Interval methods for sinusoidal parameter estimation: A comparative analysis
type: Item
instance of: scholarly article
title: Interval methods for sinusoidal parameter estimation: A comparative analysis
zbMATH Open document ID: 0966.65014
DOI: 10.1023/A:1009986615302
author: William W. Edmonson
author: Wen H. Lee
author: John M. M. Anderson
published in: Reliable Computing
publication date: 2001-08-01T00:00:00Z
review text: The paper compares three algorithms to estimate sinusoidal parameters in various applications such as signal modeling and estimation of directions of arrival. The estimation problem arises from a signal that consists of sinuoids plus an additive noise. Three algorithms are presented: EM interval method (EMIM), grouped coordinate descent based on interval methods with and without least squares. The estimation problems are decomposed via the EM algorithm and then the interval methods are applied for global optimization. EMIM has the best performance when drawing the main attention on estimation accuracy, although it has the drawback of computational complexity and slow convergence.
reviewed by: Hans-Peter Altenburg
Mathematics Subject Classification ID: 65C60
Mathematics Subject Classification ID: 65G40
Mathematics Subject Classification ID: 62-07
Mathematics Subject Classification ID: 62F10
zbMATH DE Number: 1513374
zbMATH Keywords: sinusoidal parameter estimation
zbMATH Keywords: EM algorithm
zbMATH Keywords: interval arithmetic methods
zbMATH Keywords: coordinate descent
zbMATH Keywords: EM interval method
zbMATH Keywords: least squares
zbMATH Keywords: global optimization
zbMATH Keywords: computational complexity
zbMATH Keywords: convergence
MaRDI profile type: Publication
full work available at URL: a:1009986615302
OpenAlex ID: W80852114
description: scientific article; zbMATH DE number 1513374