Positioning and Search System for Submersibles: Model Construction, Results, and Future Prospects

This paper proposes a comprehensive positioning and search system for deep sea submersibles to enhance efficiency in complex marine environments. The core position prediction model integrates Kalman and extended Kalman filters, accounting for submersible dynamics and geographical data. This approach effectively addresses nonlinear challenges from forces like buoyancy, gravity, ocean currents, and resistance. High-precision positioning on 3D topographic maps is achieved through optimized dynamic and observation equations.Equipment selection utilizes the TOPSIS model to evaluate eight deep-sea rescue tools, emphasizing functionality, cost-effectiveness, safety, and durability. Search efficiency is improved by integrating the position prediction model with the ant colony algorithm, reducing search paths and time in simulations. A multi target cooperative position prediction model, incorporating multi-target and cooperative extended Kalman filters, supports multi-submersible coordination.Environmental adaptability is demonstrated in areas like the Caribbean and Ionian Seas, highlighting the model’s robustness. While significant progress has been made, challenges remain in ensuring accuracy, stability, and feasibility in extreme conditions. Future research will focus on data collection, parameter optimization, and developing more efficient algorithms to expand the model’s applicability in diverse marine scenarios.