文献类型：期刊文献

英文题名：Probabilistic Method for Optimizing Submarine Search and Rescue Strategy Under Environmental Uncertainty

作者：Liu, Runhao[1]; Chen, Ziming[2]; Zhang, Peng[3]

机构：[1] Polytechnic Institute, Zhejiang University, Hangzhou, 310015, China; [2] School of Social and Public Administration, East China University of Science and Technology, Shanghai, 200237, China; [3] School of Mathematical Sciences, Zhejiang University, Hangzhou, 310058, China

年份：2025

外文期刊名：arXiv

收录：EI(收录号：20253218939758)

语种：英文

外文关键词：Markov processes

摘要：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’t 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. Copyright ? 2025, The Authors. All rights reserved.