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Generalized minimum noise subspace for array processing

Viet Dung Nguyen and Karim Abed-Meraim and Linh Trung Nguyen and Rodolphe Weber (2016) Generalized minimum noise subspace for array processing. IEEE Transactions on Signal Processing . ISSN 1053-587X (Submitted)

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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 trade-off between the computational gain and the subspace estimation accuracy, as compared to several standard subspace methods.

Item Type:Article
Additional Information:revised version
Subjects:Electronics and Communications
ISI/Scopus-indexed journals
Divisions:Faculty of Electronics and Telecommunications (FET)
ID Code:2195
Deposited By: A/Prof. Linh Trung Nguyen
Deposited On:08 Dec 2016 16:56
Last Modified:08 Dec 2016 16:57

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