NEW RECURSIVE LEAST SQUARE ALGORITHMS WITHOUT USING THE INITIAL INFORMATION

In this paper, new types of recursive least square (RLS) algorithms, without using the initial information of a parameter or a state to be estimated, are proposed. The proposed RLS algorithm is first obtained for a generic linear model and is then extended to a state estimator for a stochastic state-space model. Compared with the existing algorithms, the proposed RLS algorithms are simpler and more numerically stable. It is shown, by simulation studies, that the proposed RLS algorithms have better numerical stability for digital computation than existing algorithms.