Automatic Maneuver Detection in Flight Data using Wavelet Transform and Deep Learning Algorithms 2024-26-0462

The evaluation of aircraft characteristics through flight test maneuvers is fundamental to aviation safety and understanding flight attributes. This research project proposes a comprehensive methodology to detect and analyze aircraft maneuvers using full flight data, combining signal processing and machine learning techniques. Leveraging the Wavelet Transform, we unveil intricate temporal details within flight data, uncovering critical time-frequency insights essential for aviation safety. The integration of Long Short-Term Memory (LSTM) models enhances our ability to capture temporal dependencies, surpassing the capabilities of machine learning in isolation. These extracted maneuvers not only aid in safety but also find practical applications in system identification, air-data calibration, and performance analysis, significantly reducing pre-processing time for analysts. Moreover, they serve as a foundational component for developing fault-tolerant models, thereby contributing to accident reduction, casualty prevention, and bolstered public confidence in air travel. This project underscores the significance of interdisciplinary collaboration and highlights the immense potential of signal processing and machine learning fusion in shaping the future of aviation research and applications.