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Displaying similar documents to “Nonlinear Bayesian state filtering with missing measurements and bounded noise and its application to vehicle position estimation”

Bayesian estimation of mixtures with dynamic transitions and known component parameters

Ivan Nagy, Evgenia Suzdaleva, Miroslav Kárný (2011)

Kybernetika

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Probabilistic mixtures provide flexible “universal” approximation of probability density functions. Their wide use is enabled by the availability of a range of efficient estimation algorithms. Among them, quasi-Bayesian estimation plays a prominent role as it runs “naturally” in one-pass mode. This is important in on-line applications and/or extensive databases. It even copes with dynamic nature of components forming the mixture. However, the quasi-Bayesian estimation relies on mixing...

Set membership estimation of parameters and variables in dynamic networks by recursive algorithms with a moving measurement window

Kazimierz Duzinkiewicz (2006)

International Journal of Applied Mathematics and Computer Science

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The paper considers a set membership joint estimation of variables and parameters in complex dynamic networks based on parametric uncertain models and limited hard measurements. A recursive estimation algorithm with a moving measurement window is derived that is suitable for on-line network monitoring. The window allows stabilising the classic recursive estimation algorithm and significantly improves estimate tightness. The estimator is validated on a case study regarding a water distribution...

Reduced-order Unscented Kalman Filtering with application to parameter identification in large-dimensional systems

Philippe Moireau, Dominique Chapelle (2011)

ESAIM: Control, Optimisation and Calculus of Variations

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We propose a general reduced-order filtering strategy adapted to Unscented Kalman Filtering for any choice of sampling points distribution. This provides tractable filtering algorithms which can be used with large-dimensional systems when the uncertainty space is of reduced size, and these algorithms only invoke the original dynamical and observation operators, namely, they do not require tangent operator computations, which of course is of considerable benefit when nonlinear operators...