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Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/73883

Type: Journal article
Title: Computing constrained Cramér-Rao bounds
Other Titles: Computing constrained Cramer-Rao bounds
Author: Tune, Paul Li Shern
Citation: IEEE Transactions on Signal Processing, 2012; 60(10):5543-5548
Publisher: IEEE
Issue Date: 2012
ISSN: 1053-587X
School/Discipline: School of Mathematical Sciences
Statement of
Responsibility: 
Paul Tune
Abstract: We revisit the problem of computing submatrices of the Cramér-Rao bound (CRB), which lower bounds the variance of any unbiased estimator of a vector parameter ɵ. We explore iterative methods that avoid direct inversion of the Fisher information matrix, which can be computationally expensive when the dimension of ɵ is large. The computation of the bound is related to the quadratic matrix program, where there are highly efficient methods for solving it. We present several methods, and show that algorithms in prior work are special instances of existing optimization algorithms. Some of these methods converge to the bound monotonically, but in particular, algorithms converging nonmonotonically are much faster. We then extend the work to encompass the computation of the CRB when the Fisher information matrix is singular and when the parameter ɵ is subject to constraints. As an application, we consider the design of a data streaming algorithm for network measurement.
Keywords: Cramér-Rao bound; Fisher information; matrix functions; optimization; quadratic matrix program
Rights: © 2012 IEEE
RMID: 0020122180
DOI: 10.1109/TSP.2012.2204258
Appears in Collections:Mathematical Sciences publications
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