New Real-Time Sound Speed Correction Method Enhances Underwater Navigation Precision

Underwater navigation faces persistent challenges due to variations in seawater sound speed, which introduce systematic errors in acoustic positioning systems used by autonomous and remotely operated deep-sea vehicles. A new real-time correction method developed by researchers addresses this limitation by dynamically estimating sound speed variations and compensating for positioning distortions during missions.

The research, published in Satellite Navigation in 2025, presents an in-situ sound speed profile (SSP) correction scheme designed to improve Strap-down Inertial Navigation System (SINS) and Ultra-Short Baseline (USBL) integration. Underwater navigation commonly relies on SINS/USBL fusion because satellite signals cannot penetrate seawater, but navigation precision decreases with depth and distance due to non-uniform sound speed that 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. Long-endurance missions experience temporal SSP drift, causing refraction-induced travel-time and angle errors that accumulate in navigation results. The new method models temporal SSP variability using acoustic ray-tracing and applies an adaptive two-stage information filter to jointly estimate sound speed disturbance and identify USBL outliers.

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. A quasi-observation model enables estimation of SSP perturbation through differences between SINS-derived and USBL-measured travel time.

Simulations using MVP-collected CTD datasets showed 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 demonstrated significant improvements, with RMS position improving from 0.45 m to 0.08 m northward and 0.23 m to 0.07 m eastward—enhancing precision by over 80% under real mission conditions.

According to the researchers, real-time SSP 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 model integrates physical ray-tracing with adaptive filtering, enabling autonomous remotely operated vehicles to sense and correct sound-speed changes rather than rely on fixed inputs.

This advancement matters because precise underwater navigation is critical for autonomous deep-sea operations including seabed mapping, ecological monitoring, mineral exploration, under-ice routing, and long-range autonomous missions. The method reduces dependence on external CTD surveys and improves resilience to acoustic distortion, enhancing navigation robustness during long deployments. The framework provides a practical path toward self-adaptive deep-sea navigation systems that could improve efficiency and data reliability in future deep-sea exploration and marine resource assessment.