用无人机和概率模型提升落水者搜寻成功率，20分钟内仍可超80%找到
Dynamic UAV-based search operations using probabilistic diffusion modeling of Man Overboard incident victims
- 基于扩展卡尔曼滤波融合海流数据预测落水者漂移路径
- 改进的概率搜索法在20分钟后启动仍达80%以上成功率
- 适合海上搜救、无人机调度与应急系统研发者参考
2010至2019年间，超过70%的邮轮落水者不幸遇难。本文提出一种可靠预测落水者可能位置的方法，并详细描述利用无人机开展搜救的策略。该预测方法采用扩展卡尔曼滤波器，结合漏漂模型信息，在考虑人员移动不确定性和气象条件的基础上，实时追踪海面落水者。随后，无人机根据预测结果执行搜索任务。文中评估了五种搜索方法：之字形、回旋形、螺旋形、概率引导搜索及改进的概率引导搜索（IPIS）。IPIS方法在无人机延迟20分钟启动的情况下，仍能实现平均80%以上的搜寻成功率，即使检测成功率仅为30%。所有仿真环境与方法评估代码均开源于GitHub：https://github.com/diangeli/pdms-man-overboard。
原文摘要 · Abstract (English)
More than 70% of the people that fell overboard cruise ships in the period 2010-2019 lost their lives. This paper presents a strategy for reliably predicting the area a person may be in after a man overboard incident, and describes in detail the search methods to find them utilizing UAV technology. The search area prediction method employs an Extended Kalman Filter that capitalizes on the information from the Leeway model to track the missing person in the sea by taking into account the uncertainty of the movement of the person and the weather conditions in the area. Then, a UAV uses this information to search for the person. Five different methods for searching in this dynamic area are presented and evaluated - the Zigzag, the Boustrophedon, the Spiral, the Probability Informed Search and the Improved Probability Informed Search (IPIS) methods. The IPIS method provides success rate of over 80% on average for finding a person, even if the UAV initiates the search mission 20 minutes after the man overboard incident and even assuming a detection method with a success rate of 30%. All code for the simulation environment and the evaluation of the methods is available on our GitHub page at https://github.com/diangeli/pdms-man-overboard.
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