LPI Radar Signals Modulation Recognition in Complex Multipath Environment Based on Improved ResNeSt

Low probability of intercept (LPI) radar has played an important role in in modern radar systems due to its difficulty in being intercepted by noncooperative interceptors. The modulation methods of LPI radar signals are becoming increasingly complex, causing some difficulties in accurately identifying radar signals in complex electromagnetic environments. To address this problem, we propose an automatic recognition framework for LPI radar modulated signals in complex multipath electromagnetic environments. Specifically, the radar time-domain signal is converted into a time-frequency distribution image through time-frequency analysis technology, and then, adaptive filtering is performed using an adaptive network-based fuzzy inference system in the preprocessing stage to enhance the time-frequency characteristics of the signal under low signal-to-noise ratios (SNRs). In the automatic recognition stage of radar signal, the inherent characteristics of time-frequency images of signals are extracted by exploiting deep learning, and a split-attention networks combined with a joint loss function is designed, namely ResNeSt. The experimental results show that compared with the existing automatic modulation recognition methods for radar signals, this framework has higher recognition accuracy in complex electromagnetic environments and exhibits robustness against superimposed multipath effects. When the SNR is as low as -12 dB, the average probability of accurately identifying 15 typical LPI radar signals is 94.93%.