Underwater navigation faces persistent challenges due to variations in seawater sound speed, which introduce systematic errors in acoustic positioning systems essential for autonomous and remotely operated deep-sea vehicles. A new real-time sound speed profile (SSP) correction scheme, published in Satellite Navigation in 2025, addresses this limitation by dynamically estimating SSP variations while detecting outliers in Ultra-Short Baseline (USBL) measurements.
The importance of this development lies in its potential to transform deep-sea operations where precise navigation is critical. Autonomous underwater vehicles (AUVs) and autonomous remotely operated vehicles (ARVs) rely on Strap-down Inertial Navigation System (SINS) and USBL integration because satellite signals cannot penetrate seawater. However, navigation precision decreases with depth and distance as sound speed changes with temperature, salinity, and pressure across time and depth. Traditional correction methods depend on static conductivity-temperature-depth profiler measurements or empirical models that fail to adapt to real-time conditions, leading to accumulated errors during long-endurance missions.
The new method, developed by researchers from collaborating institutions, uses acoustic ray-tracing theory to link sound speed disturbances to positioning deviations. It incorporates an adaptive two-stage information filter that estimates SSP variations while identifying USBL outliers in real time. The approach begins by analyzing how time-varying SSP affects USBL acoustic propagation, altering ray incident angles and travel time. Based on Snell's law, the team derived partial differential relationships between sound-speed disturbance and horizontal/vertical displacements, constructing a quasi-observation model that enables estimation of SSP perturbation through differences between SINS-derived and USBL-measured travel time.
Simulations using MVP-collected CTD datasets demonstrated that without SSP correction, USBL horizontal positioning errors reached several meters. With the proposed algorithm, RMS error dropped markedly. Sea trials in the South China Sea showed even more impressive results, with RMS position improving from 0.45 meters to 0.08 meters northward and 0.23 meters to 0.07 meters eastward—enhancing precision by over 80% under real mission conditions. The researchers designed an Adaptive Two-stage Information filter combining SINS, Doppler Velocity Log, Pressure Gauge and USBL observations, which updates position, velocity and attitude errors while simultaneously detecting USBL anomalies through a Generalized Likelihood Ratio test and refining SSP estimation via recursive least squares.
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. Their model integrates physical ray-tracing with adaptive filtering, enabling ARVs to sense and correct sound-speed changes rather than rely on fixed inputs. This approach supports 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. This enhances navigation robustness during long deployments, making the method well-suited for AUVs and ARVs performing seabed mapping, ecological monitoring, mineral exploration, under-ice routing, or long-range autonomous missions. The authors foresee its potential to improve efficiency and data reliability in future deep-sea exploration and marine resource assessment, with further developments possibly integrating machine-learning-based SSP prediction or multi-sensor oceanographic data for proactive correction.
