A new method to stabilize fast RLS algorithms based on a first-order of the propagation of numerical errors

An effective method to stabilize fast RLS algorithms is proposed. It is based on the analysis of the propagation of the numerical errors according to a first-order linear model. Two variables are shown to be responsible for the numerical instability. The proposed method modifies the numerical properties of these variables, while preserving the theoretical form of the algorithms. This method is applied to the FTF (fast transversal filter) and fast Kalman algorithms. Experimental results in floating- and fixed-point arithmetic show the efficiency of the method