Structured least squares with bounded data uncertainties
2009 IEEE International Conference on Acoustics, Speech and Signal Processing
3261 - 3264
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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.
KeywordsBounded data uncertainties
Total least squares
Bounded data uncertainties
Ill posed problem
Linear system of equations
Signal processing applications
Structured total least squares
Total least squares
Published Version (Please cite this version)http://dx.doi.org/10.1109/ICASSP.2009.4960320
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