For passive sonar arrays, when far-field sources lie in a masking region caused by near-field interferences, direction-of-arrival (DOA) estimations of the interfered far-field sources become difficult. To mitigate the interference, we propose a near-field interference mitigation (NFIM) beamformer that utilizes subarrays and a proposed azimuth-domain filter without any source constraint. For the proposed method, this paper mathematically analyzes the masking region and the subarray configuration to separate the beams of the far-field source from the near-field interference and designs the proposed azimuth-domain filter in the proposed zeta domain. The suppression filter is implemented for all subarrays to mitigate the interferences, and the filtered outputs of all subarrays are combined to suppress residual interferences. Computer simulations show that the proposed NFIM beamformer mitigates the interferences and increases the DOA estimation performance compared with those of conventional methods.

In this paper, we propose a high resolution grid-based deep learning source localization that precisely estimates the locations of near-field sources without a priori knowledge of the number of sources. The proposed method consists of a principal component analysis network (PCAnet) and a spatial spectrum network (Sp2net). The proposed PCAnet calculates the noise spaces of the received signals by convolutional layers without a priori knowledge or the estimation of the number of sources and has the lower complexity than eigenvalue decomposition (EVD). The proposed Sp2net calculates the spatial spectrum with a linear layer from the output of the PCAnet and classifies dense location grids with a convolutional neural network (CNN). From the spatial spectrum, this paper also proposes an activation function to enlarge the values at the grid points where the near-field sources exist, which are differentiable for all input values. Then, the direction of arrivals (DOAs) and the ranges of the near-field sources are estimated with high resolution. Computer simulations demonstrated that the proposed method had better DOA and range estimation performances than those of the conventional methods.

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.

For covert underwater communications, conventional bio-mimicking methods have been developed. Signal shapes of these methods are similar to real whistles, but not the same since they divide a dolphin whistle into symbols, which are modulated by conventional digital modulations. This paper proposes a dolphin whistle mimicking modulation method that transmits different consecutive whistles with the same frequency contour of the real dolphin whistles and different time-frequency locations. This paper also develops a demodulator for decoding the proposed mimicking whistles. The mimicking performance of the proposed time-frequency shift keying was compared with those of the conventional methods by using correlation coefficients, and the proposed scheme showed the better mimicking performance than the conventional chirp spread spectrum and continuously varying-carrier frequency modulation. Using computer simulations and practical ocean experiments, the bit error rate performance of the proposed demodulator was better than that of the cross-correlation method.

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.

This paper proposes a whistle-generative adversarial network (WhistleGAN) that generates whistles for biomimetic underwater covert acoustic communication. The proposed method generates new whistles to maintain covertness by avoiding the repetitive use of the same whistles. Since the human ear perceives octave frequency such that low-frequency resolution is relatively larger than that of low frequencies, the proposed WhistleGAN uses mel filter banks to keep the fidelity in mimicking while reducing the complexity. The mean opinion score test verified that the whistles generated by the proposed method and the recorded real whistles have a similar score of 4.3, and the computer simulations proved that the bit error rate performance of the proposed method is the same as that of the real whistle.