Asymptotically Optimal Smoothing of Averaged LMS Estimates for Regression Parameter Tracking
2002 (English)In: Automatica, ISSN 0005-1098, Vol. 38, no 8, 1287-1293 p.Article in journal (Refereed) Published
The sequence of estimates formed by the LMS algorithm for a standard linear regression estimation problem is considered. It is known since earlier that smoothing these estimates by simple averaging will lead to, asymptotically, the recursive least-squares algorithm. In this paper, it is first shown that smoothing the LMS estimates using a matrix updating will lead to smoothed estimates with optimal tracking properties, also in case the true parameters are slowly changing as a random walk. The choice of smoothing matrix should be tailored to the properties of the random walk. Second, it is shown that the same accuracy can be obtained also for a modified algorithm, SLAMS, which is based on averages and requires much less computations.
Place, publisher, year, edition, pages
Elsevier, 2002. Vol. 38, no 8, 1287-1293 p.
Asymptotic MSE, Linear regression, LMS, Parameter tracking, Slow random walk, Smoothing
IdentifiersURN: urn:nbn:se:liu:diva-46936DOI: 10.1016/S0005-1098(02)00028-6OAI: oai:DiVA.org:liu-46936DiVA: diva2:267832
© 2002 Elsevier Science Ltd. All rights reserved.2009-10-112009-10-112013-07-17