Flight maneuver intelligent recognition based on deep variational autoencoder network

The selection and training of aircraft pilots has high standards, long training cycles, high resource consumption, high risk, and high elimination rate. It is the particularly urgent and important requirement for the current talent training strategy of national and military to increase efficiency an...

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Veröffentlicht in:EURASIP journal on advances in signal processing Jg. 2022; H. 1; S. 1 - 23
Hauptverfasser: Tian, Wei, Zhang, Hong, Li, Hui, Xiong, Yuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 12.03.2022
Springer
Springer Nature B.V
SpringerOpen
Schlagworte:
Aircraft maneuvers
Aircraft pilots
Algorithms
Artificial Intelligence and Cognitive Computing for Smart Internet of Vehicle
Coders
Consumption (Economics)
Data mining
Deep neural network
Dynamic time warping
Engineering
Flight maneuver recognition
Flight quality evaluation
Flight training
Pilot training
Pilots
Pilots and pilotage
Quality assessment
Quantum Information Technology
Recognition
Signal,Image and Speech Processing
Spintronics
Variational autoencoder
Variational autoencoder
Flight quality evaluation
Deep neural network
Flight maneuver recognition
Dynamic time warping
ISSN:1687-6180, 1687-6172, 1687-6180
Online-Zugang:Volltext
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Zusammenfassung:The selection and training of aircraft pilots has high standards, long training cycles, high resource consumption, high risk, and high elimination rate. It is the particularly urgent and important requirement for the current talent training strategy of national and military to increase efficiency and speed up all aspects of pilot training, reduce the training cycle and reduce the elimination rate. To this end, this paper uses deep variational auto-encoder network and adaptive dynamic time warping algorithms as support to explore the establishment of an integrated evaluation system for flight maneuver recognition and quality evaluation, solve the industry difficulty faced by current flight training data mining applications, and achieve accurate recognition and reliable quality evaluation of flight regimes under the background of high mobility. It will fully explore the benefits of existing airborne flight data for military trainee pilots, support the personalized and accurate training of flight talents, and reduce the rate of talent elimination.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1186/s13634-022-00850-x