Probabilistic Method for Optimizing Submarine Search and Rescue Strategy Under Environmental Uncertainty

TL;DR

This paper introduces a hybrid probabilistic framework combining Monte Carlo and Bayesian methods to enhance submarine search and rescue efficiency under uncertain environmental conditions.

Contribution

It develops a novel hybrid algorithm integrating Monte Carlo, Bayesian updating, and filtering for improved submarine localization in uncertain environments.

Findings

Enhanced accuracy in submarine location prediction.

Improved search success rate through probabilistic methods.

Effective optimization of rescue strategies using cost-benefit analysis.

Abstract

When coping with the urgent challenge of locating and rescuing a deep-sea submersible in the event of communication or power failure, environmental uncertainty in the ocean can not be ignored. However, classic physical models are limited to deterministic scenarios. Therefore, we present a hybrid algorithm framework combined with dynamic analysis for target submarine, Monte Carlo and Bayesian method for conducting a probabilistic prediction to improve the search efficiency. Herein, the Monte Carlo is performed to overcome the environmental variability to improve the accuracy in location prediction. According to the trajectory prediction, we integrated the Bayesian based grid research and probabilistic updating. For more complex situations, we introduced the Bayesian filtering. Aiming to maximize the rate of successful rescue and costs, the economic optimization is performed utilizing the cost-benefit analysis based on entropy weight method and the CER is applied for evaluation.