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Adaptive kernels in approximate filtering of state-space models

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F17%3A00466448" target="_blank" >RIV/67985556:_____/17:00466448 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1002/acs.2739" target="_blank" >http://dx.doi.org/10.1002/acs.2739</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1002/acs.2739" target="_blank" >10.1002/acs.2739</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Adaptive kernels in approximate filtering of state-space models

  • Original language description

    Standard Bayesian algorithms used for online filtering of states of hidden Markov models from noisy measurements assume an accurate knowledge of the measurement model in the form of a conditional probability density function. However, this knowledge is often unreachable in practice, and the used models are more or less misspecified, or it is too complex, making the resulting models intractable. This paper focuses on these issues from the particle filtering perspective. It adopts the principles of the approximate Bayesian filtering, where the particle weights are based on the (dis)similarity of the true measurements and the pseudo-measurements obtained by plugging the state particles directly into the measurement equation. Specifically, a new robust method for online tuning of the weighting kernel is proposed.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10103 - Statistics and probability

Result continuities

  • Project

    <a href="/en/project/GP14-06678P" target="_blank" >GP14-06678P: Distributed dynamic estimation in diffusion networks</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2017

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    International Journal of Adaptive Control and Signal Processing

  • ISSN

    0890-6327

  • e-ISSN

  • Volume of the periodical

    31

  • Issue of the periodical within the volume

    6

  • Country of publishing house

    GB - UNITED KINGDOM

  • Number of pages

    15

  • Pages from-to

    938-952

  • UT code for WoS article

    000403462300006

  • EID of the result in the Scopus database

    2-s2.0-84997787314