Approximate Bayesian Approach to Non-Gaussian Estimation in a Linear Model with Dependent State and Noise Vectors

Approximate Bayesian Approach to Non-Gaussian Estimation in a Linear Model with Dependent State... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Mathematics and Optimization Springer Journals

Approximate Bayesian Approach to Non-Gaussian Estimation in a Linear Model with Dependent State and Noise Vectors

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Publisher
Springer-Verlag
Copyright
Copyright © 2001 by Springer-Verlag New York Inc.
Subject
Mathematics; Calculus of Variations and Optimal Control; Optimization; Systems Theory, Control; Theoretical, Mathematical and Computational Physics; Mathematical Methods in Physics; Numerical and Computational Physics
ISSN
0095-4616
eISSN
1432-0606
D.O.I.
10.1007/s00245-001-0005-y
Publisher site
See Article on Publisher Site

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