Abstract: With the introduction of underwater bionic camouflage covert communication, conventional communication signal recognition methods can no longer meet the needs of current underwater military confrontations. However, the research on bionic communication signal recognition is still not comprehensive. This paper takes underwater communication signals that mimic dolphin whistles through phase-shifting modulation as the research object, and proposes a recognition method based on a convolutional neural network. A time‒frequency contour (TFC) masking filtering method is designed, which uses image technology to obtain the TFC mask of whistles and extracts whistles from the obtained mask. Spatial diversity combining is used to suppress the signal fading in multipath channels. The phase derivative spectrum image is obtained by Hilbert transform and continuous wavelet transform, and is then used as the basis for recognition. Finally, the effectiveness of the proposed method is verified by simulations and lake experiments. In the simulations, a recognition accuracy of 90% is achieved at a signal-to-noise ratio (SNR) of 0 dB in multipath channels. In the real underwater communication environment, a recognition accuracy of 81% is achieved at a symbol width of 50 ms and an SNR of 6.36 dB.

一种仿海豚哨声的移相调制水下通信信号识别方法

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