Fast algorithms for identification of periodically varying systems

The problem of identification/tracking of periodically varying systems is considered. When system coefficients vary rapidly with time, the most frequently used weighted least squares (WLS) and least mean squares (LMS) algorithms are not capable of tracking the changes satisfactorily. To obtain good estimation results, one has to use more specialized adaptive filters, such as the basis function (BF) algorithms, which are based on explicit models of parameter changes. Unfortunately, estimators of this kind are numerically very demanding. The paper describes new recursive algorithms that combine low computational requirements, which are typical of WLS and LMS filters, with very good tracking capabilities, which are typical of BF filters.