A method for recognition and classification for hybrid signals based on Deep Convolutional Neural Network

This paper presents a method for hybrid radar and communication signal recognition and classification based on Deep Convolutional Neural Network (DCNN) for feature extraction and classification. The main idea is to transform modulation mode into time-frequency map for recognition. To overcome the single input single output (SISO) characteristic of DCNN to output multi tags for hybrid signals, we propose a repeated selective strategy that segments the time-frequency map and classifies the selective regions by utilizing DCNN repeatedly. The experiments compare traditional methods (ANN and SVM, which can only satisfy the classification of single signals) with our method and show that the DCNN with short-time Fourier transform (STFT) performs better and more stable in single signals and achieve a classification accuracy over 92% at 0 dB and over 98% at 5 dB in hybrid signals.