TCSE-ResNet50 mixed-signal identification algorithm for joint spectrum and quartic spectrum

At present, the application of deep learning algorithm to the scene of modulation type identification mostly focuses on single digital modulation type identification, and rarely involves the identification of mixed digital and analog modulation types. At present, the signal characteristics used in the identification network are single, and the analog signal does not have the common identification characteristics such as cyclic spectrum and constellation diagram, so the existing composition method is not suitable for the identification of mixed digital-analog signal sets. In order to solve these problems, a TCSE-ResNet50 mixed-signal recognition algorithm combining the fourth power spectrum of frequency spectrum is proposed, and a feature map with wider feature applicability is formed by combining the signal spectrum and the fourth power spectrum. According to the attention mechanism module included in the proposed TCSE-ResNet50 network, the model pays more attention to discrete spectral lines and reduces the interference of other background areas or random noise on signal recognition as much as possible. At the same time, the cross entropy and triplet loss functions are combined, and the cross entropy is used to widen the characteristic distance between different kinds of signals with similar frequency domain expressions, and the triplet is used to narrow the characteristic distance between similar signals caused by random baseband symbols or random additive noise, thus completing the identification of {FM, AM, 2ASK, BPSK, 2FSK, 16QAM, 16APSK} digital-analog mixed signal sets. When the signal-to-noise ratio is -2dB, the average recognition rate of this algorithm is over 93%, which is superior to single feature input and traditional convolutional network recognition model.