Research on Intelligent Detection Method of Overlapping Multiple Signals in Time and Frequency Domain

In view of the existing intelligent signal detection methods based on deep learning theory, most of them can only detect single signal or signals which don’t overlap in time-frequency domains. This paper proposes a new intelligent detection method based on Mask R-CNN and Criminisi algorithm for time-frequency overlapping multi-signals. First, the signal in the time domain is transformed into a time-frequency image. Then, to solve the problem of missing pixels’ position information in the overlapping part of multiple signals in the time-frequency domain, the Criminisi algorithm to repair and fill the information is applied. Finally, Mask R-CNN is used for training the restored image, and used for detecting the unknown signals. Experimental results show that when the SNR is 0 dB, the average detection rate of overlapping signals in the time-frequency domain reaches 99%. Compared with the method based on convolutional neural network, the average detection rate is increased by more than 20%.