In this paper two fast RLS adaptive filtering algorithms are described. Both algorithms compute the lattice coefficients and are based on the development of square-root factorizations of the autocorrelation matrix. Due to the square-root nature of the algorithms, the recursion is numerically stable. Experimental evaluations have been performed in limited precision environment, and comparison with the stabilized fast transversal filter algorithm (Slock and Kailath, 1991) has been made. Since the described algorithms require O(N) operations per sample, where N is the filter order, from a computational complexity point of view they represent a substantial advantage over the O(N2) complexity of classical square-root algorithms.
Fast square-root RLS adaptive filtering algorithms
CARINI, ALBERTO;
1997
Abstract
In this paper two fast RLS adaptive filtering algorithms are described. Both algorithms compute the lattice coefficients and are based on the development of square-root factorizations of the autocorrelation matrix. Due to the square-root nature of the algorithms, the recursion is numerically stable. Experimental evaluations have been performed in limited precision environment, and comparison with the stabilized fast transversal filter algorithm (Slock and Kailath, 1991) has been made. Since the described algorithms require O(N) operations per sample, where N is the filter order, from a computational complexity point of view they represent a substantial advantage over the O(N2) complexity of classical square-root algorithms.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
