eprintid: 3317
rev_number: 15
eprint_status: archive
userid: 17
dir: disk0/00/00/33/17
datestamp: 2018-12-17 04:09:18
lastmod: 2018-12-17 19:19:24
status_changed: 2018-12-17 19:19:24
type: article
metadata_visibility: show
creators_name: Nguyen, Linh Trung
creators_name: Nguyen, Viet Dung
creators_name: Thameri, Messaoud
creators_name: Truong, Minh Chinh
creators_name: Abed-Meraim, Karim
creators_id: linhtrung@vnu.edu.vn
creators_id: nvdung@vnu.edu.vn
creators_id: m_thameri@hotmail.com
creators_id: tmchinh@gmail.com
creators_id: karim.abed-meraim@univ-orleans.fr
title: Low-complexity adaptive algorithms for robust subspace tracking
ispublished: pub
subjects: ECE
subjects: IT
subjects: isi
divisions: avitech
divisions: fac_fet
note: Early access
abstract: 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.
date: 2018-10-22
date_type: published
publisher: IEEE
official_url: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4200690
id_number: 10.1109/JSTSP.2018.2876626
contact_email: linhtrung@vnu.edu.vn
full_text_status: none
publication: IEEE Journal of Selected Topics in Signal Processing
refereed: TRUE
issn: 1932-4553
funders: NAFOSTED
citation:   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