Space object characterization and classification using multiple model approach = 다중 모델 기법을 고려한 우주 물체 특성 추정 및 분류 방법

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 44
  • Download : 0
This thesis addresses the research on characterization and classification of a space object at low Earth orbit using a multiple model approach. Space situational awareness has become a global interest as many industries started diving into the space market for commercialization of the resident space. The states of a space object can be estimated based on the orbital mechanics, but the inference of the object's characteristics is difficult while it is necessary when there is no a priori information about the detected object that its re-entry trajectory needs to be estimated. Thus, this thesis introduces an algorithm that estimates the ballistic coefficient of an object and classifies into a catalog depending on its shape with the multiple-model adaptive estimation that uses a parallel set of Kalman filters with multiple models. The simulation is done at the altitude of 300 km where the re-entry is expected within approximately two months, and the increase in accuracy of ballistic coefficient estimate through the moving-bank MMAE is shown. Also, the feasibility of supplementing the lack of data using techniques that speed up the convergence in the algorithm is presented.
Advisors
Choi, Han-Limresearcher최한림researcher
Description
한국과학기술원 :항공우주공학과,
Publisher
한국과학기술원
Issue Date
2020
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 항공우주공학과, 2020.8,[vi, 58 p. :]

Keywords

Multiple-model▼aKalman filter▼aMMAE▼aspace object▼aballistic coefficient▼aparameter estimation▼aspace situational awareness; 다중 모델▼a칼만 필터▼a다중 모델 적응 추정▼a우주 물체▼a탄도 계수▼a파라미터 추정▼a우주 상황 인식

URI
http://hdl.handle.net/10203/284967
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=925127&flag=dissertation
Appears in Collection
AE-Theses_Master(석사논문)
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