DocumentCode :
1059992
Title :
The Constrained CramÉr–Rao Bound From the Perspective of Fitting a Model
Author :
Moore, Terrence J. ; Kozick, Richard J. ; Sadler, Brian M.
Author_Institution :
Army Res. Lab., Adelphi
Volume :
14
Issue :
8
fYear :
2007
Firstpage :
564
Lastpage :
567
Abstract :
Stoica and Ng (1998) presented a simple expression for the constrained Cramer-Rao bound (CCRB) when the constraints are given by a differentiable function of the parameter to be estimated. This letter considers the parallel case in developing the CCRB when the parameters are locally fitted to a lower-dimensional parametric model, i.e., the parameters are locally assumed to be functions of a distinct reduced parameter vector. We employ classical elements of CRB theory on the locally fitted model to present a very simple derivation of the CCRB, conditions for attaining the bound, and a regularity condition. Examples illustrate the key ideas.
Keywords :
parameter estimation; statistical distributions; constrained Cramer-Rao bound; differentiable function; distinct reduced parameter vector; lower-dimensional parametric model; model fitting; parallel case; Covariance matrix; Laboratories; Lagrangian functions; Maximum likelihood estimation; Null space; Parameter estimation; Parametric statistics; Taylor series; Testing; Constrained CramÉr–Rao bound (CCRB); equality constraints; model fitting;
fLanguage :
English
Journal_Title :
Signal Processing Letters, IEEE
Publisher :
ieee
ISSN :
1070-9908
Type :
jour
DOI :
10.1109/LSP.2006.891316
Filename :
4276740
Link To Document :
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