An Improved Algorithm for Flight Maneuver Recognition and Evaluation Based on Support Vector Machines

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.