A stochastic subspace algorithm for blind channel identification in noise fields with unknown spatial color

A stochastic subspace algorithm for blind channel identification in noise fields with unknown spatial color

The blind channel identification problem is formulated in a stochastic state space framework. Starting from a state space model we present a preprocessing step based on two orthogonal subspace projections. Using these orthogonal projections, we derive an algorithm for blind channel estimation which...

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Bibliographic Details
Published in:1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258) Vol. 5; pp. 2619 - 2622 vol.5
Main Authors: Vandaele, P., Moonen, M.
Format: Conference Proceeding
Language:English
Published: IEEE 1999
Subjects:
Additive noise
Blind equalizers
Colored noise
Data models
Equations
Information processing
Laboratories
Signal processing algorithms
State-space methods
Stochastic resonance
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Summary:The blind channel identification problem is formulated in a stochastic state space framework. Starting from a state space model we present a preprocessing step based on two orthogonal subspace projections. Using these orthogonal projections, we derive an algorithm for blind channel estimation which is insensitive to the spatial color of the noise. The performance of this new algorithm is demonstrated through simulation examples.
ISBN:0780350413
9780780350410
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.1999.761234