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  • Deterministic Sampling for Nonlinear Dynamic State Estimation

    Igor Gilitschenski

    Band 18 von Karlsruhe Series on Intelligent Sensor-Actuator-Systems
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    The goal of this work is improving existing and suggesting novel filtering algorithms for nonlinear dynamic state estimation. Nonlinearity is considered in two ways: First, propagation is improved by proposing novel methods for approximating continuous probability distributions by discrete distributions defined on the same continuous domain. Second, nonlinear underlying domains are considered by proposing novel filters that inherently take the underlying geometry of these domains into account.

    Umfang: XVI, 167 S.

    Preis: €41.00 | £38.00 | $72.00

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    Empfohlene Zitierweise
    Gilitschenski, I. 2016. Deterministic Sampling for Nonlinear Dynamic State Estimation. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000051670
    Gilitschenski, I., 2016. Deterministic Sampling for Nonlinear Dynamic State Estimation. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000051670
    Gilitschenski, I. Deterministic Sampling for Nonlinear Dynamic State Estimation. KIT Scientific Publishing, 2016. DOI: https://doi.org/10.5445/KSP/1000051670
    Gilitschenski, I. (2016). Deterministic Sampling for Nonlinear Dynamic State Estimation. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000051670
    Gilitschenski, Igor. 2016. Deterministic Sampling for Nonlinear Dynamic State Estimation. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000051670




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    Weitere Informationen

    Veröffentlicht am 19. April 2016

    Sprache

    Englisch

    Seitenanzahl:

    200

    ISBN
    Paperback 978-3-7315-0473-3

    DOI
    https://doi.org/10.5445/KSP/1000051670