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, PI, 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.
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