Title:
STRUCTURED LEAST SQUARES WITH BOUNDED DATA UNCERTAINTIES
Authors:
M. Pilanci, O. Arikan, B. Oguz, M.C. Pinar
Abstract
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.
Index Terms: total least squares, robust solutions, inverse
problems, structured perturbations, bounded data uncertainties
Full paper available on request.