Affiliation:
1. College of Communication Engineering, Jilin University, Changchun 130012, China
Abstract
Intrapulse modulation classification of radar signals plays an important role in modern electronic reconnaissance, countermeasures, etc. In this paper, to improve the recognition rate at low signal-to-noise ratio (SNR), we propose a recognition method using the second-order short-time Fourier transform (STFT)-based synchrosqueezing transform (FSST2) combined with a modified convolution neural network, which we name MeNet. In particular, the radar signals are first preprocessed via the time–frequency analysis and STFT-based FSST2. Then, the informative features of the time–frequency images (TFIs) are deeply learned and classified through the MeNet with several specific convolutional blocks. The simulation results show that the overall recognition rate for seven types of intrapulse modulation radar signals can reach 95.6%, even when the SNR is −12 dB. Compared with other networks, the excellent recognition rate proves the superiority of our method.
Funder
Natural Science Foundation of Jilin Province
National Natural Science Foundation of China
Reference17 articles.
1. Kawalec, A., and Owczarek, R. (2004, January 17–19). Radar emitter recognition using intrapulse data. Proceedings of the 15th International Conference on Microwaves, Radar and Wireless Communications (IEEE Cat. No.04EX824), Warsaw, Poland.
2. Yuan, S., Wu, B., and Li, P. (2021). Intra-pulse modulation classification of radar emitter signals based on a 1-D selective rernel convolutional neural network. Remote Sens., 13.
3. Fusion image based radar signal feature extraction and modulation recognition;Gao;IEEE Access,2019
4. Zhang, M., Liu, L., and Diao, M. (2016). LPI radar waveform recognition based on time–frequency distribution. Sensors, 16.
5. Self-attention bi-LSTM networks for radar signal modulation recognition;Wei;IEEE Trans. Microw. Theory Techn.,2021