的某种规则划分为若干组子阵列,每一组子阵列使用相同的权值。在权值优化过程中,每一次只更新权向量的一部分,通过多次迭代更新使系统搜索得到最优权值,避免了全维相关矩阵的求逆运算。实验结果表明,与传统方法相比,该方法在大规模阵列波束形成时能够获得更高的信干噪比,并减小了求逆矩阵的维数,在一定程度上降低了计算复杂度及硬件成本。In this paper, based on the traditional LCMV beamformer and sub-array space partial adaptive array, a novel dimensionality reduction method is proposed. Firstly, the large-scale array is divided into several groups of sub-arrays according to a certain rule of sub-array division, and each group of sub-arrays uses the same weight. In the process of weight optimization, each time only a part of the weight vector is updated, the system search obtains the optimal weight through multiple iterations, which avoids the inversion of the full-dimensional correlation matrix. The experimental results show that compared with the traditional method, this method can obtain higher signal to interference and noise ratio and reduce the dimensionality of the inversion matrix in large-scale array beamforming, which reduces the computational complexity and hardware cost to a certain extent.

关 键 词：大规模阵列 波束形成 LCMV算法 部分自适应阵列 循环迭代 降维 

分 类 号：TN929.5[电子电信—通信与信息系统]