Underdetermined DOA estimation via multiple time-delay covariance matrices and deep residual network  

作　　者：CHEN Ying WANG Xiang HUANG Zhitao 

机构地区：[1]State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System,National University of Defense Technology,Changsha 410073,China

出　　处：《Journal of Systems Engineering and Electronics》2021年第6期1354-1363,共10页系统工程与电子技术（英文版）

基　　金：supported by the Program for Innovative Research Groups of the Hunan Provincial Natural Science Foundation of China(2019JJ10004)。

摘　　要：Higher-order statistics based approaches and signal sparseness based approaches have emerged in recent decades to resolve the underdetermined direction-of-arrival(DOA)estimation problem.These model-based methods face great challenges in practical applications due to high computational complexity and dependence on ideal assumptions.This paper presents an effective DOA estimation approach based on a deep residual network(DRN)for the underdetermined case.We first extract an input feature from a new matrix calculated by stacking several covariance matrices corresponding to different time delays.We then provide the input feature to the trained DRN to construct the super resolution spectrum.The DRN learns the mapping relationship between the input feature and the spatial spectrum by training.The proposed approach is superior to existing model-based estimation methods in terms of calculation efficiency,independence of source sparseness and adaptive capacity to non-ideal conditions(e.g.,low signal to noise ratio,short bit sequence).Simulations demonstrate the validity and strong performance of the proposed algorithm on both overdetermined and underdetermined cases.

关 键 词：direction-of-arrival(DOA)estimation underdetermined condition deep residual network(DRN) time delay covariance matrix 

分 类 号：TN911.7[电子电信—通信与信息系统]