通过热图分析加强无人机传感器操作员培训

Ashish Amresh, John C. Femiani, J. Fairfield, Adam J. Fairfield
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引用次数: 4

摘要

基于热图的数据可视化和挖掘是游戏引擎设计和架构中的一个新兴领域。许多最先进的游戏引擎和流行的商业游戏都采用了这种技术,它可以帮助收集和整理玩家的活动和行为,从而更好地告知系统下一步的行动。模拟游戏和严肃游戏可以通过基于热图的可视化分析和追踪玩家行为而受益匪浅。热图是随时间变化的纹理图,它表示在任何特定时间间隔内特定网格上的选定活动。本文介绍了一种实时热图数据采集与生成工具在地面作战场景和无人机传感器操作场景两种军事仿真中的应用结果。该研究展示了几种实时可视化技术,这些技术发展到模拟中,主要目的是理解参与者的行为。新手和专家数据被填充作为实验的一部分,以验证我们的方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
UAV Sensor Operator Training Enhancement through Heat Map Analysis
Heat map based data visualization and mining is an emerging area in game engine design and architecture. Employed by many state of the art game engines and popular commercial games, this technology helps populate and collate player activity and behavior to better inform the system for further action. Simulation and serious games can tremendously benefit by applying heat map based visualization for the purposes of analyzing and tracking player behavior. Heat maps are time varying texture maps that represent a chosen activity over a certain grid at any particular interval of elapsed time. In this paper results of applying a real-time heat map data capture and generation tool on two military simulations: 1) Ground-based combat scenario and 2) Unmanned Aerial Vehicle sensor operator scenario is presented. The research showcases several real-time visualization techniques developed into the simulation with the main goal of understanding participant behavior. Novice and expert data is populated as part of the experiment to validate the effectiveness of our methods.
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