Deep learning for Flight Maneuver Recognition: A survey

Abstract

Deep learning for Flight Maneuver Recognition involves flight maneuver detection and recognition tasks in different areas, including pilot training, aviation safety, and autonomous air combat. As a key technology for these applications, deep learning for Flight Maneuver Recognition research is underdeveloped and limited by domain knowledge and data sources. This paper presents a comprehensive survey of all Flight Maneuver Recognition studies since the 1980s to accurately define the research and describe its significance for the first time. In an analogy to the flourishing Human Action Recognition research, we divided deep learning for Flight Maneuver Recognition into vision-based and sensor-based studies, combed through all the literature, and referred to existing reviews of Human Action Recognition to demonstrate the similarities and differences between Flight Maneuver Recognition and Human Action Recognition in terms of problem essentials, research methods, and publicly available datasets. This paper presents the dataset-The Civil Aviation Flight University of China, which was generated from real training of a fixed-wing flight at Civil Aviation Flight University of China. We used this dataset to reproduce and evaluate several important methods of Flight Maneuver Recognition and visualize the results. Based on the evaluation results, the paper discusses the advantages, disadvantages, and overall shortcomings of these methods, as well as the challenges and future directions for deep learning for Flight Maneuver Recognition.

Keywords

• deep learning
• flight maneuver recognition
• vision-based
• sensor-based
• civil aviation flight university of china
• challenges in flight maneuver recognition