In this paper, a sequence decision framework based on the Bayesian search is proposed to solve the problem of using an autonomous system to search for the missing target in an unknown environment. In the task, search cost and search efficiency are two competing requirements because they are closely related to the search task. Especially in the actual search task, the sensor assembled by the searcher is not perfect, so an effective search strategy is needed to guide the search agent to perform the task. Meanwhile, the decision-making method is crucial for the search agent. If the search agent fully trusts the feedback information of the sensor, the search task will end when the target is “detected” for the first time, which means it must take the risk of founding a wrong target. Conversely, if the search agent does not trust the feedback information of the sensor, it will most likely miss the real target, which will waste a lot of search resources and time. Based on the existing work, this paper proposes two search strategies and an improved algorithm. Compared with other search methods, the proposed strategies greatly improve the efficiency of unmanned search. Finally, the numerical simulations are provided to demonstrate the effectiveness of the search strategies.

中文翻译：

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概率搜索中基于贝叶斯的搜索决策框架和搜索策略分析

本文提出了一种基于贝叶斯搜索的序列决策框架，以解决在未知环境下使用自主系统搜索丢失目标的问题。在任务中，搜索成本和搜索效率是两个相互竞争的要求，因为它们与搜索任务密切相关。特别是在实际的搜索任务中，搜索者组装的传感器并不完美，因此需要有效的搜索策略来指导搜索代理执行任务。同时，决策方法对于搜索代理至关重要。如果搜索代理完全信任传感器的反馈信息，则搜索任务将在首次“检测到”目标时结束，这意味着必须承担发现错误目标的风险。反过来，如果搜索代理不信任传感器的反馈信息，则很可能会错过实际目标，这将浪费大量搜索资源和时间。在现有工作的基础上，本文提出了两种搜索策略和一种改进的算法。与其他搜索方法相比，所提出的策略大大提高了无人搜索的效率。最后，提供了数值模拟以证明搜索策略的有效性。