A General Framework for Flight Maneuvers Automatic Recognition

Jing Lu, Hongjun Chai and Ruchun Jia
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Jing Lu: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Hongjun Chai: College of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China
Ruchun Jia: Wangjiang Campus, Sichuan University, Chengdu 610065, China

Mathematics, 2022, vol. 10, issue 7, 1-15

Abstract: Flight Maneuver Recognition (FMR) refers to the automatic recognition of a series of aircraft flight patterns and is a key technology in many fields. The chaotic nature of its input data and the professional complexity of the identification process make it difficult and expensive to identify, and none of the existing models have general generalization capabilities. A general framework is proposed in this paper, which can be used for all kinds of flight tasks, independent of the aircraft type. We first preprocessed the raw data with unsupervised clustering method, segmented it into maneuver sequences, then reconstructed the sequences in phase space, calculated their approximate entropy, quantitatively characterized the sequence complexity, and distinguished the flight maneuvers. Experiments on a real flight training dataset have shown that the framework can quickly and correctly identify various flight maneuvers for multiple aircraft types with minimal human intervention.

Keywords: Flight Maneuver Recognition (FMR); unsupervised clustering; phase space reconstruction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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