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

In this paper, we propose a new regularized robust estimation approach based on the robust null \taunull -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...