Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Robust Solutions to Least-Squares Problems with Uncertain Data
SIAM Journal on Matrix Analysis and Applications
An Efficient Algorithm for a Bounded Errors-in-Variables Model
SIAM Journal on Matrix Analysis and Applications
Statistical Digital Signal Processing and Modeling
Statistical Digital Signal Processing and Modeling
The Degenerate Bounded Errors-in-Variables Model
SIAM Journal on Matrix Analysis and Applications
Blind Deconvolution Using a Regularized Structured Total Least Norm Algorithm
SIAM Journal on Matrix Analysis and Applications
Overview of total least-squares methods
Signal Processing
Mean-Squared Error Estimation for Linear Systems with Block Circulant Uncertainty
SIAM Journal on Matrix Analysis and Applications
SIAM Journal on Matrix Analysis and Applications
Rethinking Biased Estimation: Improving Maximum Likelihood and the Cramér–Rao Bound
Foundations and Trends in Signal Processing
Structured least squares with bounded data uncertainties
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Parameter estimation with multiple sources and levels ofuncertainties
IEEE Transactions on Signal Processing
Robust mean-squared error estimation in the presence of model uncertainties
IEEE Transactions on Signal Processing
The extended least squares criterion: minimization algorithms andapplications
IEEE Transactions on Signal Processing
Parameter estimation problems with singular information matrices
IEEE Transactions on Signal Processing
Linear Regression With Gaussian Model Uncertainty: Algorithms and Bounds
IEEE Transactions on Signal Processing
Formulation and solution of structured total least norm problemsfor parameter estimation
IEEE Transactions on Signal Processing
Automatica (Journal of IFAC)
A Unified Approach to Superresolution and Multichannel Blind Deconvolution
IEEE Transactions on Image Processing
Regularized constrained total least squares image restoration
IEEE Transactions on Image Processing
Robust estimation in flat fading channels under bounded channel uncertainties
Digital Signal Processing
| Hi-index | 35.68 |
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 analytic formulation is developed in terms of the gradient flow of the residual norm to analyze and provide estimates to the regression. The presented analysis enables us to establish theoretical performance guarantees to compare with existing methods and also offers a criterion to choose the regularization parameter autonomously. Theoretical results and simulations in applications such as blind identification, multiple frequency estimation and deconvolution show that the proposed technique outperforms alternative methods in mean-squared error for a significant range of signal-to-noise ratio values.