A new method for correcting sound speed variations in real time promises to significantly improve the accuracy of underwater navigation for autonomous and remotely operated vehicles. The research, published in Satellite Navigation on December 21, 2025, addresses a fundamental problem in deep-sea positioning: the variability of sound speed in seawater due to changes in temperature, salinity, and pressure.
Underwater vehicles typically rely on a fusion of Strap-down Inertial Navigation Systems (SINS) and Ultra-Short Baseline (USBL) acoustic positioning, as satellite signals cannot penetrate water. However, the accuracy of USBL degrades with depth and distance because sound speed varies with environmental conditions. Traditional methods use pre-measured sound speed profiles (SSPs) or static conductivity-temperature-depth (CTD) measurements, which become outdated during long missions, leading to systematic errors.
The research team developed an in-situ SSP correction scheme that uses acoustic ray-tracing theory to link sound speed disturbances to positioning deviations. The method incorporates an adaptive two-stage information filter that estimates SSP variations while detecting USBL outliers in real time. This approach allows the navigation system to dynamically adjust to changing conditions rather than relying on fixed inputs.
Simulations and sea trials in the South China Sea demonstrated notable improvements. Without correction, USBL horizontal positioning errors reached several meters. With the new algorithm, RMS position improved from 0.45 m to 0.08 m northward and from 0.23 m to 0.07 m eastward—an enhancement of over 80% under real mission conditions. The method models temporal SSP variability by separating the water column into three layers: the shallow mixed layer, the thermocline transition zone, and the deep isothermal layer, reflecting realistic sound-speed distribution with depth.
According to the authors, real-time SSP reconstruction is crucial for addressing navigation drift in deep-sea acoustic systems. "Traditional navigation often depends on static sound speed profiles, which quickly become outdated during long missions.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. They believe the approach will support deep-ocean mapping, sampling, and seabed resource detection where precise localization is required under dynamic environmental conditions.
The SSP correction framework provides a practical path toward self-adaptive deep-sea navigation systems. By reducing dependence on external CTD surveys and improving resilience to acoustic distortion, it enhances navigation robustness during long deployments. The method is well-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. Further developments could integrate machine-learning-based SSP prediction or multi-sensor oceanographic data for proactive correction.
The research was published in Satellite Navigation (DOI: 10.1186/s43020-025-00181-w) and was supported by multiple Chinese funding agencies, including the National Natural Science Foundation of China and the National Key Research and Development Program.
