eprintid: 2484
rev_number: 8
eprint_status: archive
userid: 17
dir: disk0/00/00/24/84
datestamp: 2017-06-09 11:34:48
lastmod: 2017-06-09 11:34:48
status_changed: 2017-06-09 11:34:48
type: article
succeeds: 2195
metadata_visibility: show
creators_name: Nguyen, Viet Dung
creators_name: Abed-Meraim, Karim
creators_name: Nguyen, Linh Trung
creators_name: Weber, Rodolphe
creators_id: nvdung@vnu.edu.vn
creators_id: karim.abed-meraim@univ-orleans.fr
creators_id: linhtrung@vnu.edu.vn
creators_id: weberrod@gmail.com
title: Generalized minimum noise subspace for array processing
ispublished: pub
subjects: ECE
subjects: isi
divisions: fac_fet
abstract: Based on the minimum noise subspace (MNS) method previously introduced in the context of blind channel identification, generalized minimum noise subspace (GMNS) is proposed in this paper for array processing that generalizes MNS with respect to the availability of only a fixed number of parallel computing units. Different batch and adaptive algorithms are then introduced for fast and parallel computation of signal (principal) and noise (minor) subspaces. The computational complexity of GMNS and its related estimation accuracy are investigated by simulated experiments and a real-life experiment in radio astronomy. It is shown that GMNS represents an excellent tradeoff between the computational gain and the subspace estimation accuracy, as compared to several standard subspace methods.
date: 2017-07
date_type: published
publisher: IEEE
official_url: http://ieeexplore.ieee.org/document/7903711/
full_text_status: public
publication: IEEE Transactions on Signal Processing
volume: 65
number: 14
pagerange: 3789-3802
refereed: TRUE
issn: 1053-587X
citation:   Nguyen, Viet Dung and Abed-Meraim, Karim and Nguyen, Linh Trung and Weber, Rodolphe  (2017) Generalized minimum noise subspace for array processing.  IEEE Transactions on Signal Processing, 65  (14).   pp. 3789-3802.  ISSN 1053-587X     
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2484/2/Generalized%20Minimum%20Noise%20Subspace%20For%20Array%20Processing_Dung2017.pdf