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Blind Beamforming for Non Gaussian Signals (1993)
| Venue: | IEE Proceedings-F |
| Citations: | 719 - 31 self |
BibTeX
@ARTICLE{Cardoso93blindbeamforming,
author = {Jean-François Cardoso and Antoine Souloumiac},
title = {Blind Beamforming for Non Gaussian Signals},
journal = {IEE Proceedings-F},
year = {1993},
volume = {140},
pages = {362--370}
}
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Abstract
This paper considers an application of blind identification to beamforming. The key point is to use estimates of directional vectors rather than resorting to their hypothesized value. By using estimates of the directional vectors obtained via blind identification i.e. without knowing the arrray manifold, beamforming is made robust with respect to array deformations, distortion of the wave front, pointing errors, etc ... so that neither array calibration nor physical modeling are necessary. Rather surprisingly, `blind beamformers' may outperform `informed beamformers' in a plausible range of parameters, even when the array is perfectly known to the informed beamformer. The key assumption blind identification relies on is the statistical independence of the sources, which we exploit using fourth-order cumulants. A computationally efficient technique is presented for the blind estimation of directional vectors, based on joint diagonalization of 4th-order cumulant matrices
Keyphrases
non gaussian signal directional vector statistical independence physical modeling blind identification identification relies key assumption array calibration wave front informed beamformers informed beamformer 4th-order cumulant matrix blind estimation key point fourth-order cumulants efficient technique arrray manifold joint diagonalization plausible range
