An expert data-driven air combat maneuver model learning approach

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 279
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorPark, Su-jeongko
dc.contributor.authorPark, Soon-seoko
dc.contributor.authorChoi,Han-limko
dc.contributor.authorAn,Kyeong-sooko
dc.contributor.authorKim, Young-gonko
dc.date.accessioned2021-11-04T06:46:29Z-
dc.date.available2021-11-04T06:46:29Z-
dc.date.created2021-10-26-
dc.date.issued2021-01-
dc.identifier.citationAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021, pp.1 - 12-
dc.identifier.urihttp://hdl.handle.net/10203/288809-
dc.description.abstractThis paper considers the problem of a learning air combat maneuver model when an expert pilot’s trajectories are given. Most studies of imitation learning require large amount of data for training and have to interact with real environments, even under uncertain dynamics of enemy aircraft. Thus, we propose a new approach to solve this problem by: (i) training an internal model that can represent future states and imitate the maneuvering of an expert using MDN-RNN and a controller and (ii) generating expert-like trajectories via a dreaming process, which imagines an engagement situation in a hypothetical environment model. This approach does not require interaction with the real environment nor a reward function for training. We demonstrate the similarity between the expert trajectory and the trajectory reconstructed by the proposed model.-
dc.languageEnglish-
dc.publisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA-
dc.titleAn expert data-driven air combat maneuver model learning approach-
dc.typeConference-
dc.identifier.scopusid2-s2.0-85100321775-
dc.type.rimsCONF-
dc.citation.beginningpage1-
dc.citation.endingpage12-
dc.citation.publicationnameAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021-
dc.identifier.conferencecountryUS-
dc.identifier.conferencelocationNashville, TN-
dc.contributor.localauthorChoi,Han-lim-
dc.contributor.nonIdAuthorPark, Su-jeong-
dc.contributor.nonIdAuthorPark, Soon-seo-
dc.contributor.nonIdAuthorAn,Kyeong-soo-
dc.contributor.nonIdAuthorKim, Young-gon-
Appears in Collection
AE-Conference Papers(학술회의논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0