Abstract
In this letter, we propose a new type of recursive least squares (RLS) algorithms without using the initial information of a parameter or a state to be estimated. 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 through simulation that the proposed RLS algorithms have better numerical stability for digital computations than existing algorithms.
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