Extended fast fixed order RLS adaptive filters

The existing derivations of conventional fast RLS adaptive filters are intrinsically dependent on the shift structure in the input regression vectors. This structure arises when a tapped-delay line (FIR) filter is used as a modeling filter. In this paper, we show, unlike what original derivations may suggest, that fast fixed-order RLS adaptive algorithms are not limited to FIR filter structures. We show that fast recursions in both explicit and array forms exist for more general data structures, such as orthonormallybased models. One of the benefits of working with orthonormal bases is that fewer parameters can be used to model long impulse responses. Index Terms – RLS algorithm, Laguerre network, orthonormal model, fixed-order filter, regularized least-squares.