Abstract:

The quality evaluation of flight training is an important part of pilots'daily flight training. Aiming at the problems of low efficiency and strong subjectivity of traditional flight training quality evaluation methods, an automatic evaluation method based on maneuver action recognition is proposed. Firstly, by constructing a multi-scale feature extraction deep residual network (MSDRN), accurate recognition of tactical maneuver actions is achieved, which overcomes the problem of low recognition accuracy in traditional maneuver action recognition methods. Secondly, aiming at different types of tactical maneuver action, the flight quality evaluation index system is constructed. During the specific evaluation process, the evaluation index is automatically selected according to the action recognition results, and the evaluation results are calculated and obtained by the combination weighting method based on the game theory. Thus, the automatic evaluation method of pilot flight training quality based on maneuver action type recognition is constructed. Compared with the classifiers constructed by traditional one-dimensional convolutional neural network (1D-CNN) and residual neural network (ResNet), the established action recognition method has improved recognition accuracy by 16.2% and 3.5% respectively, and reduced the recognition computation time by 23.9% compared with ResNet. The proposed overall automatic evaluation method for the whole flight training quality gets rid of the serious dependence of the pilot on the flight instructor in the training process, which realizes the automatic and rapid evaluation of the flight training quality. The evaluation results are more scientific, providing a new method for the flight training quality evaluation.

Key words: maneuver recognition, multi-scale feature, deep residual network (DRN), the game theory, flight training evaluation