On robustness of linear prediction based blind identification

Deneire, Luc;Slock, Dirk T M
SCVT 1997, IEEE 5th Symposium on Communications and Vehicular Technology, October 14-15, 1997, Twente, Netherlands





L<1) modeling), which is automaticallyP(z)H=h(0), where P(z)is the predictionHis the channel and its first coefficient. AlthoughP(z)of overestimated order, clever use ofHto channel length overestimation. This paper


identified by, e.g., a singular multichannel Levinson


algorithm, and can be shown to be robust to AR order


overestimation. On the other hand, K.A. Meraim and A.


Gorokhov derive other robustness properties based on the








the previous equations leads to robustness of the estimation




investigates these robustness issues, comparing both


methods (and derived methods) to order estimation algorithms


for, e.g., subspace-fitting methods. An important


point developed hereunder is the implicit order estimation


schemes present in linear prediction based methods


and their influence on identification performance. Furthermore,


we develop a new order estimation method, of


low computational cost and giving the channel estimate


as a by-product.




Linear prediction based algorithms have been applied to


the multi-channel FIR identification problem. In [11], it


was shown that oversampled and/or multiple antenna received


signals may be modeled as well as low rank MA


processes as low rank AR processes. Indeed, taking FIR


nature and the singularity of the MA process into account


(due to the fact that the number of channels is bigger than


the number of sources) leads to a finite order prediction


filter (i.e. AR(

Communication systems
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