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Phase enhancement model based on supervised convolutional neural network for coherent DOA estimation

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Abstract

When the elevation of targets is smaller than beamwidth, the coherent multi-path signals will significantly degrade the direction of arrival (DOA) estimation accuracy of existing methods for a very-high-frequency (VHF) radar system. Through detailed theoretical analysis, we demonstrate that the phase distortion is the key factor of degrading the accuracy of DOA estimation. Hence, a novel phase enhancement model based on supervised convolutional neural network (CNN) for coherent DOA estimation is proposed to mitigate the phase distortion and improve estimation accuracy. The results of simulation experiments and real data have demonstrated the superiority of proposed method in DOA estimation accuracy and resolution compared to classic physics-driven methods. Moreover, the proposed scheme is suitable for the coherent DOA estimation compared with existing data-driven methods.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61571344, No. 61971323), Fundamental Research Funds for the Central Universities and the Innovation Fund of Xidian University. The authors sincerely express their gratitude to anonymous reviewers and editors for their helpful and constructive comments and suggestions.

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  1. National Laboratory of Radar Signal Processing, Xidian University, Xi’an, 710071, China

    Houhong Xiang, Baixiao Chen, Ting Yang & Dong Liu

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  1. Houhong Xiang
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  2. Baixiao Chen
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  3. Ting Yang
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  4. Dong Liu
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Correspondence to Baixiao Chen.

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Xiang, H., Chen, B., Yang, T. et al. Phase enhancement model based on supervised convolutional neural network for coherent DOA estimation. Appl Intell 50, 2411–2422 (2020). https://doi.org/10.1007/s10489-020-01678-4

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