TY - GEN
T1 - Robust Estimation in Linear ILL-Posed Problems with Adaptive Regularization Scheme
AU - Suliman, Mohamed Abdalla Elhag
AU - Sifaou, Houssem
AU - Ballal, Tarig
AU - Alouini, Mohamed-Slim
AU - Al-Naffouri, Tareq Y.
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2018/9/21
Y1 - 2018/9/21
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/630828
UR - https://ieeexplore.ieee.org/document/8462651/
UR - http://www.scopus.com/inward/record.url?scp=85054255398&partnerID=8YFLogxK
U2 - 10.1109/icassp.2018.8462651
DO - 10.1109/icassp.2018.8462651
M3 - Conference contribution
AN - SCOPUS:85054255398
SN - 9781538646588
SP - 4504
EP - 4508
BT - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -