VNU-UET Repository: No conditions. Results ordered -Date Deposited. 2024-03-29T13:26:59ZEPrintshttp://eprints.uet.vnu.edu.vn/images/sitelogo.pnghttps://eprints.uet.vnu.edu.vn/eprints/2018-12-18T02:56:40Z2018-12-18T02:56:40Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3335This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/33352018-12-18T02:56:40ZGeneralized MNS Method for Parallel Minor and Principal Subspace AnalysisThis paper introduces a generalized minimum noise subspace method for the fast estimation of the minor or principal subspaces for large dimensional multi-sensor systems. In particular, the proposed method allows parallel computation of the desired subspace when K > 1 computational units (DSPs) are available in a parallel architecture. The overall numerical cost is approximately reduced by a factor of K2 while preserving the estimation accuracy close to optimality. Different algorithm implementations are considered and their performance is assessed through numerical simulation.Linh Trung Nguyenlinhtrung@vnu.edu.vnViet Dung Nguyennvdung@vnu.edu.vnKarim Abed-Meraimkarim.abed-meraim@univ-orleans.frRodolphe Weberrodolphe.weber@univ-orleans.fr2017-06-09T11:34:48Z2017-06-09T11:34:48Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2484This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/24842017-06-09T11:34:48ZGeneralized minimum noise subspace for array processingBased 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.Viet Dung Nguyennvdung@vnu.edu.vnKarim Abed-Meraimkarim.abed-meraim@univ-orleans.frLinh Trung Nguyenlinhtrung@vnu.edu.vnRodolphe Weberweberrod@gmail.com