Research on Submarine Search Strategy Based on Bayesian Search Theory

In recent years, manned deep diving has attracted much attention, but the safety problem cannot be ignored. In this paper, based on the Bayesian search theory, a search and rescue model is constructed to improve the search efficiency of divers in distress. First, the diver trajectory is obtained by solving a system of differential equations, and the uncertainty is analyzed to clarify the demand for information and equipment. Second, the entropy weight method and TOPSIS method are used to evaluate the equipment selection in terms of procurement cost, practicality and maintenance cost. The state of the submersible is categorized into two scenarios, "drifting with the current" and "sinking to the bottom", and the probability distributions are calculated based on the current speed and geographic factors, and the final distributions are plotted. The search algorithm shows that the probability of successful search can be more than 0.5 with a limited number of iterations (about 400). Finally, the model was extended in two ways. First, a region of the Caribbean Sea was selected for the study. By extracting its depth data, a similar terrain analysis was performed and the desired results were obtained; on the other hand, the presence of multiple submersible activities in the target sea area was considered and divided into two scenarios: "The search efficiency is significantly improved when multiple unmanned submersibles are involved in the search.