A fast least-squares algorithm for linearly constrained adaptive filtering

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....

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Bibliographic Details
Published in:IEEE transactions on signal processing 1996-05, Vol.44 (5), p.1168-1174
Main Authors: Resende, L.S., Romano, J.M.T., Bellanger, M.G.
Format: Article
Language:English
Subjects:
Adaptive arrays
Adaptive filters
Applied sciences
Array signal processing
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Filtering algorithms
Information, signal and communications theory
Robustness
Roundoff errors
Signal analysis
Signal and communications theory
Signal processing
Signal, noise
Telecommunications and information theory
Transversal filters
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Summary: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.
ISSN:1053-587X
1941-0476
DOI:10.1109/78.502329