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Numerically-robust O(N2) RLS algorithms using least-squares prewhitening

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1 Author(s)
Douglas, S.C. ; Dept. of Electr. Eng., Southern Methodist Univ., Dallas, TX, USA

We derive two new O(N2) algorithms for arbitrary recursive least-squares (RLS) estimation tasks. The algorithms employ a novel update for an inverse square-root factor of the exponentially-windowed input signal autocorrelation matrix that is the least-squares equivalent of a natural gradient prewhitening algorithm. Both of the new RLS algorithms require 4N2+O(N) multiply/adds, two divides, and one square root per iteration to implement. We can prove that our new algorithms are numerically-robust, and simulations are used to indicate this fact in fixed-point arithmetic. An algorithm that computes the square-root factorization of the input signal autocorrelation matrix is also described

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Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on  (Volume:1 )

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