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In this paper a robust algorithm for DOA estimation of coherent sources inpresence of antenna array imperfections is presented. We exploit the currentadvances of deep learning to overcome two of the most common problems facingthe state of the art DOA algorithms (i.e. coherent sources and arrayimperfections). We propose a deep auto encoder (AE) that is able to correctlyresolve coherent sources without the need of spatial smoothing, hence avoidingpossible processing overhead and delays. Moreover, we assumed the presence ofarray imperfections in the received signal model such as mutual coupling, gain/phase mismatches, and position errors. The deep AE is trained using thecovariance matrix of the received signal, where it alleviates the effect ofimperfections, and at the same time act as a filters for the coherent sources.The results show significant improvement compared to the methods used in theliterature.

Deep Autoencoders for DOA Estimation of Coherent Sources using Imperfect Antenna Array