Open Access

Reduced-Rank Adaptive Filtering Using Krylov Subspace

EURASIP Journal on Advances in Signal Processing20032002:535098

Received: 23 January 2002

Published: 2 January 2003


A unified view of several recently introduced reduced-rank adaptive filters is presented. As all considered methods use Krylov subspace for rank reduction, the approach taken in this work is inspired from Krylov subspace methods for iterative solutions of linear systems. The alternative interpretation so obtained is used to study the properties of each considered technique and to relate one reduced-rank method to another as well as to algorithms used in computational linear algebra. Practical issues are discussed and low-complexity versions are also included in our study. It is believed that the insight developed in this paper can be further used to improve existing reduced-rank methods according to known results in the domain of Krylov subspace methods.


adaptive filters reduced-rank adaptive filters multiuser detection array processing Krylov subspace methods

Authors’ Affiliations

Signal and Image Processing Department of École Nationale Supérieure des Télécommunications


© Burykh and Abed-Meraim 2002