NDLI: Estimation of Constrained Parameters With Guaranteed MSE Improvement

Content Provider	IEEE Xplore Digital Library
Author	Benavoli, A. Chisci, L. Farina, A.
Copyright Year	1991
Abstract	We address the problem of estimating an unknown parameter vector x in a linear model y=Cx+v subject to the a priori information that the true parameter vector x belongs to a known convex polytope X. The proposed estimator has the parametrized structure of the maximum a posteriori probability (MAP) estimator with prior Gaussian distribution, whose mean and covariance parameters are suitably designed via a linear matrix inequality approach so as to guarantee, for any xisinX, an improvement of the mean-squared error (MSE) matrix over the least-squares (LS) estimator. It is shown that this approach outperforms existing "superefficient" estimators for constrained parameters based on different parametrized structures and/or shapes of the parameter membership region X
Sponsorship	IEEE Signal Processing Society
Starting Page	1264
Ending Page	1274
Page Count	11
File Size	775754
File Format	PDF
ISSN	1053587X
Volume Number	55
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Parameter estimation Vectors Linear matrix inequalities Covariance matrix Gaussian noise Maximum likelihood estimation Minimax techniques Gaussian distribution Shape Target tracking target tracking Constrained estimators dominating estimators least-squares methods linear estimation parameter estimation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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