한국항공우주학회지 (Journal of the Korean Society for Aeronautical & Space Sciences)

  • 제49권3호
  • /
  • Pages.197-204
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  • 2021
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  • 1225-1348(pISSN)
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  • 2287-6871(eISSN)
한국항공우주학회 (The Korean Society for Aeronautical and Space Sciences)
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심층 신경망을 이용한 실시간 유도탄 파편 탄착점 및 분산 추정

Real-Time Estimation of Missile Debris Predicted Impact Point and Dispersion Using Deep Neural Network

  • 투고 : 2020.11.10
  • 심사 : 2021.02.03
  • 발행 : 2021.03.01
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⟨ 이전 논문 다음 논문 ⟩

초록

유도탄의 비행 시험 중 고장 또는 비정상적인 기동이 발생하는 경우 비행을 계속하지 않도록 의도적으로 자폭한다. 이때 파편이 발생하며 안전 지역을 벗어났는지 여부를 실시간으로 추정하는 것이 중요하다. 본 논문에서는 Fully-Connected Neural Network(FCNN)를 이용하여 실시간으로 파편의 예상 낙하 영역 및 낙하 시간을 추정하는 방법을 제안한다. 많은 양의 학습 데이터 생성을 위해 Unscented Transform(UT)를 적용하였으며 신뢰도 확보를 위해 Monte-Carlo(MC) 시뮬레이션과 비교하여 파라미터를 선정하였다. 또한 제안한 방법의 추정 결과를 MC와 비교하여 성능을 분석하였다.

If a failure or an abnormal maneuver occurs during the flight test of a missile, the missile is deliberately self-destructed so as not to continue the flight. At this time, debris are produced and it is important to estimate the impact area in real-time whether it is out of the safety area. In this paper, we propose a method to estimate the debris dispersion area and falling time in real-time using a Fully-Connected Neural Network (FCNN). We applied the Unscented Transform (UT) to generate a large amount of training data. UT parameters were selected by comparing with Monte-Carlo (MC) simulation to secure reliability. Also, we analyzed the performance of the proposed method by comparing the estimation result of MC.

키워드

참고문헌

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