Interval Methods for Sinusoidal Parameter Estimation: A Comparative Analysis

Interval Methods for Sinusoidal Parameter Estimation: A Comparative Analysis In this paper, we address the problem of determining maximum-likelihood estimates of sinusoid parameters from a signal that consists of sinusoids and additive noise. We present three algorithms that integrate interval methods for global optimization with procedures that decompose the problem into smaller ones. Interval methods represent a global optimization technique that is based upon the branch and bound principle. More specifically, we decompose the problems via the expectation-maximization algorithm and variations of the coordinate descent algorithm. Although, we have not proven that the proposed algorithms converge to the global optimum, their performance in our simulation example was much superior to that of the popular iterative quadratic maximum likelihood (IQML) method. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Reliable Computing Springer Journals

Interval Methods for Sinusoidal Parameter Estimation: A Comparative Analysis

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Publisher
Kluwer Academic Publishers
Copyright
Copyright © 2000 by Kluwer Academic Publishers
Subject
Mathematics; Numeric Computing; Approximations and Expansions; Computational Mathematics and Numerical Analysis; Mathematical Modeling and Industrial Mathematics
ISSN
1385-3139
eISSN
1573-1340
D.O.I.
10.1023/A:1009986615302
Publisher site
See Article on Publisher Site

Abstract

In this paper, we address the problem of determining maximum-likelihood estimates of sinusoid parameters from a signal that consists of sinusoids and additive noise. We present three algorithms that integrate interval methods for global optimization with procedures that decompose the problem into smaller ones. Interval methods represent a global optimization technique that is based upon the branch and bound principle. More specifically, we decompose the problems via the expectation-maximization algorithm and variations of the coordinate descent algorithm. Although, we have not proven that the proposed algorithms converge to the global optimum, their performance in our simulation example was much superior to that of the popular iterative quadratic maximum likelihood (IQML) method.

Journal

Reliable ComputingSpringer Journals

Published: Oct 16, 2004

References

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