Structured least squares problems and robust estimators
Author
Pilanci, M.
Arıkan, Orhan
Pinar, M. C.
Date
2010-10-22Source Title
IEEE Transactions on Signal Processing
Print ISSN
1053-587X
Publisher
IEEE
Volume
58
Issue
5
Pages
2453 - 2465
Language
English
Type
ArticleItem Usage Stats
134
views
views
140
downloads
downloads
Abstract
A novel approach is proposed to provide robust and accurate estimates for linear regression problems when both the measurement vector and the coefficient matrix are structured and subject to errors or uncertainty. A new analytic formulation is developed in terms of the gradient flow of the residual norm to analyze and provide estimates to the regression. The presented analysis enables us to establish theoretical performance guarantees to compare with existing methods and also offers a criterion to choose the regularization parameter autonomously. Theoretical results and simulations in applications such as blind identification, multiple frequency estimation and deconvolution show that the proposed technique outperforms alternative methods in mean-squared error for a significant range of signal-to-noise ratio values.
Keywords
Blind identificationDeconvolution
Errors-in-variables
Frequency estimation
Least squares
Robust least squares
Structured total least squares
Blind identification
Blind identifications
Errors in variables
Least Square
Robust least squares
Structured total least squares
Blind equalization
Communication channels (information theory)
Convolution
Estimation
Measurement errors
Signal to noise ratio
Uncertainty analysis
Frequency estimation
Permalink
http://hdl.handle.net/11693/22338Published Version (Please cite this version)
http://dx.doi.org/10.1109/TSP.2010.2041279Collections
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