Deep learning-based Direction-of-arrival estimation for far-field sources under correlated near-field interferences

초록

This paper proposes a deep learning-based Direction-of-arrival (DOA) estimation to detect interfered far-field sources. The proposed method consists of a near-field interference rejection network (NFIRnet) and a DOA estimation network (DOAnet). The NFIRnet calculates the near-field components of the covariance matrix by convolutional neural networks with the proposed complex mapper. The near-field components are rejected from the covariance matrix. The DOAnet removes the residuals of the interferences by the proposed self-spatial attention network and estimates the DOAs of the interfered far-field sources. Computer simulations demonstrated that the proposed method had better DOA estimation performance than the conventional methods. (c) 2022 The Authors. Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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