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Author Pei-Jung Chung
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2008
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations ♦ Applied physics
Subject Keyword Covariance matrix ♦ Maximum likelihood estimation ♦ Estimation ♦ Robustness ♦ Upper bound ♦ Arrays ♦ Computational modeling
Abstract We study the performance of a recently proposed robust ML estimation procedure for unknown numbers of signals. This approach finds the ML estimate for the maximum number of signals and selects relevant components associated with the true parameters from the estimated parameter vector. Its computational cost is significantly lower than conventional methods based on information theoretic criteria or multiple hypothesis tests. We show that the covariance matrix of relevant estimates is upper and lower bounded by two covariance matrices. These bounds are easy to compute by existing results for standard ML estimation. Our analysis is further confirmed by numerical experiments over a wide range of SNRs.
Description Author affiliation: Sch. of Eng. & Electron., Univ. of Edinburgh, Edinburgh (Pei-Jung Chung)
ISBN 9781424422401
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2008-07-21
Publisher Place Germany
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 1.08 MB
Page Count 5
Starting Page 86
Ending Page 90

Source: IEEE Xplore Digital Library