As one of the widespread physical phenomena in the global ocean system, the ocean front has a very important influence on underwater sound propagation. Firstly, this paper systematically reviews several methods for the detection of ocean fronts in the past decades, including traditional oceanographic methods, artificial intelligence methods, and acoustic methods, highlighting the advantages and disadvantages of each method. Next, some modeling studies of ocean fronts are reported in this paper. Based on the above research, we pay more attention to research progress on the acoustic effects of ocean fronts, including simulation analysis and experimental research, which has also been the focus of underwater acousticians for a long time. In addition, this paper looks forward to the future development direction of this field, which can provide good guidance for the study of ocean fronts and their acoustic effects in the future.

Aiming at the problems in the bearing-only tracking (BOT) of underwater weak target in the presence of interferers, such as discontinuity of tracking trajectory and target loss, a novel joint algorithm based on interference suppression (IS) and Kalman filter (ISKF) is proposed in this paper. In the proposed algorithm, in order to smooth the corrupted trajectory of weak target and improve the precision of tracking, a feedback structure which connects IS module and Kalman filter (KF) module is built. Furthermore, the feedback structure renders the measured results of bearing trajectory shareable mutually between IS module and KF module in real-time. In ISKF joint algorithm, IS module is employed as a preprocessing module to eliminate the power of interferers, and KF module is employed as tracker to obtain the bearing trajectory of weak target. In the initial step, the coarse target bearing and interference bearing are estimated using the conventional beamforming (CBF) method. In the updated step, the precise BOT trajectory of weak target is jointly updated using IS and KF. Moreover, when the target intersects with the interference, the ISKF joint algorithm can still track the weak target effectively with the aid of prediction model of KF. Finally, the sea trial results show that the proposed algorithm can achieve more accurate and robust real-time tracking than KF only, which benefits from the feedback between the IS module and the KF module.

As one of the most common mesoscale phenomena in the ocean, the ocean front is defined as a narrow transition zone between two water masses with obviously different properties. In this study, we proposed an ocean front reconstruction method based on the K-means algorithm iterative hierarchical clustering sound speed profile (SSP). This method constructed the frontal zone from the perspective of SSP. Meanwhile, considering that acoustic ray tracing is a very sensitive tool for detecting the location of ocean fronts because of the strong dependence of the transmission loss (TL) on SSP structure, this paper verified the feasibility of the method from the perspective of the TL calculation. Compared with other existing methods, this method has the key step of iterative hierarchical clustering according to the accuracy of clustering results. The results of iterative hierarchical clustering of the SSP can reconstruct the ocean front. Using this method, we reconstructed the ocean front in the Gulf Stream-related sea area and obtained the three-dimensional structure of the Gulf Stream front (GSF). The three-dimensional structure was divided into seven layers in the depth range of 0–1000 m. Iterative hierarchical clustering SSP by K-means algorithm provides a new method for judging the frontal zone and reconstructing the geometric model of the ocean front in different depth ranges.

As a mesoscale phenomenon of the ocean, the ocean front can directly affect the structural characteristics of sound speed profiles and further affect the acoustic propagation characteristics of the sea area. In this paper, we use the fuzzy C-means (FCM) algorithm to cluster the surface sound speed in the sea area of the Kuroshio Extension (KE) and detect the frontal zone of Kuroshio Extension (KEF). At the same time, the sound speed profile (SSP) is used instead of the temperature profile to establish the model of the sound speed field in the front area of the Kuroshio Extension and to improve the theoretical model of the ocean front. Compared with the actual ocean front calculated by reanalysis data, the root means square error (RSME) of the transmission loss (TL) calculated by the model is controlled below 6 dB, which proves the validity of the model. Finally, we propose the melt function in the model to forecast the depth change of the acoustic convergence area. Compared with the actual calculation result based on reanalysis data, the root means square error (RSME) of the depth forecasting after the frontal zone is 43.3 m. This reconstruction method does not rely on the high spatial resolution data of the whole sea depth and can be of referential significance to acoustic detection in the ocean front environment.

The sound speed profile (SSP) is a necessary prerequisite for acoustic field computation and underwater target localization and monitoring. Due to the dynamic nature of the ocean, the reconstruction of SSPs with surface characteristics is a big challenge. In this study, the Single Empirical Orthogonal Function Regression (sEOF-R) method is employed to establish the regression relationship between the surface parameters and the sound speed anomaly profile (SSAP) in three typical sea areas, namely the equator, Kuroshio Extension (KE), and Northeast Pacific. Based on the established regression relationship and the surface parameters, the underwater SSP is reconstructed. Results show that the reconstruction effects in the three areas show the best performance in the Northeast Pacific, followed by the equator and finally the KE. The quantitative analysis suggests that the local sea level anomaly (SLA) plays the dominant role in influencing the reconstruction effect, followed by the sea surface temperature anomaly (SSTA). Further analysis demonstrates that the sEOF-R method is limited in time-varying and space-varying areas. The SSP reconstructed from the sea surface information in this study is useful for the inversion of the underwater structures.