Deep-sea exploration demands centimeter-to-meter level positioning accuracy in environments where satellite signals cannot penetrate. This paper reviews the core technologies that enable high-precision underwater navigation, including strapdown inertial navigation systems (SINS), Doppler velocity logs (DVL), acoustic positioning arrays (USBL/LBL), and emerging AI-driven multi-sensor fusion methods. We organize these technologies into a five-layer architecture—from raw sensor data acquisition through signal processing, information fusion, navigation solution, to end-use applications—and analyze how each layer addresses the unique challenges of deep-sea operation: extreme hydrostatic pressure, acoustic multipath interference, geomagnetic anomalies, and long-duration drift accumulation. We examine recent breakthroughs in deep learning-enhanced DVL error compensation, invariant extended Kalman filtering, factor graph optimization for tightly coupled SINS/DVL/USBL integration, and gravity gradient-aided terrain matching. A systematic comparison reveals that single-technology approaches reach fundamental accuracy limits: acoustic methods achieve 0.01–5 m but require surface infrastructure, inertial integration maintains 0.5–5 m but drifts over time, and geophysical matching covers 10–500 m with full depth capability. Multi-source fusion that combines these complementary strengths now delivers sub-meter accuracy at depths exceeding 6,000 m. We identify three frontier directions that could reshape the field: neuromorphic sensor fusion that replaces handcrafted filter models, quantum inertial measurement that eliminates gyroscope drift at its physical source, and swarm-cooperative navigation that distributes positioning tasks across autonomous vehicle fleets. These advances will directly support the next generation of deep-sea mineral extraction, submarine cable maintenance, and full-ocean-depth scientific survey.

Zhu Zhentao. Key Technologies and Equipment Applications for High-Precision Navigation and Positioning in Deep Sea. Academic Journal of Engineering and Technology Science (2026), Vol. 9, Issue 3: 149-157. https://doi.org/10.25236/AJETS.2026.090319.

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