A state-space approach to adaptive RLS 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...

State-space RLS

Kalman filter is linear optimal estimator for random signals. We develop state-space RLS that is counterpart of Kalman filter for deterministic signals i.e. there is no process noise but only observation noise. State-space RLS inherits its optimality properties from the standard least squares. It gives excellent tracking performance as compared to existing forms of RLS. A large class of signals...

Exact RLS Laguerre-lattice adaptive filtering

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.