Overview of total least-squares methods
Signal Processing
The matrix-restricted total least-squares problem
Signal Processing
Localization refinement for wireless sensor networks
Computer Communications
High-performance numerical algorithms and software for structured total least squares
Journal of Computational and Applied Mathematics
Fast deconvolution with approximated PSF by RSTLS with antireflective boundary conditions
Journal of Computational and Applied Mathematics
Short Communication: A rectilinear Gaussian model for estimating straight-line parameters
Journal of Visual Communication and Image Representation
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The constrained total least squares (CTLS) method is a natural extension of TLS to the case when the noise components of the coefficients are algebraically related. The CTLS technique is developed, and some of its applications to superresolution harmonic analysis are presented. The CTLS problem is reduced to an unconstrained minimization problem over a small set of variables. A perturbation analysis of the CTLS solution is derived, and from it the root mean-square error (RMSE) of the CTLS solution, which is valid for small noise levels, is obtained in closed form. The complex version of the Newton method is derived and applied to determine the CTLS solution. It is also shown that the CTLS problem is equivalent to a constrained parameter maximum-likelihood problem. The CTLS technique is applied to estimate the frequencies of sinusoids in white noise and the angle of arrival of narrowband wavefronts at a linear uniform array. In both cases the CTLS method shows superior or similar accuracy to other advanced techniques