Radar Signal Modulation Type Recognition Based on Double CNN
JIN Lijie， WU Yatao
Nanjing Research Institute of Electronics Technology， Nanjing 210039， JiangSu， China
Abstract In view of the complexity of radar waveform and the decline of radar emitter signal recognition accuracy based on conventional pulse characteristics, a network structure of double CNN is proposed to realize the classification and recognition of 9 common radar signals. When using a single CNN structure, four modulation types can be identified accurately, but the recognition accuracy of phase coding and its composite modulation signals is intolerable . Because the time-frequency characteristics of BPSK and QPSK are similar. This paper adopts the processing method of double CNN structure, which has strong adaptability. Radar signals can still be classified and recognized when modulation parameters are not fixed. The simulation results show that the recognition accuracy of 9 modulation signals is higher than 95% when the signal-to-noise ratio (SNR) is 0 dB. Finally, the reliability of this method is verified by analyzing the relationship between recognition accuracy and SNR.
Key words ：
modulation type recognition
CNN
phase encoding
recognition accuracy
deep learning
Received: 10 November 2021
Published: 25 March 2022
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2022, Vol. 5 
