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장다현(Da-Hyun Jang),윤승원(Seung-Won Yoon),박태원(Tae-Won Park),김현주(Hyeon-Ju Kim),이규철(Kyu-Chul Lee) 대한전자공학회 2024 대한전자공학회 학술대회 Vol.2024 No.6
Air traffic is continually increasing, and the development and operation of autonomous aircraft are also expanding. Consequently, the need for accurate aircraft trajectory prediction for safe air traffic management has become increasingly important. This study develops a trajectory prediction model for low-altitude aircraft using ADS-B data, which provides real-time location information of aircraft. It utilizes the LSTM model, which is suitable for time-series prediction, and applies the concepts of look_back and forward_length to find the optimal parameters. Experimental results prove that the LSTM model exhibits superior performance in aircraft trajectory prediction, and optimal training parameters are presented.
보간기법을 적용한 AIS 데이터 기반 선박 경로 예측 딥러닝 연구
이원희(Won-Hee Lee),윤승원(Seung-Won Yoon),장다현(Da-Hyun Jang),이규철(Kyu-Chul Lee) 한국컴퓨터정보학회 2024 韓國컴퓨터情報學會論文誌 Vol.29 No.3
The research on predicting the routes of ships, which constitute the majority of maritime transportation, can detect potential hazards at sea in advance and prevent accidents. Unlike roads, there is no distinct signal system at sea, and traffic management is challenging, making ship route prediction essential for maritime safety. However, the time intervals of the ship route datasets are irregular due to communication disruptions. This study presents a method to adjust the time intervals of data using appropriate interpolation techniques for ship route prediction. Additionally, a deep learning model for predicting ship routes has been developed. This model is an LSTM model that predicts the future GPS coordinates of ships by understanding their movement patterns through real-time route information contained in AIS data. This paper presents a data preprocessing method using linear interpolation and a suitable deep learning model for ship route prediction. The experimental results demonstrate the effectiveness of the proposed method with an MSE of 0.0131 and an Accuracy of 0.9467.