基于协方差矩阵锥化和导向矢量估计的鲁棒自适应波束形成算法  

Robust Adaptive Beamforming Based on CovarianceMatrix Taper and Steering Vector Estimation

作　　者：郭云舟 贾维敏 金伟 朱丰超 GUO Yunzhou;JIA Weimin;JIN Wei;ZHU Fengchao(Rocket Force University of Engineering,Xi'an 710025,China)

机构地区：[1]火箭军工程大学,西安710025

出　　处：《电光与控制》2020年第10期57-61,共5页Electronics Optics & Control

基　　金：国家自然科学基金(61601474)。

摘　　要：当数据中含有期望信号时,自适应波束形成对以导向矢量失配为代表的失配误差非常敏感,其性能急剧下降,这一情况在同时存在移动干扰时将更为严重。针对移动干扰和模型误差同时存在的情况,提出了一种基于协方差矩阵锥化和导向矢量估计的鲁棒自适应波束形成算法。该算法首先对协方差矩阵加权来增强协方差矩阵,然后利用增强后的协方差矩阵估计实际导向矢量,最后用增强协方差矩阵和估计后的导向矢量进行波束形成。仿真结果表明,所提算法在展宽零陷的同时也具有一定的抗模型误差能力,且提高了波束形成器对移动干扰和模型误差的鲁棒性。When the desired signal is present in training snapshots,the adaptive beamforming is very sensitive to the mismatch represented by the steering vector mismatch,and its performance degrades rapidly.This situation will be more serious when there is jammer in motion at the same time.To solve the problem,a robust adaptive beamforming algorithm based on covariance matrix taper and steering vector estimation is proposed.The algorithm weights the sample covariance matrix to enhance it,and then estimates the real steering vector by using the enhanced covariance matrix.Finally,the enhanced covariance matrix and the estimated steering vector are used for beamforming.The simulation results show that the proposed algorithm can overcome model mismatch while broadening the null notch,which improves the robustness of the beamformer to jammer in motion and model mismatch.

关 键 词：信号处理 自适应波束形成 协方差矩阵锥化 导向矢量估计 鲁棒性 

分 类 号：TN911.7[电子电信—通信与信息系统]