Finding behavioral patterns of UAV operators using Multichannel Hidden Markov Models
Finding behavioral patterns of UAV operators using Multichannel Hidden Markov Models
复制标题
使用多通道隐马尔可夫模型寻找无人机操作员的行为模式
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
David Camacho
中科院分区：
文献类型：
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作者：
V. Rodríguez;A. González;David Camacho
近年来，无人机（UAV）在许多不同的研究领域和工业应用中已经成为一个非常热门的话题。预计到2020年，这些技术和相关行业将大幅增长。尽管无人机控制系统的自主性越来越强，但无人机操作员的角色仍然是保证使命成功的关键因素，特别是当一个操作员必须监督多个无人机时。为此，许多来自不同领域的努力已经投入到经营者行为的研究和分析。本文提出了一种在轻量级多无人机仿真环境中发现和建模无人机操作员行为模式的新方法。我们的方法是基于多通道（或多变量）隐马尔可夫模型（MC-HALGORY），它允许收集在同一个模型的并行数据序列，如操作员的相互作用和使命事件的组合。预处理数据，创建，选择和分析模型的不同步骤进行了描述，并与经验不足的运营商进行了实验，以显示如何使用这种建模技术可以产生一个描述性的行为模型。
In recent years Unmanned Aerial Vehicles (UAVs) have become a very popular topic in many different research fields and industrial applications. These technologies, and the related industries, are expected to grow dramatically by 2020. Although the systems designed to control UAVs are increasingly autonomous, the role of UAV operators is still a critical aspect that guarantee the mission success, specially when one single operator must supervise multiple UAVs. For this reason, much effort from different areas has been put into the study and analysis of the operator behavior. This work presents a new method to find and model behavioral patterns among UAV operators in a lightweight multi-UAV simulation environment. Our approach is based on MultiChannel (or Multivariate) Hidden Markov Models (MC-HMMs), which allow to gather in the same model parallel data sequences, such as the combination of operator interactions and mission events. The different steps for preprocessing data, creating, selecting and analyzing the model are described, and an experiment with inexperienced operators has been carried out to show how a descriptive model of behaviour can be generated using this modelling technique.