Structured Matrices and Fast RLS Adaptive Filtering *

QRD - RLS Adaptive Filtering

The main limitation of FQRD-RLS algorithms is that they lack an explicit weight vector term. Furthermore, they do not directly provide the variables allowing for a straightforward computation of the weight vector as is the case with the conventional QRD-RLS algorithm, where a back-substitution procedure can be used to compute the coefficients. Therefore, the applications of the FQRD-RLS algorit...

Exact RLS Laguerre-lattice adaptive filtering

Fast subsampled-updating stabilized fast transversal filter (FSU SFTF) RLS algorithm for adaptive filtering

We present a new, doubly fast algorithm for Recursive Least-Squares (RLS) adaptive ltering that uses displacement structure and subsampled-updating. The Fast Subsampled-Updating Stabilized Fast Transversal Filter (FSU SFTF) algorithm is mathematically equivalent to the classical Fast Transversal Filter (FTF) algorithm. The FTF algorithm exploits the shift invariance that is present in the RLS a...

Spectral line RLS adaptive filtering algorithm

A family of adaptive filtering algorithms for processing signals which have energy concentrated in a relatively small number of component subspaces in the spectral domain is introduced. The approach is based on transform domain signal decomposition and linear least squares filtering of the selected subset of transform domain signal components. The derivation is based on the linear least squares...

Order-recursive RLS Laguerre adaptive filtering

This paper solves the problem of designing recursive-least-squares (RLS) lattice (or order-recursive) algorithms for adaptive filters that do not involve tapped-delay-line structures. In particular, an RLS–Laguerre lattice filter is obtained.