Intelligent broadband detection method of communication signal based on deep learning

Aiming at the problem that multi-signal time-frequency domain overlapping is very likely to appear in the actual scene, it is difficult to identify, and a broadband signal intelligent detection method based on time-frequency analysis and target detection network is proposed. The communication signal is transformed into a time-frequency image, and the YOLOv5 network model is improved. Introduce the attention mechanism in the feature extraction network, highlight the signal area information, modify the K-Means clustering rules, recalculate the size of the prior frame, and use the improved model to study the time-frequency diagram of multiple signals overlapping. The results show that under different signal-to-noise ratios, the detection and parameter estimation of six types of random overlapping communication signals are realized. Overall, the signal detection probability reaches 94.74%, the false alarm probability is 2.86%, and the average error of parameter estimation is 2.15%. The analysis results in this paper can provide support for the rapid and effective detection of signals in practical applications.