Structured least squares with bounded data uncertainties
Please cite this item using this persistent URLhttp://hdl.handle.net/11693/26734
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
- Conference Paper 
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. ©2009 IEEE.
Showing items related by title, author, creator and subject.
Pilanci, M.; Arikan, O.; Pinar, M.C. (2010)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 ...
Gholami, M.R.; Gezici, S.; Strom, E.G. (2013)We deal with the positioning problem based on two-way time-of-arrival (TW-TOA) measurements in asynchronous wireless sensor networks. The optimal estimator for this problem poses a difficult global optimization problem. ...
Guvenc I.; Gezici, S.; Sahinoglu, Z. (2012)In this paper, theoretical lower bounds on performance of linear least-squares (LLS) position estimators are obtained, and performance differences between LLS and nonlinear least-squares (NLS) position estimators are ...