Underwater navigation faces significant challenges due to the variability of sound speed in seawater, which introduces systematic errors in acoustic positioning systems. A new study published in Satellite Navigation presents a real-time sound speed profile (SSP) correction scheme that enhances the integration of strap-down inertial navigation systems (SINS) and ultra-short baseline (USBL) positioning. The method, validated through simulations and sea trials in the South China Sea, demonstrates substantial improvements in positional accuracy, achieving reductions in root mean square errors by over 80% in northward and eastward directions.
The research addresses a critical issue in deep-sea navigation: the temporal drift of sound speed profiles due to changing temperature, salinity, and pressure. Traditional approaches rely on static conductivity-temperature-depth (CTD) measurements or empirical models that cannot adapt to real-time conditions. The new scheme employs acoustic ray-tracing theory to link sound speed disturbances to positioning deviations and incorporates an adaptive two-stage information filter. This filter jointly estimates SSP variations while detecting USBL outliers through a Generalized Likelihood Ratio test, enabling robust navigation even in variable ocean environments.
The study details how time-varying SSP affects USBL acoustic propagation by altering ray incident angles and travel time. Using Snell's law, the team derived partial differential relationships between sound-speed disturbance and horizontal/vertical displacements. A quasi-observation model was constructed to estimate SSP perturbation from differences between SINS-derived and USBL-measured travel time. The SSP disturbance is represented across three depth layers: shallow-water mixed layer, thermocline transition zone, and deep isothermal layer, reflecting realistic sound-speed distribution.
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 confirmed these results, showing RMS position improved from 0.45 m to 0.08 m northward and 0.23 m to 0.07 m eastward. The researchers note that traditional navigation often depends on static sound speed profiles, which quickly become outdated during long missions.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 autonomous vehicles to sense and correct sound-speed changes rather than rely on fixed inputs.
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 full study is available in the journal Satellite Navigation at https://doi.org/10.1186/s43020-025-00181-w. The research was supported by the National Natural Science Foundation of China and other national programs.
