Wideband Direction-of-Arrival Estimation Based on Deep Learning  

作　　者：Liya Xu Yi Ma Jinfeng Zhang Bin Liao 

机构地区：[1]Shenzhen University,Shenzhen 518060,China

出　　处：《Journal of Beijing Institute of Technology》2021年第4期412-424,共13页北京理工大学学报（英文版）

基　　金：the National Natural Sci-ence Foundation of China(No.62101340).

摘　　要：The performance of traditional high-resolution direction-of-arrival(DOA)estimation methods is sensitive to the inaccurate knowledge on prior information,including the position of ar-ray elements,array gain and phase,and the mutual coupling between the array elements.Learning-based methods are data-driven and are expected to perform better than their model-based counter-parts,since they are insensitive to the array imperfections.This paper presents a learning-based method for DOA estimation of multiple wideband far-field sources.The processing procedure mainly includes two steps.First,a beamspace preprocessing structure which has the property of fre-quency invariant is applied to the array outputs to perform focusing over a wide bandwidth.In the second step,a hierarchical deep neural network is employed to achieve classification.Different from neural networks which are trained through a huge data set containing different angle combinations,our deep neural network can achieve DOA estimation of multiple sources with a small data set,since the classifiers can be trained in different small subregions.Simulation results demonstrate that the proposed method performs well both in generalization and imperfections adaptation.

关 键 词：direction-of-arrival(DOA)estimation deep-neural network(DNN) WIDEBAND mul-tiple sources array imperfection 

分 类 号：TN911.7[电子电信—通信与信息系统] TP18[电子电信—信息与通信工程]