TY  - JOUR
ID  - SisLab2484
UR  - http://ieeexplore.ieee.org/document/7903711/
IS  - 14
A1  - Nguyen, Viet Dung
A1  - Abed-Meraim, Karim
A1  - Nguyen, Linh Trung
A1  - Weber, Rodolphe
Y1  - 2017/07//
N2  - 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.
PB  - IEEE
JF  - IEEE Transactions on Signal Processing
VL  - 65
SN  - 1053-587X
TI  - Generalized minimum noise subspace for array processing
SP  - 3789
AV  - public
EP  - 3802
ER  -