Synchronized perturbation elimination and DOA estimation via signal selection mechanism and parallel deep capsule networks in multipath environment  

作　　者：Ying CHEN Cong WANG Kunlai XIONG Zhitao HUANG 

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 [2]Institute of Information Fusion,Naval Aeronautical and Astronautical University,Yantai 264001,China

出　　处：《Chinese Journal of Aeronautics》2021年第12期158-170,共13页中国航空学报（英文版）

基　　金：supported by the Program for Innovative Research Groups of the Hunan Provincial Natural Science Foundation of China(No.2019JJ10004)。

摘　　要：State-of-the-art model-driven Direction-Of-Arrival(DOA)estimation methods for multipath signals face great challenges in practical application because of the dependence on the precise multipath model.In this paper,we introduce a framework,based on deep learning,for synchronizing perturbation auto-elimination with effective DOA estimation in multipath environment.Firstly,a signal selection mechanism is introduced to roughly locate specific signals to spatial subregion via frequency domain filters and compressive sensing-based method.Then,we set the mean of the correlation matrix’s row vectors as the input feature to construct the spatial spectrum by the corresponding single network within the parallel deep capsule networks.The proposed method enhances the generalization capability to untrained scenarios and the adaptability to non-ideal conditions,e.g.,lower SNRs,smaller snapshots,unknown reflection coefficients and perturbational steering vectors,which make up for the defects of the previous model-driven methods.Simulations are carried out to demonstrate the superiority of the proposed method.

关 键 词：Deep capsule network Direction-Of-Arrival(DOA)estimation Multipath propagation Parallel training Perturbation elimination 

分 类 号：TN911.7[电子电信—通信与信息系统]