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 positioning errors that compromise the precision of 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 integrating acoustic ray-tracing theory with adaptive filtering to dynamically estimate sound speed disturbances and improve navigation accuracy.

The research, detailed in the study available at https://doi.org/10.1186/s43020-025-00181-w, focuses on Strap-down Inertial Navigation System (SINS) and Ultra-Short Baseline (USBL) integration, which is commonly used for underwater navigation since satellite signals cannot penetrate seawater. Navigation precision typically 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 rely on static conductivity-temperature-depth (CTD) profiler measurements or empirical models that fail to adapt to real-time conditions, leading to refraction-induced travel-time and angle errors that accumulate during long-endurance missions.

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 work 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.

A two-order SSP disturbance representation separates the shallow-water mixed layer, the thermocline transition zone, and the deep isothermal layer, reflecting realistic sound-speed distribution with depth. To fuse navigation data, the researchers designed an Adaptive Two-stage Information (ATI) filter combining SINS, Doppler Velocity Log (DVL), Pressure Gauge (PG) and USBL observations. The filter 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.

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 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. The new model integrates physical ray-tracing with adaptive filtering, enabling autonomous remotely operated vehicles (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, 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 authors foresee its potential to improve efficiency and data reliability in future deep-sea exploration and marine resource assessment, representing a significant advancement in underwater navigation technology.