Direction of arrival estimation for coherent signals based on convolutional neural network with attention mechanism

Jiajie Qi; Yue Cui; Junfeng Wang; Aokun Kong; Feiyu Yang; Xiaoyuan Zhang

doi:10.1117/12.3031355

19 July 2024 Direction of arrival estimation for coherent signals based on convolutional neural network with attention mechanism

Jiajie Qi, Yue Cui, Junfeng Wang, Aokun Kong, Feiyu Yang, Xiaoyuan Zhang

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Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131818T (2024) https://doi.org/10.1117/12.3031355
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

A direction of arrival (DOA) estimation method for coherent signals is proposed based on an improved convolutional neural network (CNN) with attention mechanism. The real, imaginary and phase components of the covariance matrix are employed as CNN multi-channel inputs. A regression model is adopted as CNN output which avoids the grid mismatch problem caused by multi-classification output. Moreover, to enhance the accuracy of the estimation, attention mechanism is added to the CNN, which could give more weight to the main features effectively. The simulation results demonstrate that the proposed method surpasses the performance of other approaches, which is suitable to resolve coherent signals in multipath environments.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiajie Qi, Yue Cui, Junfeng Wang, Aokun Kong, Feiyu Yang, and Xiaoyuan Zhang "Direction of arrival estimation for coherent signals based on convolutional neural network with attention mechanism", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131818T (19 July 2024); https://doi.org/10.1117/12.3031355

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KEYWORDS

Physical coherence

Covariance matrices

Convolutional neural networks

Education and training

Computer simulations

Deep learning

Monte Carlo methods

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