Research on radar target recognition method based on deep learning

Duanyang Shi; Qiang Lin; Bing Hu; Guochao Wang

doi:10.1117/12.2626642

16 December 2021 Research on radar target recognition method based on deep learning

Duanyang Shi, Qiang Lin, Bing Hu, Guochao Wang

Proceedings Volume 12153, International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2021); 121530D (2021) https://doi.org/10.1117/12.2626642
Event: International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2021), 2021, Sanya, China

Abstract

At present, after radar target detection, radar target recognition mainly depends on manual judgment. Manual identification relies too much on the operator's personal experience and subjective consciousness, which takes a long time and has a large error. To solve this problem, a radar target recognition method based on deep learning is proposed. By analyzing the differential characteristics of radar echoes of different targets, taking the range profile of radar echo sequence as the data set, a deep residual network classifier model is designed to classify and identify the types of radar targets. In order to improve the generalization ability of the network and avoid the over-fitting problem, the deep convolutional generative adversarial network is used to expand the range profile data set of radar echo sequence. In order to ensure the quality of the generated samples during data set expansion, the expanded samples are screened with the peak signal-to-noise ratio as the index. The experimental results of radar measured data show that this method has a good effect on radar target recognition.

Citation Download Citation

Duanyang Shi, Qiang Lin, Bing Hu, and Guochao Wang "Research on radar target recognition method based on deep learning", Proc. SPIE 12153, International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2021), 121530D (16 December 2021); https://doi.org/10.1117/12.2626642

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KEYWORDS

Radar

Target recognition

Convolution

Statistical modeling

Data modeling

Feature extraction

Image quality

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