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Expected-mode augmentation algorithms for variable-structure multiple-model estimation

  • X. Rong Li
  • , Vesselin P. Jilkov
  • , Jifeng Ru
  • , Anwer Bashi
  • University of New Orleans

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

7 Scopus citations

Abstract

This paper presents a new class of variable-structure algorithms, referred to as expected-mode augmentation (EMA), for multiple-model estimation. In this approach, the original model set is augmented by a variable set of models intended to match the expected value of the unknown true mode. These models are generated adaptively in real time as (globally or locally) probabilistically weighted sums of modal states over the model set. This makes it possible to cover a large continuous mode space by a relatively small number of models at a given accuracy level. Performance of the proposed EMA algorithms is evaluated via a simulated example of a maneuvering target tracking problem.

Original languageEnglish
Title of host publicationIFAC Proceedings Volumes (IFAC-PapersOnline)
EditorsGabriel Ferrate, Eduardo F. Camacho, Luis Basanez, Juan. A. de la Puente
PublisherIFAC Secretariat
Pages175-180
Number of pages6
Edition1
ISBN (Print)9783902661746
DOIs
StatePublished - 2002
Externally publishedYes
Event15th World Congress of the International Federation of Automatic Control, 2002 - Barcelona, Spain
Duration: 21 Jul 200226 Jul 2002

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
Number1
Volume15
ISSN (Print)1474-6670

Conference

Conference15th World Congress of the International Federation of Automatic Control, 2002
Country/TerritorySpain
CityBarcelona
Period21/07/0226/07/02

Keywords

  • Adaptive estimation
  • IMM
  • Multiple model
  • Target tracking
  • Variable structure

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