Journal of Aerospace System Engineering (항공우주시스템공학회지)

The Society for Aerospace System Engineering (항공우주시스템공학회)
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An Improved CNN-LSTM Hybrid Model for Predicting UAV Flight State

무인항공기 비행 상태 예측을 위한 개선된 CNN-LSTM 혼합모델

  • Hyun Woo Seo (School of Mechanical and Aerospace Engineering, Gyeongsang National University) ;
  • Eun Ju Choi (Korea Aerospace Research Institute) ;
  • Byoung Soo Kim (School of Mechanical and Aerospace Engineering, Gyeongsang National University) ;
  • Yong Ho Moon (School of Mechanical and Aerospace Engineering, Gyeongsang National University)
  • 서현우 (경상국립대학교 일반대학원 기계항공우주공학부) ;
  • 최은주 (한국항공우주연구원 항공연구소) ;
  • 김병수 (경상국립대학교 일반대학원 기계항공우주공학부) ;
  • 문용호 (경상국립대학교 일반대학원 기계항공우주공학부)
  • Received : 2024.03.05
  • Accepted : 2024.04.16
  • Published : 2024.06.30
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Abstract

In recent years, as the commercialization of unmanned aerial vehicles (UAVs) has been actively promoted, much attention has been focused on developing a technology to ensure the safety of UAVs. In general, the UAV has the potential to enter an uncontrollable state caused by sudden maneuvers, disturbances, and pilot error. To prevent entering an uncontrolled situation, it is essential to predict the flight state of the UAV. In this paper, we propose a flight state prediction technique based on an improved CNN-LSTM hybrid mode to enhance the flight state prediction performance. Simulation results show that the proposed prediction technique offers better state prediction performance than the existing prediction technique, and can be operated in real-time in an on-board environment.

최근에 무인항공기의 사업화가 활발하게 추진됨에 따라 무인항공기의 안전성 확보를 위한 기술 개발에 많은 관심이 집중되고 있다. 일반적으로 무인항공기는 운용 중 급기동, 외란, 조종사 실수 등으로 인하여 조종 불능의 상태로 진입할 가능성을 지닌다. 조종 불능 상태로의 진입을 예방하기 위해서는 무인항공기의 비행 상태를 예측하는 것이 필수적으로 요구된다. 본 논문에서는 무인항공기의 비행 상태 예측 성능의 향상을 위하여 개선된 CNN-LSTM 혼합모델을 제안한다. 모의실험은 제안하는 모델을 이용한 예측 기법이 기존 예측 기법에 비하여 비행 상태 예측 성능이 우수하며 온보드 환경에서 실시간으로 운용됨을 보인다.

Keywords

Acknowledgement

본 논문은 항공우주연구원의 국토교통부 연구개발사업의 연구비 지원(21ACTO-B151664-03)에 의해 수행되었습니다.

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