Abstract

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.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.