relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3317/
title: Low-complexity adaptive algorithms for robust subspace tracking
creator: Nguyen, Linh Trung
creator: Nguyen, Viet Dung
creator: Thameri, Messaoud
creator: Truong, Minh Chinh
creator: Abed-Meraim, Karim
subject: Electronics and Communications
subject: Information Technology (IT)
subject: ISI-indexed journals
description: This paper introduces new, low-complexity, adaptive  algorithms for robust subspace tracking in certain adverse scenarios of noisy data. First, an adequate weighted least-squares criterion is considered for the design of a robust subspace tracker that is most efficient in the burst noise case. Second, by using data pre-processing and robust statistics estimate, we introduce a second method that is shown to be the most efficient for subspace tracking in the case of impulsive noise (e.g. α-stable noise). Finally, a ‘detect-and-skip’ approach is adopted where the corrupted measurements are detected and treated as ‘missing’ data. The resulting algorithm is particularly effective in the case where the data is affected by sparse ‘outliers’. All these approaches were analyzed and their convergence properties  were investigated. Moreover, the proposed subspace tracking algorithms were compared by simulated experiments to some state-of-the-art methods, in different noise/outliers contexts.
publisher: IEEE
date: 2018-10-22
type: Article
type: PeerReviewed
identifier:   Nguyen, Linh Trung and Nguyen, Viet Dung and Thameri, Messaoud and Truong, Minh Chinh and Abed-Meraim, Karim  (2018) Low-complexity adaptive algorithms for robust subspace tracking.  IEEE Journal of Selected Topics in Signal Processing .    ISSN 1932-4553     
relation: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4200690
relation: 10.1109/JSTSP.2018.2876626