Pilanci, MertArıkan, OrhanOguz, B.Pınar, Mustafa C.2016-02-082016-02-0820091520-6149http://hdl.handle.net/11693/26734Date of Conference: 19-24 April 2009In 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.EnglishBounded data uncertaintiesInverse problemsRobust solutionsStructured perturbationsTotal least squaresBounded data uncertaintiesCoefficient matrixCore problemsIll posed problemLeast SquareLinear system of equationsMultiple sinusoidsNumerical comparisonRobust solutionsSignal processing applicationsSignal restorationStructured perturbationsStructured total least squaresTotal least squaresUncertain modelsAcousticsDifferential equationsFrequency estimationLinear systemsSignal processingInverse problemsConference Paper10.1109/ICASSP.2009.4960320