Open AccessNovel Approach to 2D DOA Estimation for Uniform Circular Arrays Using Convolutional Neural Networks
Author(s) -
Dong Chen,
Young Hoon Joo
Publication year - 2021
Publication title -
international journal of antennas and propagation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.282
H-Index - 37
eISSN - 1687-5877
pISSN - 1687-5869
DOI - 10.1155/2021/5516798
Subject(s) - robustness (evolution) , azimuth , direction of arrival , artificial neural network , computer science , convolutional neural network , algorithm , covariance matrix , pattern recognition (psychology) , artificial intelligence , mathematics , telecommunications , biochemistry , chemistry , geometry , antenna (radio) , gene
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.