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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Veröffentlicht in:	IEEE transactions on signal processing 1996-05, Vol.44 (5), p.1168-1174
Hauptverfasser:	Resende, L.S., Romano, J.M.T., Bellanger, M.G.
Format:	Artikel
Sprache:	eng
Schlagworte:	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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Beschreibung
Zusammenfassung:	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