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Approximate Bayesian Approach to Non-Gaussian Estimation in a Linear Model with Dependent State and Noise Vectors |
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| Authors: | H S Hoang R Baraille O Talagrand P DeMey |
| Institution: | SHOM$/$GRGS$/$CNRS$/$CNES, 18 Avenue Edouard Belin, 31401 Toulouse Cédex 4, France hoang@thor.cst.cnes.fr, FR SHOM$/$GRGS$/$CMO, 18 Avenue Edouard Belin, 31401 Toulouse Cédex 4, France baraille@thor.cst.cnes.fr, FR LMD$/$ENS, 24 Rue Lhomond, 75231 Paris C&;eacute;dex 05, France talagran@ella.ens.fr, FR GRGS$/$CNRS, 18 Avenue Edouard Belin, 31401 Toulouse C&;amp;eacute&;semi;dex 4, France demey@thor.cst.cnes.fr, FR |
| Abstract: | This paper extends the results of Masreliez 8] on the design of non-Gaussian estimators for a more general class of the parameter estimation problem when the system state and the observation noise may be dependent and non-Gaussian simultaneously. It is shown that the proposed non-Gaussian algorithms can approximate with high precision the minimum mean square estimator. Application of the approach to the design of different optimal (and stable) estimation algorithms is illustrated. The efficiency of the proposed algorithms is tested in some simulation experiments. Accepted 5 September 2000. Online publication 26 February 2001. |
| Keywords: | , Linear model, Non-Gaussian estimation, Robust Bayesian estimation, AMS Classification, 62H12, 62J12 |
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