DOA estimation of coherent sources based on deep learning

Shuhan Guo; Guoping Hu; Fangzheng Zhao; Xuchen Gao; Hao Zhou; Yule Zhang

doi:10.1117/12.3005020

20 October 2023 DOA estimation of coherent sources based on deep learning

Shuhan Guo, Guoping Hu, Fangzheng Zhao, Xuchen Gao, Hao Zhou, Yule Zhang

Proceedings Volume 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023); 129161V (2023) https://doi.org/10.1117/12.3005020
Event: Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 2023, Kunming, China

Abstract

Aiming at the problem of Direction of Arrival (DOA) estimation of coherent mixed targets in the far field of uniform linear array, the deep convolutional neural network and deep convolutional self-encoder are designed by combining deep learning with DOA estimation of coherent sources. The deep convolutional encoder is trained by comparing the difference between the received covariance matrix of the independent source array and the received covariance matrix of the coherent source array under the same condition, so as to realize the process of decorrelation, and then DOA estimation is carried out. The simulation results show that both methods can extract spatial features sufficiently, improve the accuracy of DOA estimation and reduce the complexity of the algorithm, and the method based on deep convolutional self-encoder has better performance.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shuhan Guo, Guoping Hu, Fangzheng Zhao, Xuchen Gao, Hao Zhou, and Yule Zhang "DOA estimation of coherent sources based on deep learning", Proc. SPIE 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 129161V (20 October 2023); https://doi.org/10.1117/12.3005020

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KEYWORDS

Convolution

Signal to noise ratio

Covariance matrices

Deep convolutional neural networks

Deep learning

Monte Carlo methods

Computer simulations

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