Deep Autoencoders for DOA Estimation of Coherent Sources using Imperfect Antenna Array
| Autor: | Omar Eissa, Aya Mostafa Ahmed, Aydin Sezgin |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences business.industry Covariance matrix Computer science Computer Science - Information Theory Information Theory (cs.IT) Deep learning Signal Autoencoder Antenna array FOS: Electrical engineering electronic engineering information engineering Overhead (computing) Artificial intelligence Electrical Engineering and Systems Science - Signal Processing business Algorithm Decoding methods Smoothing |
| Zdroj: | 2020 Third International Workshop on Mobile Terahertz Systems (IWMTS). |
| Popis: | In this paper a robust algorithm for DOA estimation of coherent sources in presence of antenna array imperfections is presented. We exploit the current advances of deep learning to overcome two of the most common problems facing the state of the art DOA algorithms (i.e. coherent sources and array imperfections). We propose a deep auto encoder (AE) that is able to correctly resolve coherent sources without the need of spatial smoothing, hence avoiding possible processing overhead and delays. Moreover, we assumed the presence of array imperfections in the received signal model such as mutual coupling, gain/ phase mismatches, and position errors. The deep AE is trained using the covariance matrix of the received signal, where it alleviates the effect of imperfections, and at the same time act as a filters for the coherent sources. The results show significant improvement compared to the methods used in the literature. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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