针对如何快速寻找失踪目标的时空搜索问题, 提出了一种基于递归贝叶斯的搜索决策框架和自适应搜索策略; 在此决策框架中, 考虑了搜索者的速度上限和传感器存在虚警检测与漏报检测等实际因素, 在理论上推导了该决策框架下的置信度函数演化的表达式, 并据此提出了一种有效的搜索策略。由于在该决策框架下能研究和比较不同策略对决策的影响, 这为不同的策略进行分析和比较提供了一个有效的平台。最后, 通过大量的数值仿真验证了该决策框架和所提出的策略的有效性和实用性。

Aiming at the spatial-temporal search problem of how to find the missing target quickly, a recursive Bayesian method based search decision-making framework and an adaptive search strategy are proposed.In the decision-making framework, the upper limit of the searcher's speed, the possibilities of false alarm detection and missed detection of the sensor are taken into consideration, and the expression of the confidence function evolution under the decision framework is theoretically derived, based on which an effective search theory is proposed.Since the impact of different strategies on decision-making can be studied and compared under this decision-making framework, it provides an effective platform for comparison and analysis of different strategies.Finally, the effectiveness and practicability of the decision framework and proposed strategy are verified by a large number of numerical simulations.