A DOA estimation method for array signals based on deep learning

Jiayi Yan; Qinglong Bao; Ning Zhong

doi:10.1117/12.3005936

10 October 2023 A DOA estimation method for array signals based on deep learning

Jiayi Yan, Qinglong Bao, Ning Zhong

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127991M (2023) https://doi.org/10.1117/12.3005936
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

The traditional Direction of Arrival (DOA) estimation algorithms are based on model parameters, which depends on the accuracy of the array model. When the array model has errors, the matching between the model and the data will fail, which affects the estimation performance to some extent. Therefore, this paper constructs the nonlinear relationship between the received signal and its spatial spectrum through the neural network framework, and uses the data-driven of deep learning to realize the DOA estimation. The neural network consists of an autoencoder network and multiple parallel 1-D VGG networks to achieve spatial spectrum estimation of the angle region. The simulation results show that the DOA estimation method proposed in this paper has good generalization ability, and also show good robustness under array error conditions.

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

Citation Download Citation

Jiayi Yan, Qinglong Bao, and Ning Zhong "A DOA estimation method for array signals based on deep learning", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127991M (10 October 2023); https://doi.org/10.1117/12.3005936

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KEYWORDS

Error analysis

Neural networks

Education and training

Computer simulations

Deep learning

Machine learning

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