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Resampling-free Stochastic Integration Filter

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F20%3A43959775" target="_blank" >RIV/49777513:23520/20:43959775 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.23919/FUSION45008.2020.9190535" target="_blank" >https://doi.org/10.23919/FUSION45008.2020.9190535</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.23919/FUSION45008.2020.9190535" target="_blank" >10.23919/FUSION45008.2020.9190535</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Resampling-free Stochastic Integration Filter

  • Original language description

    The paper deals with the state estimation of nonlinear stochastic systems with additive Gaussian noises by means of the Gaussian filters leveraging numerical integration rules. The filters were derived under the assumption of the joint state and measurement predictive density being Gaussian, which is violated by the system nonlinearity. Such violation can hardly be monitored by the standard Gaussian filters, which re-generate a new set of points for each involved numerical integration to accommodate their variance increase due to the additive noises. The paper proposes a stochastic integration filter algorithm that modifies the points instead of their resampling and thus admits reusing the points in the next time steps. The distribution of the points can thus bear more information than just the first two moments in case of the standard Gaussian filters. The acquired information is then utilized for the Gaussian assumption monitoring purposes. In the event of the assumption violation, the filter may change its behavior. As a by-product of reusing the points, the computational costs of the proposed filter are significantly reduced compared to the standard stochastic integration filter.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20205 - Automation and control systems

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

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

Others

  • Publication year

    2020

  • 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 2020 IEEE 23rd International Conference on Information Fusion (FUSION)

  • ISBN

    978-0-578-64709-8

  • ISSN

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    1-8

  • Publisher name

    IEEE

  • Place of publication

    Rustenburg

  • Event location

    Rustenburg, Jihoafrická republika

  • Event date

    Jul 6, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article