On Exponentially Weighted Recursive Least Squares for Estimating Time-Varying Parameters
نویسنده
چکیده
The exponentially weighted recursive least-squares (RLS) has a long history as an algorithm to track timevarying parameters in signal processing and time series analysis. By reviewing the optimality conditions of RLS under a regression framework, possible sources of suboptimality of RLS for tracking time-varying parameters, especially when the parameters satisfy a state-space model, are identified. A straightforward relationship between the RLS variables and the Kalman filtering variables is established under the statespace model assumption. This relationship enables a unified development of several simple algorithms that generalize and extend the traditional RLS. Numerical examples are given to demonstrate the improved tracking performance of theses algorithms.
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