电讯技术2025，Vol.65Issue(5)：674-683,10.DOI:10.20079/j.issn.1001-893x.240521002
基于先验驱动残差注意力网络的阵元故障MIMO雷达DOA估计
DOA Estimation for MIMO Radar under Element Failure Based on Prior-driven Residual Attention Network
摘要
Abstract
Affected by factors such as harsh electromagnetic environment and aging components,the probability of antenna array element failure of multiple-input multiple-output(MIMO)radar increases,and array element failure will severely degrade the performance of Direction of arrival(DOA)estimation.Most existing deep learning-based DOA estimation methods fail to fully exploit the prior information of the array model,resulting in extremely complex mapping relationships,which makes network fitting difficult.To this end,a DOA estimation method for MIMO radar under element failure based on a prior-driven residual attention network is proposed.Firstly,by leveraging the doubly Toeplitz prior properties of the covariance matrix of MIMO radar,a prior-driven residual attention network is constructed.Residual attention blocks are introduced to weight the features of the covariance matrix.The network aims to learn the mapping relationship between the covariance matrix with missing data of the failed elements and the generation vector of the complete covariance matrix.Then,the complete covariance matrix is obtained from the generated vectors output by the network.Finally,the Reduced Dimension ESPRIT(RD-ESPRIT)algorithm is used to estimate the target DOA.Simulation results show that the proposed algorithm outperforms existing methods in DOA estimation under element failure,achieving a 43.26％accuracy improvement at a signal-to-noise ratio of 15 dB compared with the best existing algorithm.关键词
MIMO雷达/DOA估计/双重Toeplitz先验/残差网络/注意力机制Key words
MIMO radar/DOA estimation/doubly Toeplitz prior/residual network/attention mechanism分类
电子信息工程引用本文复制引用
陈金立,周龙,李家强,姚昌华..基于先验驱动残差注意力网络的阵元故障MIMO雷达DOA估计[J].电讯技术,2025,65(5):674-683,10.基金项目
国家自然科学基金资助项目(62071238) （62071238）
江苏省自然科学基金(BK20191399) （BK20191399）
