Abstract
In this paper, we present a generalized least mean square (LMS) algorithm. This new filter, which has been termed as state-space least mean square (SSLMS), incorporates linear time-varying state-space model of the underlying environment. The tracking ability of the LMS is limited due to linear regression model assumption. By overcoming this restriction, SSLMS exhibits a marked improvement in tracking performance over standard LMS and its known variants. The derivation of SSLMS is based on the minimum norm solution of an underdetermined linear least squares problem. An example of tracking a linear time-varying system demonstrates the ability and flexibility of SSLMS.
Sign-in/Register to access full text options 
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 callDisclaimer: 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.
