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Article Dans Une Revue IEEE Transactions on Signal Processing Année : 2017

Generalized Minimum Noise Subspace For Array Processing

Résumé

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.
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Dates et versions

hal-01295030 , version 1 (30-03-2016)
hal-01295030 , version 2 (25-07-2016)
hal-01295030 , version 3 (08-06-2017)

Identifiants

Citer

Viet-Dung Nguyen, Karim Abed-Meraim, Nguyen Linh-Trung, Rodolphe Weber. Generalized Minimum Noise Subspace For Array Processing. IEEE Transactions on Signal Processing, 2017, 65 (14), pp.3789 - 3802. ⟨10.1109/TSP.2017.2695457⟩. ⟨hal-01295030v3⟩
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