Iterated minimum upper bound filter for tracking orbit maneuvering targets

Hua Lan, Yan Liang, Wei Zhang, Feng Yang, Quan Pan

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

3 Scopus citations

Abstract

In this paper, the movement of a maneuvering low earth orbit satellite is modeled by a nonlinear stochastic system with unknown disturbance input, and an Iterated Minimum Upper Bound Filter is proposed to decrease the upper bound of the covariance of estimate errors via iterative optimization. The Monte Carlo simulation shows that the proposed filter significantly reduces the peak estimation errors due to orbit maneuvers compared with the well-known interacting multiple model method. Besides, it can accurately detect the target maneuvering time instant through thresholding the estimated fading factor.

Original languageEnglish
Title of host publicationProceedings of the 16th International Conference on Information Fusion, FUSION 2013
Pages1051-1057
Number of pages7
StatePublished - 2013
Event16th International Conference of Information Fusion, FUSION 2013 - Istanbul, Turkey
Duration: 9 Jul 201312 Jul 2013

Publication series

NameProceedings of the 16th International Conference on Information Fusion, FUSION 2013

Conference

Conference16th International Conference of Information Fusion, FUSION 2013
Country/TerritoryTurkey
CityIstanbul
Period9/07/1312/07/13

Keywords

  • iterative minimum upper bound filter
  • iterative optimization
  • orbit maneuver

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