Quantitative Evaluation Method for Anomaly Levels of Complex Flight Maneuver Based on Multi-sensor Data

The methods that identify complex flight maneuvers from multi-sensor flight parameter data fusion and conduct automated quantitative evaluations of anomaly levels could play an important role in enhancing flight safety and pilot training. However, existing methods focus on anomaly detection at individual flight parameter data points, making it challenging to accurately quantify the overall abnormality of a flight maneuver. To address this issue, this paper proposes a novel method for the quantitative evaluation of anomaly levels in complex flight maneuvers using multi-sensor data. The proposed method comprises two stages: complex flight maneuver recognition and anomaly level quantification. In the complex flight maneuver recognition stage, a one-dimensional dual attention mechanism (1D-DAM) is introduced to capture discriminative fusion features in both the temporal and variable dimensions. Based on this mechanism, we develop a one-dimensional dual attention mechanism ResNet (1D-DAMResNet) model to achieve the recognition of complex flight maneuvers. Subsequently, in the anomaly level quantification stage, we employ a clustering technique to establish a standard maneuver benchmark library, which serves as a reference for the flight maneuver evaluations of different categories. According to the results of flight maneuver recognition, the corresponding category of standard maneuver from the library is automatically selected, and the dynamic time warping algorithm is then utilized to compute the anomaly quantification score of the test maneuver, thereby determining its anomaly level. Compared to contrastive methods, the proposed complex flight maneuver recognition model demonstrates significant advantages in both accuracy and stability, with an average precision, recall, and F1 scores of 99.75%. Additionally, the proposed anomaly level quantification method provides an automatic quantification of the overall anomaly level of maneuvers, and the results are highly interpretable. Overall, this paper introduces a novel approach for the quantitative evaluation of anomaly levels in maneuvers, which not only contributes to improving the accuracy of flight training evaluation but also significantly enhances the efficiency and quality of flight training.