Radar jamming classification and recognition technology based on deep learning

Jingyi Wang; Wenhao Dong; Qiang Fu; Zhiyong Song

doi:10.1117/12.2600396

1 June 2021 Radar jamming classification and recognition technology based on deep learning

Jingyi Wang, Wenhao Dong, Qiang Fu, Zhiyong Song

Proceedings Volume 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021); 118480T (2021) https://doi.org/10.1117/12.2600396
Event: International Conference on Signal Image Processing and Communication (ICSIPC 2021), 2021, Chengdu, China

Abstract

Towed radar active jamming has attracted much attention in the military struggle of air-to-air combat because of its simple structure and remarkable efficiency. The radar jamming patterns are becoming more and more complicated. In order to realize the identification of towed decoys, it is necessary to classify and identify the jamming patterns. The difference between time-frequency images of different interference patterns is the key to classification and recognition. Deep learning provides classifiers for classification algorithms with its powerful image data processing capabilities. Therefore, in this paper, aiming at towed decoy interference, the convolutional neural network, which is good at image analysis in deep learning, is applied to the radar active interference pattern time-frequency image classification and recognition technology. The simulation experiment part uses convolutional neural network (ResNeXt residual network) to classify and verify two different interference patterns of dense false target interference and noise convolutional interference.

Citation Download Citation

Jingyi Wang, Wenhao Dong, Qiang Fu, and Zhiyong Song "Radar jamming classification and recognition technology based on deep learning", Proc. SPIE 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021), 118480T (1 June 2021); https://doi.org/10.1117/12.2600396

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