• Title/Summary/Keyword: 기본 전투 기동

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Design of an Autonomous Air Combat Guidance Law using a Virtual Pursuit Point for UCAV (무인전투기를 위한 가상 추적점 기반 자율 공중 교전 유도 법칙 설계)

  • You, Dong-Il;Shim, Hyunchul
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.42 no.3
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    • pp.199-212
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    • 2014
  • This paper describes an autonomous air combat guidance law using a Virtual Pursuit Point (VPP) in one-on-one close engagement for Unmanned Combat Aerial Vehicle (UCAV). The VPPs that consist of virtual lag and lead points are introduced to carry out tactical combat maneuvers. The VPPs are generated based on fighter's aerodynamic performance and Basic Fighter Maneuver (BFM)'s turn circle, total energy and weapon characteristics. The UCAV determines a single VPP and executes pursuit maneuvers based on a smoothing function which evaluates probabilities of the pursuit types for switching maneuvers with given combat states. The proposed law is demonstrated by high-fidelity real-time combat simulation using commercial fighter model and X-Plane simulator.

The Development of Rule-based AI Engagement Model for Air-to-Air Combat Simulation (공대공 전투 모의를 위한 규칙기반 AI 교전 모델 개발)

  • Minseok, Lee;Jihyun, Oh;Cheonyoung, Kim;Jungho, Bae;Yongduk, Kim;Cheolkyu, Jee
    • Journal of the Korea Institute of Military Science and Technology
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    • v.25 no.6
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    • pp.637-647
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    • 2022
  • Since the concept of Manned-UnManned Teaming(MUM-T) and Unmanned Aircraft System(UAS) can efficiently respond to rapidly changing battle space, many studies are being conducted as key components of the mosaic warfare environment. In this paper, we propose a rule-based AI engagement model based on Basic Fighter Maneuver(BFM) capable of Within-Visual-Range(WVR) air-to-air combat and a simulation environment in which human pilots can participate. In order to develop a rule-based AI engagement model that can pilot a fighter with a 6-DOF dynamics model, tactical manuals and human pilot experience were configured as knowledge specifications and modeled as a behavior tree structure. Based on this, we improved the shortcomings of existing air combat models. The proposed model not only showed a 100 % winning rate in engagement with human pilots, but also visualized decision-making processes such as tactical situations and maneuvering behaviors in real time. We expect that the results of this research will serve as a basis for development of various AI-based engagement models and simulators for human pilot training and embedded software test platform for fighter.