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 language||English (US)|
|Title of host publication||2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||5|
|State||Published - Sep 21 2018|