Number of the records: 1  

Nonlinear bayesian state filtering with missing measurements and bounded noise and its application to vehicle position estimation

  1. 1.
    0360239 - ÚTIA 2012 RIV CZ eng J - Journal Article
    Pavelková, Lenka
    Nonlinear bayesian state filtering with missing measurements and bounded noise and its application to vehicle position estimation.
    Kybernetika. Roč. 47, č. 3 (2011), s. 370-384. ISSN 0023-5954
    R&D Projects: GA MŠk 1M0572
    Institutional research plan: CEZ:AV0Z10750506
    Keywords : non-linear state space model * bounded uncertainty * missing measurements * state filtering * vehicle position estimation
    Subject RIV: BC - Control Systems Theory
    Impact factor: 0.454, year: 2011
    http://library.utia.cas.cz/separaty/2011/AS/pavelkova-0360239.pdf

    The paper deals with parameter and state estimation and focuses on two problems that frequently occur in many practical applications: (i) bounded uncertainty and (ii) missing measurement data. An algorithm for the state estimation of the discrete-time non-linear state space model whose uncertainties are bounded is proposed. The algorithm also copes with situations when some measurements are missing. It uses Bayesian approach and evaluates maximum a posteriori probability (MAP) estimates of states and parameters. As the model uncertainties are supposed to have a bounded support, the searched estimates lie within an area that is described by the system of inequalities. In consequence, the problem of MAP estimation becomes the problem of nonlinear mathematical programming (NLP). The estimation with missing data reduces to the omission of corresponding inequalities in NLP formulation.
    Permanent Link: http://hdl.handle.net/11104/0197835
    FileDownloadSizeCommentaryVersionAccess
    0360239.pdf1302.4 KBPublisher’s postprintopen-access
     
Number of the records: 1