Robust Estimation in Linear ILL-Posed Problems with Adaptive Regularization Scheme

Mohamed A. Suliman; Houssem Sifaou; Tarig Ballal; Mohamed-Slim Alouini; Tareq Y. Al-Naffouri

ICASSP 2018accepted0 citations

Robust Estimation in Linear ILL-Posed Problems with Adaptive Regularization Scheme

Mohamed A. Suliman, Houssem Sifaou, Tarig Ballal, Mohamed-Slim Alouini, Tareq Y. Al-Naffouri

Abstract

In this paper, we propose a new regularized robust estimation approach based on the robust τ -estimator applied to linear ill-posed problems in the presence of noise outliers. Additionally, we introduce a new approach to obtain the optimal regularization parameter for the proposed robust estimator by using tools from random matrix theory. Simulation results demonstrate that the proposed approach with its automated regularization parameter selection outperforms a set of benchmark methods.

BibTeX

@inproceedings{icassp2018_robustestimation,
  title = {Robust Estimation in Linear ILL-Posed Problems with Adaptive Regularization Scheme},
  author = {Mohamed A. Suliman and Houssem Sifaou and Tarig Ballal and Mohamed-Slim Alouini and Tareq Y. Al-Naffouri},
  booktitle = {ICASSP 2018},
  year = {2018}
}