Abstract
General order-recursive least-squares estimation employing a sliding window is described. It is shown that time and order updates of any order-recursive sliding window least-squares algorithm can be obtained solely by 3*3 hyperbolic Householder transformations. Applying this general observation to the sliding window least-squares estimation of time-series signals results in a new algorithm: the hyperbolic Householder lattice (HHL) algorithm. This work broadens the range of applications of QR-decomposition (QRD)-based adaptive least-squares algorithms by allowing a sliding window formulation in addition to the known exponentially windowed form.