By establishing a linear regression relationship between the projection coefficient of the empirical orthogonal function (EOF) of the sound speed profile (SSP) and remote sensing parameters of the sea surface, the single empirical orthogonal function regression (sEOF-r) method was used to reconstruct the underwater SSP from satellite remote sensing data. However, because the ocean is a complex dynamical system, the parameters of the surface and the subsurface did not conform to the linear regression model in strict sense. This paper proposes a self-organizing map (SOM)-based nonlinear inversion method that used satellite observations to obtain anomalies in data on the sea surface temperature and height, and combined them with the EOF coefficient from an Argo buoy to train and generate a map. The SSP was then reconstructed by obtaining the best matching neuron. The results of SSP reconstruction in the northern part of the South China Sea showed that the relationship between the parameters of the sea surface and the subsurface could be adequately expressed by the nonlinear neuronal topology. The SOM algorithm generated a smaller inversion error than linear inversion and had better robustness. It improved the average accuracy of reconstruction by 0.88 m/s and reduced the mean-squared reconstruction error to less than 1.19 m/s. It thus offered significant promise for acoustic applications.

In this study, an underwater source range estimation method based on unsupervised domain adaptation (UDA) is proposed. In contrast to traditional deep-learning frameworks using real-world data, UDA does not require labeling of the measured data, making it more practical. First, a classifier based on a deep neural network is trained with labeled simulated data generated using acoustic propagation models and, then, the adaptive procedure is applied, wherein unlabeled measured data are employed to adjust an adaptation module using the adversarial learning algorithm. Adversarial learning is employed to alleviate the marginal distribution divergence, which reflects the difference between the measured and theoretically computed sound field, in the latent space. This divergence, caused by environmental parameter mismatch or other unknown corruption, can be detrimental to accurate source localization. After the completion of the adaptive procedure, the measured and simulated data are projected to the same space, eliminating distribution discrepancy, which is beneficial for source localization tasks. Experimental results show that range estimation based on UDA outperforms the match-field-processing method under four scenarios of few snapshots, few array elements, low signal-to-noise ratio, and environmental parameter mismatch, verifying the robustness of the method.

The bearing-only tracking of an underwater uncooperative target can protect maritime territories and allows for the utilization of sea resources. Considering the influences of an unknown underwater environment, this work aimed to estimate 2-D locations and velocities of an underwater target with uncertain underwater disturbances. In this paper, an adaptive two-step bearing-only underwater uncooperative target tracking filter (ATSF) for uncertain underwater disturbances is proposed. Considering the nonlinearities of the target’s kinematics and the bearing-only measurements, in addition to the uncertain noise caused by an unknown underwater environment, the proposed ATSF consists of two major components, namely, an online noise estimator and a robust extended two-step filter. First, using a modified Sage-Husa online noise estimator, the uncertain process and measurement noise are estimated at each tracking step. Then, by adopting an extended state and by using a robust negative matrix-correcting method in conjunction with a regularized Newton-Gauss iteration scheme, the current state of the underwater uncooperative target is estimated. Finally, the proposed ATSF was tested via simulations of a 2-D underwater uncooperative target tracking scenario. The Monte Carlo simulation results demonstrated the reliability and accuracy of the proposed ATSF in bearing-only underwater uncooperative tracking missions.

Accurate 3D passive tracking of an underwater uncooperative target is of great significance to make use of the sea resources as well as to ensure the safety of our maritime areas. In this paper, a 3D passive underwater uncooperative target tracking problem for a time-varying non-Gaussian environment is studied. Aiming to overcome the low observability drawback inherent in the passive target tracking problem, a distributed passive underwater buoys observing system is considered and the optimal topology of the distributed measurement system is designed based on the nonlinear system observability analysis theory and the Cramer–Rao lower bound (CRLB) analysis method. Then, considering the unknown underwater environment will lead to time-varying non-Gaussian disturbances for both the target’s dynamics and the measurements, the robust optimal nonlinear estimator, namely the adaptive particle filter (APF), is proposed. Based on the Bayesian posterior probability and Monte Carlo techniques, the proposed algorithm utilizes the real-time optimal estimation technique to calculate the complex noise online and tackle the underwater uncooperative target tracking problem. Finally, the proposed algorithm is tested by simulated data and comprehensive comparisons along with detailed discussions that are made to demonstrate the effectiveness of the proposed APF.

Given that spatiotemporal measurement of the subsurface profile over a wide range are difficult to obtain, surface observations from satellites are often used to estimate the sound speed profile (SSP). This paper proposes a multisource method based on the self-organizing map (SOM) to improve the estimation of the SSP by merging surface observations with satellite data. Surface observations from the Kuroshio Extension Observatory (KEO) were used to supplement satellite observations (anomalies in the measured sea level and sea surface temperature) to this end. Different combinations of the surface parameters were assessed, their errors were analyzed, and differences between the results before and after the multisource parameters were used are discussed. The proposed method significantly increased the accuracy of estimating the SSP when the parameters obtained from in situ measurements were used, with a root mean square error of 2.18 m/s, less than a third of the error obtained when only satellite observations were used. The proposed method provides a new approach to determining an accurate three-dimensional structure of the sound speed when various surface observations are available.

The ambient noise field in the ocean is formed by a mixture of many source types, such as seismic noise, marine noise, shipping noise, and wind-generated noise. The correlation matrices of the ambient noise spectrum levels offer a promising approach for identifying and isolating the active source mechanisms in ambient noise datasets. This study attempts to use frequency correlation matrices to examine inter-frequency relationships and identify frequency bands that are dominated by specific sources present in samples of ambient noise. Deep-ocean ambient noise recorded by hydroacoustic monitoring systems in the East China Sea and South China Sea was used to analyze the frequency correlation characteristics over a broadband frequency range, with a focus on the contribution of wind-related and shipping-related source mechanisms to the overall ambient noise field at various frequencies. The primary sources observed in both the South China Sea and East China Sea were shipping sources and wind sources. During the observation window, wind was responsible for a significant amount of the ambient noise energy above 477 Hz in the East China Sea, while the corresponding frequency threshold was shifted up to 575 Hz in the South China Sea. Another interesting feature was also observed in the East China Sea: a higher degree of correlation was found between the levels at the wind noise dominated frequencies and mixture noise (mainly consisting of wind noise and shipping noise) dominated frequencies at the upper receiver than at the lower receiver. Data analysis results show that the cause of this effect is that shipping activity contributes more to the soundscape at the lower receiver than at the upper receiver.