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A fast least-squares algorithm for linearly constrained adaptive filtering

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3 Author(s)
Resende, L.S. ; Univ. Estadual de Campinas, Sao Paulo, Brazil ; Romano, J.M.T. ; Bellanger, M.G.

An extension of the field of fast least-squares techniques is presented. It is shown that the adaptation gain, which is updated with a number of operations proportional to the number of transversal filter coefficients, can be used to update the coefficients of a linearly constrained adaptive filter. An algorithm that is robust to round-off errors is derived. It is general and flexible. It can handle multiple constraints and multichannel signals. Its performance is illustrated by simulations and compared with the classical LMS-based Frost (1972) algorithm

Published in:

Signal Processing, IEEE Transactions on  (Volume:44 ,  Issue: 5 )

Date of Publication:

May 1996

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