Whistle mimicking underwater acoustic communication with Nyquist constrained autoencoder learned whale waveforms

Biomimetic underwater acoustic communication is attractive for covert military operations because it disguises communication signals as marine-mammal whistles, but in modification-type whistle-mimicking schemes, concealment and communication reliability are tightly coupled and often trade off against each other. The proposed method addresses this problem by establishing a whale-whistle waveform-learning framework that extracts baseband pulses from real false killer whale whistles, learns Nyquist-constrained representative symbol waveforms through an autoencoder, and combines them with envelope-driven adaptive symbol durations to preserve whale-like waveform statistics while equalizing symbol energy. The proposed method is evaluated in terms of mimicry using Evaluation of Covertness of Sound Mimicking Marine Mammals (ECSM3) and higher-order cumulants, and in terms of communication performance using Bit Error Rate (BER) under Additive White Gaussian Noise, a simulated underwater channel, and 1.5 km sea trials against BOK, continuous varying carrier frequency modulation (CV-CFM), Hybrid Orthogonal Division Frequency Modulation (HODFM), and Variable Duartion Phase Shift Keying (V-DPSK) at matched data rates. The proposed method achieves the highest mean ECSM3 score with the smallest variation and the lowest BER among the same-rate schemes; in the 1.5 km sea trial at 250 bps, it attains a BER of 0.0245, which corresponds to about 72.8%, 79.4%, and 87.4% lower BER than V-DPSK, HODFM, and CV-CFM, respectively, demonstrating that learned whale-like pulses can simultaneously improve covertness and reliable underwater acoustic communication.