A fast least-squares algorithm for linearly constrained adaptivefiltering

  • Authors:
  • L.S. Resende;J.M.T. Romano;M.G. Bellanger

  • Affiliations:
  • Univ. Estadual de Campinas, Sao Paulo;-;-

  • Venue:
  • IEEE Transactions on Signal Processing
  • Year:
  • 1996

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Abstract

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