A new method for correcting sound speed variations in real time promises to significantly enhance the accuracy of underwater navigation for autonomous and remotely operated vehicles. Published in Satellite Navigation (DOI: 10.1186/s43020-025-00181-w), the research addresses a critical challenge: the unpredictable changes in seawater sound speed due to temperature, salinity, and pressure that cause systematic errors in acoustic positioning systems.
Underwater vehicles typically rely on a fusion of Strap-down Inertial Navigation Systems (SINS) and Ultra-Short Baseline (USBL) acoustic positioning, as GPS signals cannot penetrate water. However, the precision of this combination degrades with depth and distance because sound speed varies across time and depth. Pre-measured sound speed profiles (SSPs) are used as initial references, but they quickly become outdated during long missions, leading to errors from refraction-induced travel-time and angle distortions.
Researchers from collaborating institutions developed an adaptive approach that models temporal SSP variability using acoustic ray-tracing theory. The method employs an Adaptive Two-stage Information (ATI) filter that jointly estimates sound speed disturbances and identifies USBL outliers in real time. By linking sound speed disturbances to positioning deviations through partial differential relationships derived from Snell's law, the system can correct navigation errors dynamically.
Simulations and sea trials in the South China Sea demonstrated substantial improvements. Without correction, USBL horizontal positioning errors reached several meters. With the proposed algorithm, RMS error dropped markedly: northward positioning improved from 0.45 meters to 0.08 meters, and eastward from 0.23 meters to 0.07 meters—enhancing precision by over 80% under real mission conditions. The method also incorporates a two-order SSP disturbance representation that separates the shallow-water mixed layer, thermocline transition zone, and deep isothermal layer, reflecting realistic sound-speed distribution with depth.
"Traditional navigation often depends on static sound speed profiles, which quickly become outdated during long missions. Our model integrates physical ray-tracing with adaptive filtering, enabling ARVs to sense and correct sound-speed changes rather than rely on fixed inputs," the team noted. The approach reduces dependence on external conductivity-temperature-depth (CTD) profiler surveys and improves resilience to acoustic distortion, enhancing navigation robustness during long deployments.
This SSP correction framework is particularly suited for autonomous remotely operated vehicles (ARVs) and autonomous underwater vehicles (AUVs) performing seabed mapping, ecological monitoring, mineral exploration, under-ice routing, or long-range autonomous missions. The authors foresee potential for further developments, including machine-learning-based SSP prediction or multi-sensor oceanographic data integration for proactive correction, which could improve efficiency and data reliability in future deep-sea exploration and marine resource assessment.
The research was supported by the National Natural Science Foundation of China, the National Key Research and Development Program of China, and other funding sources. The study was published in Satellite Navigation, a journal that focuses on advances in satellite navigation technology and applications.
