Accurately reconstructing a three-dimensional (3D) ocean sound speed field (SSF) is essential for various ocean acoustic applications, but the sparsity and uncertainty of sound speed samples across a vast ocean region make it a challenging task. To tackle this challenge, a large body of reconstruction methods has been developed, including spline interpolation, matrix/tensor-based completion, and deep neural networks (DNNs)-based reconstruction. However, a principled analysis of their effectiveness in 3D SSF reconstruction is still lacking. This paper performs a thorough analysis of the reconstruction error and highlights the need for a balanced representation model that integrates expressiveness and conciseness. To meet this requirement, a 3D SSF-tailored tensor DNN is proposed, which uses tensor computations and DNN architectures to achieve remarkable 3D SSF reconstruction. The proposed model not only includes the previous tensor-based SSF representation model as a special case but also has a natural ability to reject noise. The numerical results using the South China Sea 3D SSF data demonstrate that the proposed method outperforms state-of-the-art methods. The code is available at https://github.com/OceanSTARLab/Tensor-Neural-Network.

Underwater gliders travel through the ocean by buoyancy control, which makes their motion silent and involves low energy consumption. Due to those advantages, numerous studies on underwater acoustics have been carried out using gliders and different acoustic payloads have been developed. This paper aims to illustrate the use of gliders in underwater acoustic observation and target detection through experimental data from two sea trials. Firstly, the self-noise of the glider is analyzed to illustrate its feasibility as an underwater acoustic sensing platform. Then, the ambient noises collected by the glider from different depths are presented. By estimating the transmission loss, the signal receiving ability of the glider is assessed, and a simulation of target detection probability is performed to show the advantages of the glider over other underwater vehicles. Moreover, an adaptive line enhancement is presented to further reduce the influence of self-noise. Meanwhile, two hydrophones are mounted at both ends of the glider to form a simple array with a large aperture and low energy consumption. Thus, the target azimuth estimation is verified using broadband signals, and a simple scheme to distinguish the true angle from the port‒starboard ambiguity is presented. The results indicate that the glider does have advantages in long-term and large-scale underwater passive sensing.

Reconstructing ocean sound speed field (SSF) from limited and noisy measurements/estimates is crucial for many ocean acoustic applications, including underwater tomography, target localization/tracking, and communications. Classical reconstruction methods include deterministic approaches (e.g., spline interpolation) and geostatistical methods (e.g., kriging). They exhibit a strong link to linear regression and Gaussian process regression in machine learning (ML) literature, by uniformly viewing them as supervised regression models that learn the mapping from the geographical locations to the sound speed outputs. From a unified ML perspective, theoretical analysis indicates that classical reconstruction methods have several drawbacks, such as the sensitivity to noises and high computational cost. To overcome these drawbacks, inspired by the recent thriving development of graph machine learning, we introduce graph-guided Bayesian low-rank matrix completions (LRMCs) for fine-scale and accurate ocean SSF reconstruction. In particular, a more general graph-guided LRMC model is proposed that encompasses the state-of-the-art one as a special case. The proposed model and the associated inference algorithm simultaneously exploit the global (low-rankness) and local (graph structure) information of ocean sound speed data, thus striking an outstanding balance of reconstruction accuracy and computational complexity. Numerical results using real-life ocean SSF data have demonstrated the encouraging performances of the proposed approaches.

Underwater gliders are widely used in ocean observation with long operational endurance. Since gliders use buoyancy to generate gliding motion through water columns with a pair of wings, they are susceptible to ocean currents. With the limited navigation capability, the position deviation is usually quite large. To improve the localization accuracy of the glider, a passive acoustic method is proposed, in which a sound source periodically sends signals and two hydrophones mounted on the glider are employed to receive signals. The extended Kalman filter is developed to estimate the positions of gliders and the depth-dependent currents using the time difference of arrival between the signal receptions and the direction of signal arrival. Both the hydrophones and the source are assumed as nonsynchronous, and to obtain the direction of arrival, an algorithm using the self-noise to synchronize two hydrophones mounted on the glider is proposed. Simulations with different scenarios are carried out and data from a field experiment performed at the South China Sea are processed to demonstrate the method.

This paper presents a tracking technique for performing sequential geoacoustic inversion monitoring range-independent environmental parameters in shallow water. The inverse problem is formulated in a state-space model with a state equation for the time-evolving sound speed profile (SSP) and a measurement equation that incorporates acoustic measurements via a hydrophone array. The particle filter (PF) is an ideal algorithm to perform tracking of environmental parameters for nonlinear systems with non-Gaussian probability densities. However, it has the problem of the mismatch between the proposal distribution and the a posterior probability distribution (PPD). The ensemble Kalman filter (EnKF) can obtain the PPD based on the Bayes theorem. A tracking algorithm improves the performance of the PF by employing the PPD of the EnKF as the proposal distribution of the PF. Tracking capabilities of this filter, the EnKF and the PF are compared with synthetic acoustic pressure data and experimental SSP data. Simulation results show the proposed method enables the continuous tracking of the range-independent SSP and outperforms the PF and the EnKF. Moreover, the complexity analysis is performed, and the computational complexity of the proposed method is greatly increased because of the combination of the PF and the EnKF.

The probability distribution of three-dimensional sound speed fields (3D SSFs) in an ocean region encapsulates vital information about their variations, serving as valuable data-driven priors for SSF inversion tasks. However, learning such a distribution is challenging due to the high dimensionality and complexity of 3D SSFs. To tackle this challenge, we propose employing the diffusion model, a cutting-edge deep generative model that has showcased remarkable performance in diverse domains, including image and audio processing. Nonetheless, applying this approach to 3D ocean SSFs encounters two primary hurdles. First, the lack of publicly available well-crafted 3D SSF datasets impedes training and evaluation. Second, 3D SSF data consist of multiple 2D layers with varying variances, which can lead to uneven denoising during the reverse process. To surmount these obstacles, we introduce a novel 3D SSF dataset called 3DSSF, specifically designed for training and evaluating deep generative models. In addition, we devise a high-capacity neural architecture for the diffusion model to effectively handle variations in 3D sound speeds. Furthermore, we employ state-of-the-art continuous-time-based optimization method and predictor-corrector scheme for high-performance training and sampling. Notably, this paper presents the first evaluation of the diffusion model's effectiveness in generating 3D SSF data. Numerical experiments validate the proposed method's strong ability to learn the underlying data distribution of 3D SSFs, and highlight its effectiveness in assisting SSF inversion tasks and subsequently characterizing the transmission loss of underwater acoustics.