• DocumentCode
    2567460
  • Title

    Estimating state-space models in innovations form using the expectation maximisation algorithm

  • Author

    Wills, Adrian ; Schön, Thomas B. ; Ninness, Brett

  • Author_Institution
    Sch. of Electr. Eng. & Comput. Sci., Univ. of Newcastle, Callaghan, NSW, Australia
  • fYear
    2010
  • fDate
    15-17 Dec. 2010
  • Firstpage
    5524
  • Lastpage
    5529
  • Abstract
    The expectation maximisation (EM) algorithm has proven to be effective for a range of identification problems. Unfortunately, the way in which the EM algorithm has previously been applied has proven unsuitable for the commonly employed innovations form model structure. This paper addresses this problem, and presents a previously unexamined method of EM algorithm employment. The results are profiled, which indicate that a hybrid EM/gradient-search technique may in some cases outperform either a pure EM or a pure gradient-based search approach.
  • Keywords
    expectation-maximisation algorithm; identification; state-space methods; expectation maximisation algorithm; gradient-based search; identification problem; innovation; state-space model; Algorithm design and analysis; Data models; Joints; Kalman filters; Maximum likelihood estimation; Technological innovation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control (CDC), 2010 49th IEEE Conference on
  • Conference_Location
    Atlanta, GA
  • ISSN
    0743-1546
  • Print_ISBN
    978-1-4244-7745-6
  • Type

    conf

  • DOI
    10.1109/CDC.2010.5717145
  • Filename
    5717145