Novel Approach to 2D DOA Estimation for Uniform Circular Arrays Using Convolutional Neural Networks

Dong Chen,Younghoon Joo,Raffaele Solimene

doi:10.1155/2021/5516798

Dong Chen, Younghoon Joo + Show 1 more

Open Access

https://doi.org/10.1155/2021/5516798

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Abstract

This paper presents a novel efficient high-resolution two-dimensional direction-of-arrival (2D DOA) estimation method for uniform circular arrays (UCA) using convolutional neural networks. The proposed 2D DOA neural network in the single source scenario consists of two levels. At the first level, a classification network is used to classify the observation region into two subregions (0°, 180°) and (180°, 360°) according to the azimuth angle degree. The second level consists of two parallel DOA networks, which correspond to the two subregions, respectively. The input of the 2D DOA neural network is the preprocessed UCA covariance matrix, and its outputs are the estimated elevation angle to be modified by postprocessing and the estimated azimuth angle. The purpose of the postprocessing is to enhance the proposed method’s robustness to the incident signal frequency. Moreover, in the inevitable array imperfections scenario, we also achieve 2D DOA estimation via transfer learning. Besides, although the proposed 2D DOA neural network can only process one source at a time, we adopt a simple strategy that enables the proposed method to estimate the 2D DOA of multiple sources in turn. Finally, comprehensive simulations demonstrate that the proposed method is effective in computation speed, accuracy, and robustness to the incident signal frequency and that transfer learning could significantly reduce the amount of required training data in the case of array imperfections.

Highlights

Direction-of-arrival (DOA) estimation, as one of the crucial technologies in antenna array systems, has been widely applied in many fields, such as sonar, seismology, radar, and mobile communication [1,2,3]. e multiple signal classification (MUSIC) [4, 5] and estimation of signal parameter via rotational invariance technique (ESPRIT) [6, 7] are two classic conventional one-dimensional (1D) DOA estimation algorithms
We propose a 2D DOA estimation model consisting of three modules: preprocessing, 2D DOA neural network, and postprocessing. e preprocessing provides appropriate input features for the 2D DOA neural network. e 2D DOA neural network outputs the estimated elevation and azimuth angle. e postprocessing modifies the elevation angle output by the 2D DOA neural network
E main contributions of this paper are as follows: (1) DOA estimation for uniform circular arrays (UCA) using convolutional neural networks (CNN) is extended from 1D to 2D; (2) the robustness of the proposed method to the incident signal frequency is effectively improved by simple postprocessing; (3) the feasibility of using transfer learning to reduce the amount of training data in DOA estimation is verified

Summary

Introduction

Direction-of-arrival (DOA) estimation, as one of the crucial technologies in antenna array systems, has been widely applied in many fields, such as sonar, seismology, radar, and mobile communication [1,2,3]. e multiple signal classification (MUSIC) [4, 5] and estimation of signal parameter via rotational invariance technique (ESPRIT) [6, 7] are two classic conventional one-dimensional (1D) DOA estimation algorithms. References [12, 17,18,19] adopt the strategy of taking only the first row of the preprocessed array covariance matrix as the input feature vector while ignoring noise interference on each element of the covariance matrix. References [21,22,23] use CNN to achieve 1D DOA estimation based on UCA and ULA, respectively, and obtain satisfactory results. E main contributions of this paper are as follows: (1) DOA estimation for UCA using CNN is extended from 1D to 2D; (2) the robustness of the proposed method to the incident signal frequency is effectively improved by simple postprocessing; (3) the feasibility of using transfer learning to reduce the amount of training data in DOA estimation is verified. Other terms used in the study follow the general notations unless otherwise stated

Preliminary and Problem Formulation

Simulation Results

Performance of the 2D DOA Estimation Model

Conclusions