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A method of radar target detection based on convolutional neural network

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Abstract

Radar target detection (RTD) is one of the most significant techniques in radar systems, which has been widely used in the field of military and civilian. Although radar signal processing has been revolutionized since the introduction of deep learning, applying deep learning in RTD is considered as a novel concept. In this paper, we propose a model for multitask target detection based on convolutional neural network (CNN), which works directly with radar echo data and eliminates the need for time-consuming radar signal processing. The proposed detection method exploits time and frequency information simultaneously; therefore, the target can be detected and located in multi-dimensional space of range, velocity, azimuth and elevation. Due to the lack of labeled radar complex data, we construct a radar echo dataset with multiple signal-to-noise ratio (SNR) for RTD. Then, the CNN-based model is evaluated on the dataset. The experimental results demonstrated that the CNN-based detector has better detection performance and measuring accuracy in range, velocity, azimuth and elevation and more robust to noise in comparison with traditional radar signal processing approaches and other state-of-the-art methods.

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Acknowledgements

This work was supported in part by the Natural Science Foundation of China under Grant 71671178, in part by the Equipment Advance Research Fund 6142502180101. It is also supported by the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, 101400, China

    Wen Jiang, Yihui Ren, Ying Liu & Jiaxu Leng

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  1. Wen Jiang
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  2. Yihui Ren
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  3. Ying Liu
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  4. Jiaxu Leng
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Correspondence to Wen Jiang.

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Jiang, W., Ren, Y., Liu, Y. et al. A method of radar target detection based on convolutional neural network. Neural Comput & Applic 33, 9835–9847 (2021). https://doi.org/10.1007/s00521-021-05753-w

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