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State and parameter estimation of state‐space model with entry‐wise correlated uniform noise
Author(s) -
Pavelková Lenka,
Kárný Miroslav
Publication year - 2014
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2438
Subject(s) - estimator , noise (video) , state (computer science) , bounded function , state space , state space representation , estimation , bayes estimator , estimation theory , mathematics , a priori and a posteriori , space (punctuation) , bayesian probability , maximum a posteriori estimation , computer science , maximum likelihood , algorithm , statistics , artificial intelligence , engineering , mathematical analysis , philosophy , systems engineering , epistemology , image (mathematics) , operating system
SUMMARY Joint parameter and state estimation is proposed for linear state‐space model with uniform, entry‐wise correlated, state and output noises ( LSU model for short). The adopted Bayesian modelling and approximate estimation produce an estimator that (a) provides the maximum a posteriori estimate enriched by information on its precision, (b) respects correlated noise entries without demanding the user to tune noise covariances, and (c) respects bounded nature of real‐life variables. Copyright © 2013 John Wiley & Sons, Ltd.