A new real-time correction method for underwater navigation addresses the persistent challenge of sound speed variability in seawater, which often degrades positioning accuracy for autonomous and remotely operated deep-sea vehicles. Published in Satellite Navigation, the study introduces an in-situ sound speed profile (SSP) correction scheme that integrates with Strap-down Inertial Navigation System (SINS) and Ultra-Short Baseline (USBL) fusion, demonstrating significant improvements in positional precision during sea trials.
Underwater navigation relies heavily on SINS/USBL integration because satellite signals cannot penetrate water. However, sound speed varies with temperature, salinity, and pressure, causing refraction that introduces systematic errors in acoustic positioning. Traditional methods using pre-measured or static sound speed profiles become outdated during long missions, leading to accumulating drift. The proposed method models temporal SSP variability through acoustic ray-tracing theory and employs an adaptive two-stage information filter to estimate sound speed disturbances while detecting USBL outliers in real time.
Researchers from collaborating institutions derived partial differential relationships between sound speed disturbance and horizontal/vertical displacements using Snell's law. They constructed a quasi-observation model to estimate SSP perturbation based on differences between SINS-derived and USBL-measured travel time. A two-order SSP disturbance representation separates the shallow mixed layer, thermocline, and deep isothermal layer to reflect realistic depth-dependent sound speed distribution. The adaptive 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 algorithm, RMS error dropped markedly. Sea trials in the South China Sea confirmed 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. The method reduces dependence on external CTD surveys and improves resilience to acoustic distortion, enhancing navigation robustness during long deployments.
According to the authors, real-time SSP reconstruction is crucial for addressing navigation drift. '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. This approach 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.
The SSP correction framework provides a practical path toward self-adaptive deep-sea navigation systems. Further developments could integrate machine-learning-based SSP prediction or multi-sensor oceanographic data for proactive correction. The study, supported by the National Natural Science Foundation of China and other funding sources, was published with DOI: 10.1186/s43020-025-00181-w. The original source URL is https://doi.org/10.1186/s43020-025-00181-w. The research was published in Satellite Navigation and the related link is http://chuanlink-innovations.com.
