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A Rao-Blackwellized particle filter to estimate the time-varying noise parameters in non-linear state-space models using alternative stabilized forgetting

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F16%3APU122022" target="_blank" >RIV/00216305:26220/16:PU122022 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1109/ISSPIT.2016.7886040" target="_blank" >http://dx.doi.org/10.1109/ISSPIT.2016.7886040</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ISSPIT.2016.7886040" target="_blank" >10.1109/ISSPIT.2016.7886040</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    A Rao-Blackwellized particle filter to estimate the time-varying noise parameters in non-linear state-space models using alternative stabilized forgetting

  • Original language description

    The identification of slowly-varying noise parameters in non-linear state-space models constitutes a long-standing problem. The present paper addresses this task using the Bayesian framework and sequential Monte Carlo (SMC) methodology. The proposed approach utilizes an algebraic structure of the model so that the Rao-Blackwellization of the parameters can be performed, thus involving a finite-dimensional sufficient statistic for each particle trajectory into the resulting algorithm. However, relying on standard SMC methods, such techniques are known to suffer from the particle path degeneracy problem. To counteract this issue, it is proposed to use alternative stabilized forgetting, which compensates for the incomplete knowledge of a model of parameter variations by finding a compromise between possible predictive densities of the parameters. An experimental study proves the efficiency of the introduced Rao-Blackwellized particle filter (RBPF) compared to some recently proposed approaches.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/LQ1601" target="_blank" >LQ1601: CEITEC 2020</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2016

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of the 16th International Symposium on Signal Processing and Information Technology, ISSPIT 2016

  • ISBN

    978-1-5090-5844-0

  • ISSN

  • e-ISSN

  • Number of pages

    6

  • Pages from-to

    229-234

  • Publisher name

    Institute of Electrical and Electronics Engineers

  • Place of publication

    Limassol

  • Event location

    Limassol

  • Event date

    Dec 12, 2016

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000406122500042