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

Abstract

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.