1. College of Information Science & Technology, Southwest Jiaotong University Chengdu 610031, China; 2. Chengdu Hua Ri Communication Technology Co. Ltd., Chengdu 610045, China
Abstract：In order to increase the generality and accuracy of radio modulation recognition in complex radio propagation environment，a multiple feature combined convolutional network system based on deep learning is proposed. Carrier features were detected with front convolutional network in the first stage. Then，the signal filtered by the front CNN was converted into spectrograms with the proposed pre-process method. Finally，the lightweight backend convolutional network was designed to extract the time-frequency features of spectrograms. The networks，which run on TensorFlow，achieved 99.23% accuracy with real airport communication signals. The experiment indicates that the proposed networks could be applied in real-time airport radio detection.
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