A Multipulse Radar Signal Recognition Approach via HRF-Net Deep Learning Models.

In the field of electronic countermeasure, the recognition of radar signals is extremely important. This paper uses GNU Radio and Universal Software Radio Peripherals to generate 10 classes of close-to-real multipulse radar signals, namely, Barker, Chaotic, EQFM, Frank, FSK, LFM, LOFM, OFDM, P1, and P2. In order to obtain the time-frequency image (TFI) of the multipulse radar signal, the signal is Choi-Williams distribution (CWD) transformed. Aiming at the features of the multipulse radar signal TFI, we designed a distinguishing feature fusion extraction module (DFFE) and proposed a new HRF-Net deep learning model based on this module. The model has relatively few parameters and calculations. The experiments were carried out at the signal-to-noise ratio (SNR) of -14 ∼ 4 dB. In the case of -6 dB, the recognition result of HRF-Net reached 99.583% and the recognition result of the network still reached 97.500% under -14 dB. Compared with other methods, HRF-Nets have relatively better generalization and robustness.