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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages4504-4508
Number of pages5
ISBN (Print)9781538646588
DOIs
StatePublished - Sep 21 2018

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