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Point-Mass Filter with Decomposition of Transient Density

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F22%3A00559994" target="_blank" >RIV/67985556:_____/22:00559994 - isvavai.cz</a>

  • Alternative codes found

    RIV/49777513:23520/22:43966076

  • Result on the web

    <a href="http://dx.doi.org/10.1109/ICASSP43922.2022.9747607" target="_blank" >http://dx.doi.org/10.1109/ICASSP43922.2022.9747607</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ICASSP43922.2022.9747607" target="_blank" >10.1109/ICASSP43922.2022.9747607</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Point-Mass Filter with Decomposition of Transient Density

  • Original language description

    The paper deals with the state estimation of nonlinear stochastic dynamic systems with special attention on a grid-based numerical solution to the Bayesian recursive relations, the point-mass filter (PMF). In the paper, a novel functional decomposition of the transient density describing the system dynamics is proposed. The decomposition is based on a non-negative matrix factorization and separates the density into functions of the future and current states. Such decomposition facilitates a thrifty calculation of the convolution, which is a bottleneck of the PMF performance. The PMF estimate quality and computational costs can be efficiently controlled by choosing an appropriate rank of the decomposition. The performance of the PMF with the transient density decomposition is illustrated in a terrain-aided navigation scenario.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20205 - Automation and control systems

Result continuities

  • Project

    <a href="/en/project/GA22-11101S" target="_blank" >GA22-11101S: Tensor Decomposition in Active Fault Diagnosis for Stochastic Large Scale Systems</a><br>

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2022

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    2022 IEEE International Conference on Acoustics, Speech, and Signal Processing

  • ISBN

    978-1-6654-0540-9

  • ISSN

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

    5752-5756

  • Publisher name

    IEEE

  • Place of publication

    Singapore

  • Event location

    Singapur

  • Event date

    May 22, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000864187906010