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This paper uses GNU Radio and Universal Software Radio Peripherals to generate 10 classes of close\u2010to\u2010real multipulse radar signals, namely, Barker, Chaotic, EQFM, Frank, FSK, LFM, LOFM, OFDM, P1, and P2. In order to obtain the time\u2010frequency image (TFI) of the multipulse radar signal, the signal is Choi\u2013Williams 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\u2010Net deep learning model based on this module. The model has relatively few parameters and calculations. The experiments were carried out at the signal\u2010to\u2010noise ratio (SNR) of \u221214 \u223c 4\u2009dB. In the case of \u22126\u2009dB, the recognition result of HRF\u2010Net reached 99.583% and the recognition result of the network still reached 97.500% under \u221214\u2009dB. 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