Predictive Guidance Scheme with NAR Neural Network for Autonomous Aerial Refueling

Yufei Ma, Dongli Yuan, Jianguo Yan, Yaohong Qu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In order to improve the passive tracking situation of receiver aircraft for drogue during the docking phase of AAR (autonomous aerial refueling), a strategy of AAR predictive guidance scheme based on NAR (Nonlinear Auto Regressive) neural network model is proposed. The NAR neural network model is used to predict the future position of the drogue as the target point of receiver aircraft. The simulation is carried out by Monte Carlo target test. The simulation results indicate that the proposed algorithm possesses high prediction precision and significantly enhances the success rate of AAR, which is of crucial importance in achievement of AAR technique.

Original languageEnglish
Title of host publication2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538611715
DOIs
StatePublished - Aug 2018
Event2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018 - Xiamen, China
Duration: 10 Aug 201812 Aug 2018

Publication series

Name2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018

Conference

Conference2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018
Country/TerritoryChina
CityXiamen
Period10/08/1812/08/18

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