Facing Environmental Uncertainties: A Submersible Path Prediction Model Based on Kalman Filter

Abstract

Covering two-thirds of the Earth's surface, the oceans are abundant in natural resources, presenting significant opportunities for exploration and exploitation. This underlines the importance of enhancing the positioning accuracy and safety of submersibles, which serve as essential instruments for oceanic exploration and study. Facing the complexities of the marine environment and the inherent challenges of exploration, this paper focuses on the development of an advanced submersible positioning model. By integrating a thorough analysis of ocean physics and dynamics with the unique environmental features of the Ionian Sea, a tributary of the Mediterranean Sea, this model is established. At its core, the Kalman filter algorithm is employed, enabling precise prediction and estimation of a submersible's real-time position within challenging marine settings. This capability allows for the submersible's navigational direction and speed to be automatically adjusted based on predicted positional changes, effectively addressing environmental challenges and ensuring both accuracy in positioning and navigational safety. Such advancements offer crucial technical support for ocean exploration and significantly improve the autonomous navigation of submersibles in uncharted and intricate marine environments.