Precise underwater navigation is critical for autonomous and remotely operated deep-sea vehicles, yet variations in seawater sound speed often introduce systematic acoustic positioning errors. A study published in Satellite Navigation (DOI: 10.1186/s43020-025-00181-w) 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. However, navigation precision decreases with depth and distance due to non-uniform sound speed, which changes with temperature, salinity, and pressure. Pre-measured sound speed profiles serve as initial references, but long-endurance missions experience temporal SSP drift, causing refraction-induced errors that accumulate in navigation results. Traditional correction relies on static conductivity-temperature-depth (CTD) profiler measurements or empirical models that fail to adapt to real-time conditions.
Researchers from collaborating institutions reported a new real-time SSP correction scheme for tightly coupled SINS/USBL navigation. The 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. Based on Snell's law, the team derived partial differential relationships between sound-speed disturbance and horizontal/vertical displacements. 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 showed RMS position improved 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. 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.
This 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.
