Machine Learning Based Biomimetic Underwater Covert Acoustic Communication Method Using Dolphin Whistle Contours

Jongmin Ahn; Hojun Lee; Yongcheol Kim; Wanjin Kim; Jaehak Chung

doi:10.3390/s20216166

Machine Learning Based Biomimetic Underwater Covert Acoustic Communication Method Using Dolphin Whistle Contours

Sensors (Basel). 2020 Oct 29;20(21):6166. doi: 10.3390/s20216166.

Authors

Jongmin Ahn¹, Hojun Lee¹, Yongcheol Kim¹, Wanjin Kim², Jaehak Chung¹

Affiliations

¹ Department of Electrical and Computer Engineering, INHA University, Incheon 22212, Korea.
² Agency of Defense Development, Jinhae-gu 51682, Korea.

Abstract

For underwater acoustic covert communications, biomimetic covert communications have been developed using dolphin whistles. The conventional biomimetic covert communication methods transmit slightly different signal patterns from real dolphin whistles, which results in a low degree of mimic (DoM). In this paper, we propose a novel biomimetic communication method that preserves the large DoM with a low bit error rate (BER). For the transmission, the proposed method utilizes the various contours of real dolphin whistles with the link information among consecutive whistles, and the proposed receiver uses machine learning based whistle detectors with the aid of the link information. Computer simulations and practical ocean experiments were executed to demonstrate the better BER performance of the proposed method. Ocean experiments demonstrate that the BER of the proposed method was 0.002, while the BER of the conventional Deep Neural Network (DNN) based detector showed 0.36.

Keywords: bio-mimetic; modulation; secure LPD/LPI communication; signal processing.