Affiliation(s): 1State Key Lab of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China;
moreAffiliation(s): 1State Key Lab of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China; 2Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang, China;
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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 masking filtering method is designed, which uses image technology to obtain the time-frequency contour 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 simulation and lake experiments. In the simulation experiments, a recognition accuracy of 90% is achieved at an SNR of 0 dB in multipath channels, and in the real underwater communication environment, a recognition accuracy of 80% is achieved at a symbol width of 50 ms and an SNR of 6.36 dB.
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