• DocumentCode
    1384410
  • Title

    Multiple model bootstrap filter for maneuvering target tracking

  • Author

    McGinnity, Shaun ; Irwin, George W.

  • Author_Institution
    Dept. of Electr. & Electron. Eng., Queen´´s Univ., Belfast, UK
  • Volume
    36
  • Issue
    3
  • fYear
    2000
  • fDate
    7/1/2000 12:00:00 AM
  • Firstpage
    1006
  • Lastpage
    1012
  • Abstract
    The extension of the bootstrap filter to the multiple model target tracking problem is considered. Bayesian bootstrap filtering is a very powerful technique since it represents samples by random samples and is therefore not restricted to linear, Gaussian systems, making it ideal for the multiple model problem where very complex densities can be generated
  • Keywords
    Bayes methods; Markov processes; bootstrap circuits; nonlinear systems; parameter estimation; probability; target tracking; Bayesian bootstrap filtering; Markov model; complex densities; linear Gaussian systems; maneuvering target tracking; multiple model bootstrap filter; random samples; Bayesian methods; Density functional theory; Matched filters; Merging; Power generation; Power system modeling; State estimation; State-space methods; Switches; Target tracking;
  • fLanguage
    English
  • Journal_Title
    Aerospace and Electronic Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9251
  • Type

    jour

  • DOI
    10.1109/7.869522
  • Filename
    869522
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1384410