Extended fast fixed-order RLS adaptive filters

The existing derivations of conventional fast RLS adaptive filters are intr insically dependent on the shift structure in the input regression vectors. This structure arises when a tapped-delay line (FIR) filter is used as a m odeling filter. In this paper, we show, unlike what original derivations ma y suggest, that fast fixed-order RLS adaptive algorithms are not limited to FIR filter structures. We show that fast recursions in both explicit and a rray forms exist for more general data structures, such as orthonormally ba sed models. One of the benefits of working with orthonormal bases is that f ewer parameters can be used to model long impulse responses.