Augmented Reality Based Common Operational Picture Training System for Army in Course of Action Analysis

論文翻譯標題: 擴增實境共同作戰圖像教學系統於陸軍分析行動方案
  • 茆 家麒

學生論文: Doctoral Thesis

摘要

The military decision making process (MDMP) is an important course for army training and military tactics education in Taiwan Core training of the course involves tactics in constructing common operational pictures (COP) During military tactics operation COP allows the army to display the military force battlefield space and war situation schedule for troops on both sides through information visualization Such information visualization allows the commanders and staff officers to enhance situation awareness while simultaneously engaging critical thinking in decision making In so much as information visualization is useful in military actions with the evolution of war types and increased demands for information the two-dimensional display can no longer accommodate the visual recognition and context-awareness needs in current battlefield situations This study therefore aimed to investigate how to better use COP in order to enhance the effectiveness of military tactics education and raise situation awareness Through reviewing the literature on military operations user experience and human-computer interaction (HCI) the researcher identified possible ways to combine virtual information and realistic environment with augmented reality (AR) to achieve intuitiveness interaction and immersion for tactics education As shown by recent studies AR has been accredited for its potential in an application in such fields as medicine education manufacturing and business The research design includes a needs analysis definition of key concepts problem interpretation strategies for visualization application system building and learning performance evaluation To understand users’ needs and issues interviews and focus groups were conducted to analyze and identify key factors of user behaviors in COP Inductive categorization of the data and the use of visualization strategies led to the construction of a protocol of AR-based COP training system The system mainly employed visualization effects through three-dimensional animation along with traditional visual guidance strategies to represent dynamic changes in weather patterns landscapes and physical tactic images The evaluation of the experiment involved an experimental and a control group with 22 participants in each both of which were classes at the Army Command and Staff College (ACSC) The analysis focused on the course of action approval of MDMP The scope of the experiment is the course of action (COA) analysis phase in the Military Decision-Making Process (MDMP) The evaluation was divided into two stages During the first stage the Technology Acceptance Model (TAM) was used to investigate the process and effects of an improved system incorporated in the tactics course Results showed significant effects with the AR-based visualization cognitive usability ease of use situation awareness interaction and intention to use Stage two based on the evaluation items of the action course the experimental group and the control group are distinguished for the verification project Further Compared with the independent Sample t-test which shows that the average scores are better than the original version with the new technology system Finally the Delphi Method was conducted by the teacher of the tactical team to evaluate the experts’ opinion of the system performance The results of this study indicated that system protocol design based on user experience and visualization strategies can effectively facilitate the enrichment of information visualization for tactical thinking and decision-making through better understanding about users’ attitudes and willingness to adopt the new system The positive outcomes of the study prove that AR technology can be applied to the current teaching of military tactics Lastly the system prototype and experiment proposed in this study also provide future reference related to military education using AR-based systems
獎項日期2018 七月 11
原文English
監督員Chien-Hsu Chen (Supervisor)

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