Underwater navigation systems face persistent challenges due to variations in seawater sound speed, which introduce systematic positioning errors that compromise mission accuracy. A new real-time sound speed profile correction method, published in Satellite Navigation in 2025, demonstrates substantial improvements in navigation precision for autonomous and remotely operated deep-sea vehicles.
The research addresses a fundamental limitation in Strap-down Inertial Navigation System and Ultra-Short Baseline integration, which serves as the primary navigation method underwater since satellite signals cannot penetrate seawater. Traditional approaches rely on pre-measured sound speed profiles or static conductivity-temperature-depth profiler measurements, but these methods fail to adapt to real-time environmental changes during long-endurance missions.
The newly developed method uses acoustic ray-tracing theory to model how time-varying sound speed profiles affect acoustic propagation, altering ray incident angles and travel time. Based on Snell's law, researchers derived relationships between sound-speed disturbance and positioning displacements. The approach incorporates an adaptive two-stage information filter that simultaneously estimates sound speed variations while detecting USBL outliers in real time.
Simulations using MVP-collected CTD datasets showed that without sound speed profile correction, USBL horizontal positioning errors reached several meters. With the proposed algorithm, RMS error dropped significantly. Sea trials conducted in the South China Sea demonstrated remarkable improvements, with RMS position improving from 0.45 meters to 0.08 meters northward and from 0.23 meters to 0.07 meters eastward, representing precision enhancements exceeding 80% under actual mission conditions.
The technical details of this research are available in the published article at https://doi.org/10.1186/s43020-025-00181-w. The work was supported by multiple funding sources including the National Natural Science Foundation of China and the National Key Research and Development Program of China.
According to the research team, real-time sound speed profile reconstruction is crucial for addressing navigation drift in deep-sea acoustic systems. Traditional navigation often depends on static sound speed profiles that quickly become outdated during long missions. The new model integrates physical ray-tracing with adaptive filtering, enabling autonomous vehicles to sense and correct sound-speed changes rather than relying on fixed inputs.
This advancement has significant implications for deep-sea operations, including ocean mapping, ecological monitoring, mineral exploration, under-ice routing, and long-range autonomous missions. By reducing dependence on external CTD surveys and improving resilience to acoustic distortion, the method enhances navigation robustness during extended deployments. The framework provides a practical path toward self-adaptive deep-sea navigation systems that can maintain precision in variable ocean environments.
The researchers indicate that further developments could integrate machine-learning-based sound speed profile prediction or multi-sensor oceanographic data for proactive correction. They foresee the method's potential to improve efficiency and data reliability in future deep-sea exploration and marine resource assessment, supporting more accurate seabed mapping and resource detection where precise localization is required under dynamic environmental conditions.
