This paper proposes a radar emitter signal recognition method based on time-frequency analysis and dilated residual network (DRN) to solve the problem of difficulty in feature extraction and low accuracy in recognition of complex multiple radar emitter signals under low signal-to-noise ratio (SNR). Firstly

the signal time-domain waveform is transformed into a two-dimensional time-frequency image by time-frequency analysis to reflect the essential characteristics of signal. Then the time-frequency image pre-processing is carried out to retain the time-frequency image complete information and adapt to the deep learning model input. Finally

the DRN is constructed to automatically extract the signal time-frequency image features and realize the recognition of radar emitter signal. Experimental results show that when the SNR is -6dB

the overall recognition rate of the proposed method for 16 types of radar signals can reach 98.2%

and the overall recognition rate for time-frequency image similar to linear frequency modulation (LFM) signals is more than 95%. In this paper

a new intelligent recognition method for radar emitter signal is presented

which has nice engineering application prospects.