Generalized Minimum Noise Subspace For Array Processing

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 trade-off between the computational gain and the subspace estimation accuracy, as compared to several standard subspace methods
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IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2017, 64 (14), pp.3789 - 3802. 〈10.1109/TSP.2017.2695457〉
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Soumis le : lundi 25 juillet 2016 - 23:31:28
Dernière modification le : mardi 20 février 2018 - 01:15:29

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Viet-Dung Nguyen, Karim Abed-Meraim, Nguyen Linh-Trung, Rodolphe Weber. Generalized Minimum Noise Subspace For Array Processing. IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2017, 64 (14), pp.3789 - 3802. 〈10.1109/TSP.2017.2695457〉. 〈hal-01295030v2〉

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