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IEICE Transactions on Communications
Online ISSN : 1745-1345
Print ISSN : 0916-8516
Special Section on Emerging Communication Technologies in Conjunction with Main Topics of ICETC2022
Deep Neural Networks Based End-to-End DOA Estimation System
Daniel Akira ANDOYuya KASEToshihiko NISHIMURATakanori SATOTakeo OHGANEYasutaka OGAWAJunichiro HAGIWARA
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JOURNAL FREE ACCESS

2023 Volume E106.B Issue 12 Pages 1350-1362

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Abstract

Direction of arrival (DOA) estimation is an antenna array signal processing technique used in, for instance, radar and sonar systems, source localization, and channel state information retrieval. As new applications and use cases appear with the development of next generation mobile communications systems, DOA estimation performance must be continually increased in order to support the nonstop growing demand for wireless technologies. In previous works, we verified that a deep neural network (DNN) trained offline is a strong candidate tool with the promise of achieving great on-grid DOA estimation performance, even compared to traditional algorithms. In this paper, we propose new techniques for further DOA estimation accuracy enhancement incorporating signal-to-noise ratio (SNR) prediction and an end-to-end DOA estimation system, which consists of three components: source number estimator, DOA angular spectrum grid estimator, and DOA detector. Here, we expand the performance of the DOA detector and angular spectrum estimator, and present a new solution for source number estimation based on DNN with very simple design. The proposed DNN system applied with said enhancement techniques has shown great estimation performance regarding the success rate metric for the case of two radio wave sources although not fully satisfactory results are obtained for the case of three sources.

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© 2023 The Institute of Electronics, Information and Communication Engineers
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