Radar emitter recognition based on CNN and LSTM

Han Liu; Donghang Cheng; Xiaojun Sun; Feng Wang

doi:10.1117/12.2615142

15 October 2021 Radar emitter recognition based on CNN and LSTM

Han Liu, Donghang Cheng, Xiaojun Sun, Feng Wang

Proceedings Volume 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering; 119331T (2021) https://doi.org/10.1117/12.2615142
Event: 2021 International Conference on Neural Networks, Information and Communication Engineering, 2021, Qingdao, China

Abstract

To solve the problem of low recognition efficiency under the low SNR of radar emitter signal recognition, CNN and LSTM are used to realize the recognition of signals in different intra-pulse modulation modes. Firstly, this paper achieves the local characteristics of the signals with CNN. Then capture the global characteristics with LSTM. Finally, construct the logical regression classification to complete the classification and recognition task. The simulation results show that when the SNR is -6dB, the overall recognition accuracy can reach 98%, and when the SNR is greater than -2dB, the accuracy rate is up to 100%. To verify the effect of different CNN layers and LSTM layers, comparative experiments are carried out. The results show that the appropriate increase of convolution layers is beneficial to improve the accuracy, while the lack of LSTM was not conducive to classification and recognition.

Citation Download Citation

Han Liu, Donghang Cheng, Xiaojun Sun, and Feng Wang "Radar emitter recognition based on CNN and LSTM", Proc. SPIE 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering, 119331T (15 October 2021); https://doi.org/10.1117/12.2615142

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