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A new family of approximate QR-LS algorithms for adaptive filtering

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2 Author(s)
Y. Zhou ; Dept. of Electr. & Electron. Eng., Hong Kong Univ. ; S. C. Chan

This paper proposes a new family of approximate QR-based least squares (LS) adaptive filtering algorithms called p-TA-QR-LS algorithms. It extends the TA-QR-LS algorithm by retaining different number of diagonal plus off-diagonals (denoted by an integer p) of the triangular factor of the augmented data matrix. For p=1 and N it reduces respectively to the TA-QR-LS and the QR-RLS algorithms. It not only provides a link between the QR-LMS-type and the QR-RLS algorithms through a well-structured family of algorithms, but also offers flexible complexity-performance tradeoffs in practical implementation. These results are verified by computer simulation and the mean convergence of the algorithms is also analyzed

Published in:

IEEE/SP 13th Workshop on Statistical Signal Processing, 2005

Date of Conference:

17-20 July 2005