A method of jamming recognition based on multi-domain joint  convolutional neural network

Pengfei Fan; Yatao Wu; Yao Wei; Guoqiang Guo; Zhefan Peng

doi:10.1117/12.2634675

4 May 2022 A method of jamming recognition based on multi-domain joint convolutional neural network

Pengfei Fan, Yatao Wu, Yao Wei, Guoqiang Guo, Zhefan Peng

Proceedings Volume 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021); 1217202 (2022) https://doi.org/10.1117/12.2634675
Event: International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 2021, Nanchang, China

Abstract

Radar intelligence has become the development trend of radar, and accurate jamming recognition plays a vital role in the intelligent perception of the radar. With the input of multi-domain signals, including time domain, pulse compression, and frequency domain signals, a multi-domain joint convolutional neural network model was developed to classify different types of radar jamming. The results in this paper showed that when the jamming-to-noise ratio of the jamming was greater than 2 dB, the recognition accuracy rate of this model was over 94%. Compared with the one-dimensional convolutional neural network model, the recognition rate of multi-domain joint model proposed here was greatly improved, which, to a certain extent, has promoted the development of radar intelligence.

Citation Download Citation

Pengfei Fan, Yatao Wu, Yao Wei, Guoqiang Guo, and Zhefan Peng "A method of jamming recognition based on multi-domain joint convolutional neural network", Proc. SPIE 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 1217202 (4 May 2022); https://doi.org/10.1117/12.2634675

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