Structured least squares with bounded data uncertainties

  • Authors:

  • M. Pilanci;O. Arikan;B. Oguz;M. C. Pinar

  • Affiliations:

  • Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey;Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey;Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA;Department of Industrial Engineering, Bilkent University, Ankara, Turkey

  • Venue:
  • ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
  • Year:
  • 2009

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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.