Počet záznamů: 1

Non-Parametric Bayesian Measurement Noise Density Estimation in Non-Linear Filtering

  1. 1.
    0363163 - UTIA-B 2012 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
    Okzan, E. - Saha, S. - Gustafsson, F. - Šmídl, Václav
    Non-Parametric Bayesian Measurement Noise Density Estimation in Non-Linear Filtering.
    Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing 2011. Piscataway: IEEE, 2011, s. 5924-5927. ISBN 978-1-4577-0539-7.
    [IEEE International Conference on Acoustics, Speech and Signal Processing. Praha (CZ), 22.05.2011-27.05.2011]
    Výzkumný záměr: CEZ:AV0Z10750506
    Klíčová slova: Particle filtering * Dirichlet process * Bayesian Estimation
    Kód oboru RIV: BD - Teorie informace
    http://library.utia.cas.cz/separaty/2011/AS/smidl-non-parametric bayesian measurement noise density estimation in non-linear filtering.pdf http://library.utia.cas.cz/separaty/2011/AS/smidl-non-parametric bayesian measurement noise density estimation in non-linear filtering.pdf

    In this study, we investigate online Bayesian estimation of the measurement noise density of a given state space model using particle filters and Dirichlet process mixtures. Dirichlet processes are widely used in statistics for nonparametric density estimation. In the proposed method, the unknown noise is modeled as a Gaussian mixture with unknown number of components. The joint estimation of the state and the noise density is done via particle filters. Furthermore, the number of components and the noise statistics are allowed to vary in time. An extension of the method for the estimation of time varying noise characteristics is also introduced.
    Trvalý link: http://hdl.handle.net/11104/0199219