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State estimate consistency monitoring in Gaussian filtering framework

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F18%3A43952473" target="_blank" >RIV/49777513:23520/18:43952473 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1016/j.sigpro.2018.02.013" target="_blank" >https://doi.org/10.1016/j.sigpro.2018.02.013</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.sigpro.2018.02.013" target="_blank" >10.1016/j.sigpro.2018.02.013</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    State estimate consistency monitoring in Gaussian filtering framework

  • Original language description

    The paper deals with the state estimation of the nonlinear stochastic dynamic systems by inherently approximate Gaussian filters. In particular, the stress is laid on the evaluation of the Gaussian filter state estimate consistency, which is a key indicator of the filter correct functionality and, thus, vital information for safety-critical applications. A novel on-line state estimate consistency monitoring test is proposed. Compared to the state-of-the-art tests, the proposed test directly works in the state-space domain without an assumption on the known true system state. Design of the test is free of specification or tuning of any threshold by the user. The proposed test is thoroughly analysed and compared with other tests on a theoretical and simulation basis.

  • 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

    20205 - Automation and control systems

Result continuities

  • Project

    <a href="/en/project/GC16-19999J" target="_blank" >GC16-19999J: Cooperative Approaches to Design of Nonlinear Filters</a><br>

  • Continuities

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

Others

  • Publication year

    2018

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Signal Processing

  • ISSN

    0165-1684

  • e-ISSN

  • Volume of the periodical

    148

  • Issue of the periodical within the volume

    July 2018

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    12

  • Pages from-to

    145-156

  • UT code for WoS article

    000428824600014

  • EID of the result in the Scopus database

    2-s2.0-85042300699