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Direction of Arrival Estimation of Array Defects Based on Deep Neural Network

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Abstract

For the case where the antenna array has defects, most traditional direction of arrival (DOA) estimation methods are poorly adapted. In this paper, a DOA estimation method based on residual neural networks (ResNets) is introduced to obtain better adaptability to the defects of the antenna array and improve the generalization ability to unknown scenes. The framework of deep neural network mainly includes two parts: spatial classification networks (SCN) and ResNets. Firstly, the received signals are divided into corresponding signal space subregions, this operation can relieve the generalization burden of the ResNet that follows. Then, the output of the SCN is used as the input of several parallel ResNets, and each of ResNet judges whether there are signal components in the preset grid of the corresponding subspace regions. Finally, the results of all ResNets are combined into a spatial spectrum, and a spectrum peak search is performed to obtain the estimated direction of the signal. A large number of simulation results are available to confirm that the proposed method not only has excellent generalization ability, but also has high estimation accuracy under different array defects.

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The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 51877151, Tianjin Municipal Natural Science Foundation under Grant 18JCZDJC99900 and the Program for Innovative Research Team in University of Tianjin under Grant TD13-5040.

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Tiangong University, Tianjin, 300387, China

    Jianxiong Li, Xingkai Shao, Jie Li & Lijun Ge

  2. Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin, 300387, China

    Jianxiong Li, Xingkai Shao, Jie Li & Lijun Ge

Authors
  1. Jianxiong Li
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  2. Xingkai Shao
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  3. Jie Li
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Correspondence to Jie Li.

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Li, J., Shao, X., Li, J. et al. Direction of Arrival Estimation of Array Defects Based on Deep Neural Network. Circuits Syst Signal Process 41, 4906–4927 (2022). https://doi.org/10.1007/s00034-022-02011-9

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  • DOI: https://doi.org/10.1007/s00034-022-02011-9

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