항공기-조류 충돌 위험도 평가를 이용한 시뮬레이션 프로그램 구현
Development of a Simulation Framework for Bird–Aircraft Collision Risk Assessment
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This paper proposes a web-based simulation to predict and visualize the risk of bird strikes around airports. Unlike conventional high-cost equipment, such as radar and thermal imaging cameras, the proposed system estimates collision probabilities in a low-cost and effective manner using positional data from migratory birds and aircraft. Bird risk levels are quantified by incorporating species-specific attributes, including body mass, flock size, and flight characteristics, along with seasonal occurrence patterns. To forecast bird trajectories, the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF) algorithms are applied and compared. Subsequently, a probabilistic model integrating these elements is used to compute the real-time collision risk. Experimental results indicate that the EKF, UKF, and PF exhibit similar performances in terms of real-time prediction, with the PF demonstrating superior accuracy in short-term forecasts under high uncertainty. The web platform developed using React and a map-based interface intuitively visualizes both aircraft routes and bird movements while supporting filtering by airport, season, and bird species. In general, the findings confirm that a practical and scalable bird-strike management system can be implemented without relying on costly sensors. Moreover, the system exhibits strong potential applications in private airports, military airfields, and drone operation zones, as well as offers future extensions to multisensor integration and machine learning–based risk assessment.
Abstract
This paper proposes a web-based simulation to predict and visualize the risk of bird strikes around airports. Unlike conventional high-cost equipment, such as radar and thermal imaging cameras, the proposed system estimates collision probabilities in a low-cost and effective manner using positional data from migratory birds and aircraft. Bird risk levels are quantified by incorporating species-specific attributes, including body mass, flock size, and flight characteristics, along with seasonal occurrence patterns. To forecast bird trajectories, the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF) algorithms are applied and compared. Subsequently, a probabilistic model integrating these elements is used to compute the real-time collision risk. Experimental results indicate that the EKF, UKF, and PF exhibit similar performances in terms of real-time prediction, with the PF demonstrating superior accuracy in short-term forecasts under high uncertainty. The web platform developed using React and a map-based interface intuitively visualizes both aircraft routes and bird movements while supporting filtering by airport, season, and bird species. In general, the findings confirm that a practical and scalable bird-strike management system can be implemented without relying on costly sensors. Moreover, the system exhibits strong potential applications in private airports, military airfields, and drone operation zones, as well as offers future extensions to multisensor integration and machine learning–based risk assessment.