Super resolution DOA estimation based on deep neural network

Wanli Liu

doi:10.1038/s41598-020-76608-y

Abstract

Recently, deep neural network (DNN) studies on direction-of-arrival (DOA) estimations have attracted more and more attention. This new method gives an alternative way to deal with DOA problem and has successfully shown its potential application. However, these works are often restricted to previously known signal number, same signal-to-noise ratio (SNR) or large intersignal angular distance, which will hinder their generalization in real application. In this paper, we present a novel DNN framework that realizes higher resolution and better generalization to random signal number and SNR. Simulation results outperform that of previous works and reach the state of the art.

Highlights

Deep neural network (DNN) studies on direction-of-arrival (DOA) estimations have attracted more and more attention
Machine learning approach based on artificial neural network (ANN) has shown its potential application in DOA e stimation[11,12,13,14]
The performances of DOA estimation based on our proposed deep neural network (DNN) framework are investigated

Summary

Introduction

Deep neural network (DNN) studies on direction-of-arrival (DOA) estimations have attracted more and more attention. The more practical situation is that the signal number is unknown, so the DOA estimation naturally falls into a direction classification problem In this case, the observation space is separated into N subspaces and the activation values of N neurons in output layer represent the probability of a signal locating in each subspace. The observation space is separated into N subspaces and the activation values of N neurons in output layer represent the probability of a signal locating in each subspace This situation has been studied in Liu et al.’s w ork[28], where they propose a two-stage DNN structure that is characterized by a multitask autoencoder and a group of parallel multilayer classifiers. For the general reason that results often get better as DNN becomes deeper, we believe that a much deeper structure may boost the performance of DNN based DOA estimation

Methods

Results

Conclusion