Structured least squares with bounded data uncertainties

In many signal processing applications the core problem reduces to a linear system of equations. Coefficient matrix uncertainties create a significant challenge in obtaining reliable solutions. In this paper, we present a novel formulation for solving a system of noise contaminated linear equations...

Full description

Saved in:
Bibliographic Details
Main Authors: Pilanci, M., Arikan, O., Oguz, B., Pinar, M.C.
Format: Conference Proceeding
Language:English
Subjects:
bounded data uncertainties
Equations
Frequency estimation
Industrial electronics
inverse problems
Least squares methods
Maximum likelihood estimation
Noise robustness
Pollution measurement
robust solutions
Signal processing
structured perturbations
total least squares
Uncertainty
Vectors
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In many signal processing applications the core problem reduces to a linear system of equations. Coefficient matrix uncertainties create a significant challenge in obtaining reliable solutions. In this paper, we present a novel formulation for solving a system of noise contaminated linear equations while preserving the structure of the coefficient matrix. The proposed method has advantages over the known Structured Total Least Squares (STLS) techniques in utilizing additional information about the uncertainties and robustness in ill-posed problems. Numerical comparisons are given to illustrate these advantages in two applications: signal restoration problem with an uncertain model and frequency estimation of multiple sinusoids embedded in white noise.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2009.4960320