This paper presents a new Metacognitive Decision Making (MDM) framework inspired by human-like metacognitive principles. The MDM framework is incorporated in unmanned aerial vehicles (UAVs) deployed for decentralized stochastic search without communication for detecting stationary targets (fixed/sudden pop-up) and dynamic targets. The UAVs are equipped with multiple sensors (varying sensing capability) and search for targets in a largely unknown area. The MDM framework consists of a metacognitive component and a self-cognitive component. The metacognitive component helps to self-regulate the search with multiple sensors addressing the issues of "which-sensor-to-use", "when-to-switch-sensor", and "how-to-search". Each sensor possesses inverse characteristics for the sensing attributes like sensing range and accuracy. Based on the information gathered by multiple sensors carried by each UAV, the self-cognitive component regulates different levels of stochastic search and switching levels for effective searching. The lower levels of search aim to localize the search space for the possible presence of a target (detection) with different sensors. The highest level of a search exploits the search space for target confirmation using the sensor with the highest accuracy among all sensors. The performance of the MDM framework with two sensors having low accuracy with wide range sensor for detection and increased accuracy with low range sensor for confirmation is evaluated through Monte-Carlo simulations and compared with six multi-UAV stochastic search algorithms (three self-cognitive searches and three self and social-cognitive based search). The results indicate that the MDM framework is efficient in detecting and confirming targets in an unknown environment.

翻译：本文提出了一种受人类元认知原理启发的全新元认知决策框架。该框架被集成于无人机中，用于无需通信的分散随机搜索，以探测静态目标（固定/突然出现的突发目标）和动态目标。无人机配备多种传感器（具有不同的感知能力），并在大范围未知区域中搜索目标。MDM框架由元认知组件和自我认知组件构成。元认知组件通过传感器选择、切换时机及搜索策略三个环节实现多传感器搜索的自我调节。每种传感器在感知范围和精度等属性上呈现反向特性。基于各无人机搭载的多传感器采集的信息，自我认知组件可调节随机搜索的不同层级及切换级别，从而实现高效搜索。低层级搜索旨在利用不同传感器定位可能存在的目标区域（探测），而最高层级搜索则利用所有传感器中精度最高的传感器在搜索空间内进行目标确认。通过蒙特卡洛仿真，评估了采用两种传感器（低精度宽范围传感器用于探测、高精度窄范围传感器用于确认）的MDM框架性能，并与六种多无人机随机搜索算法（三种基于自我认知的搜索和三种基于自我与社会认知的搜索）进行对比。结果表明，MDM框架在未知环境中能够高效完成目标探测与确认。