Sonar serves as a critical submarine detection apparatus for naval vessels, with its detection range forming the foundation of its overall performance in underwater surveillance. The Luzon Strait, in the eastern part of the South China Sea, presents a complex hydrographic setting that profoundly influences sonar performance, necessitating mastery of the detection range variation for enhanced anti-submarine operational efficiency. This study employs the Bellhop acoustic propagation model to estimate the transmission loss. Subsequently, a detection probability integration approach is applied to determine the sonar detection range in the Luzon Strait from 2019 to 2023, which is then subjected to statistical analysis. The findings indicate the following. (1) During the summer and autumn, the shallow mixed layer fails to generate a surface duct, resulting in shorter detection ranges that are primarily dependent on the water depth. In the Shallow Water Zone (<150 m), frequent interactions between sound waves and the sea boundaries lead to considerable acoustic energy attenuation, maintaining a short detection range. In the Intermediate Depth Zone (150–2500 m), sound rays retain adequate energy post-seabed reflection, extending the sonar detection to 5–8 km. Beyond 2500 m, the diminishing reflective energy restricts the range to 2–5 km. (2) Conversely, in the winter and spring, the formation of a surface duct becomes the predominant determinant of the detection range, capable of exceeding 10 km, overshadowing the influence of the water depth.

To tackle the problem of existing underwater vehicle covert path planning methods ignoring ambient noise fields, an automated path planning method based on a statistically characterized environmental noise field is proposed. The method involves constructing a background noise spectrum level model using Automatic Identification System (AIS) data and wind speed data. Then, a Range-Dependent Acoustic Model (RAM) is integrated to generate a statistically significant 10th percentile noise field. The result is subsequently incorporated into the sonar equation to develop a noise-considerate concealment effectiveness model, which serves as input for a noise-considerate A* path planning algorithm. Comparative analyses of path planning results demonstrate that, within the studied maritime domain, the noise-prioritized path exhibits a statistically significant reduction in the median detection range by approximately 17%, a 50% reduction in the minimum detection range, and a 20% reduction in the maximum detection range, relative to alternative paths planned with a fixed noise level assumption.

Most mesoscale eddy tracking methodologies used prior to this study evaluated eddy features using a distance-based proximity relationship, rather than considering similarities between eddies. This study applies a fast normalized cross-correlation methodology in the field of image registration to propose a novel mesoscale eddy tracking methodology that can rapidly and comprehensively calculate the similarities between two eddies and judge their relationship through the correlation coefficient, thus facilitating a more accurate mesoscale eddy trajectory tracking. The sea level anomaly data field is employed to identify the positions of eddies over time. The tracking methodology is then used to track the mesoscale eddy trajectories. After comparing the local nearest neighbor methodology (LNN) with our proposed new methodology in the Northwest Pacific Ocean, we conclude that the proposed methodology can address issues of discontinuity in tracking; especially in cases involving eddies with long lifespans. The tracking trajectories utilized in the proposed methodology achieve superior continuity and integrity and a higher degree of characterization than LNN, with the tracking results showing greater consistency with real eddy motion. The new methodology proposed in this paper has great significance for more widespread use.