TY - GEN
T1 - An Improved Algorithm for Flight Maneuver Recognition and Evaluation Based on Support Vector Machines
AU - Li, Xiaokang
AU - Zhu, Tianyi
AU - Bian, Zimu
AU - Yu, Zhuxin
AU - Gao, Xiaoguang
AU - Wan, Kaifang
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The advancement of defense capabilities relies heavily on improving air combat proficiency. Effective pilot training plays a pivotal role in achieving this goal. Simulated flight training is a critical method for training pilots, and leveraging intelligent scoring algorithms can significantly enhance pilot proficiency. In this study, we propose an enhanced SVM algorithm that incorporates PCA for dimensionality reduction. By combining pilot training-related data from flight simulators with advanced machine learning techniques, we aim to develop an intelligent digital instructor system. This system provides real-time, objective, and quantitative assessments, along with detailed diagnostic feedback to pilot trainees. Furthermore, the algorithm's potential extends beyond civilian pilot training to autonomous air combat scenarios involving UCAVs in the future.
AB - The advancement of defense capabilities relies heavily on improving air combat proficiency. Effective pilot training plays a pivotal role in achieving this goal. Simulated flight training is a critical method for training pilots, and leveraging intelligent scoring algorithms can significantly enhance pilot proficiency. In this study, we propose an enhanced SVM algorithm that incorporates PCA for dimensionality reduction. By combining pilot training-related data from flight simulators with advanced machine learning techniques, we aim to develop an intelligent digital instructor system. This system provides real-time, objective, and quantitative assessments, along with detailed diagnostic feedback to pilot trainees. Furthermore, the algorithm's potential extends beyond civilian pilot training to autonomous air combat scenarios involving UCAVs in the future.
KW - pilot training
KW - Principal Component Analysis
KW - Support Vector Machine
UR - http://www.scopus.com/inward/record.url?scp=85216586655&partnerID=8YFLogxK
U2 - 10.1109/ICCSI62669.2024.10799254
DO - 10.1109/ICCSI62669.2024.10799254
M3 - 会议稿件
AN - SCOPUS:85216586655
T3 - 2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024
BT - 2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024
Y2 - 8 November 2024 through 12 November 2024
ER -