The side-scan sonar (SSS) is a critical sensor device used to explore underwater environments in the deep sea. Gathering SSS data, however, is an expensive and time-consuming task because it requires sensor towing and involves complicated field operations. Recently, deep learning has been making advances rapidly in the field of computer vision. Benefiting from this development, generative adversarial networks (GANs) have been demonstrated to produce realistic synthetic data of various types, including images and acoustics signals. In this letter, we propose a GAN-based semantic image synthesis model based on GAN that can generate high-quality SSS images at a low cost in less time. We evaluate the proposed model using both shallow and deep water SSS data sets that include a diverse range of imaging conditions. such as high and low sonar operating frequencies and different landscapes. The experimental results show that the proposed method can effectively generate synthesized SSS data characterized by the shape and style of real data, thereby demonstrating its promising potential for SSS data augmentation in diverse SSS relevant machine learning tasks.

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.

In underwater biomimetic covert communications, multiple whale whistles are mimicked for improving covertness. The conventional mimicking methods suffered from the trade-off between the mimicking performance and the bit error rate (BER). In this paper, we propose a continuous phase-multiple chirp modulation (CP-MCM) and a hybrid orthogonal whistle modulation (HOWM) to improve the BER and the mimicking performance of the biomimetic covert communication with the multiple whistles. The proposed CP-MCM utilizes chirp signals as the carriers of transmission symbols to mimic the frequency contour of the whistles accurately. The proposed HOWM utilizes the conventional OFDM in the overlapping whistle regions to avoid the interferences and the CP-MCM in the nonoverlapping regions to attain the large mimicking performance. Computer simulations and practical ocean experiments demonstrated that the proposed HOWM mimicked the multiple whistles with better BER performance than conventional methods.

Short-range biomimetic covert communications have been developed using dolphin whistles for underwater acoustic covert communications. Due to a channel characteristics difference by range, the conventional short-range methods cannot be directly applied to long-range communications. To enable long-range biomimicking communication, overcoming the large multipath delay and a high degree of mimic (DoM) in the low-frequency band is required. This paper proposes a novel biomimetic communication method that preserves a low bit-error rate (BER) with a large DoM in the low-frequency band. For the transmission, the proposed method utilizes the time-dependent frequency change of the whistle, and its receiver obtains additional SNR gain from the multipath delay. Computer simulations and practical ocean experiments were executed to demonstrate that the BER performance of the proposed method is better than the conventional methods. For the DoM assessment, the novel machine learning-based method was utilized, and the result shows that the whistles generated by the proposed method were recognized as the actual whistle of the right humpback whale.

Underwater acoustic (UWA) biomimicking communications have been developed for covert communications. For the UWA covert communications, it is difficult to achieve the bit error rate (BER) and the degree of mimic (DoM) performances at the same time. This paper proposes a biomimicking covert communication method to increase both BER and DoM (degree of mimic) performances based on the Time Frequency Shift Keying (TFSK). To increase DoM and BER performances, the orthogonality requirements of the time- and frequency-shifting units of the TFSK are theoretically derived, and the whistles are multiplied by the sequence with a large correlation. Two-step DoM assessments are also developed for the long-term whistle signals. Computer simulations and practical lake and ocean experiments demonstrate that the proposed method increases the DoM by 35% and attains a zero BER at −6 dB of Signal to Noise Ratio (SNR).