Bibliographic Details
| Title: |
LPI radar emitter signals recognition in low SNR based on SE-ResNeXt network. (English) |
| Authors: |
XU Guiguang, WANG Xudong, WANG Fei, HU Guobing, GAO Yongxing, LUO Zehu |
| Source: |
Systems Engineering & Electronics; Dec2022, Vol. 44 Issue 12, p3676-3684, 9p |
| Subject Terms: |
RADAR, FEATURE extraction, SIGNAL-to-noise ratio, TIME-frequency analysis, DEEP learning, MULTICASTING (Computer networks) |
| Abstract: |
Aiming at the problem of low signal to noise ratio (SNR) and low probability of intercept (LPI) radar pulse waveform recognition accuracy, a radar emitter signal recognition method based on time-frequency analysis, squeeze-excitation (SE) and ResNeXt network is proposed. Firstly, the radar time domain signal is transformed into a two-dimensional time-frequency image (TFI) by Choi-Williams distribution (CWD); then, the TFI pre-processing is used to reduce the noise interference and the difference in frequency dimension location distribution, adapting to deep learning network input; finally, the TFI features are extracted by adding dilated convolution and SE structure on the basis of ResNeXt to achieve radar emitter classification. The experimental results show that when the SNR is as low as --8 dB, the overall recognition accuracy of the method for 12 types of common LPI radar waveforms can still reach 98. 08%. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |
