On the Tracking Performance of Adaptive Filters and Their Combinations

Author(s)

Claser, Raffaello ^[1] ; Nascimento, Vitor H. ^[1]

Affiliation

^[1] Univ Sao Paulo, Dept Elect Syst Engn, BR-05508900 Sao Paulo - Brazil

• Document type: Journal article
• Source: IEEE TRANSACTIONS ON SIGNAL PROCESSING; v. 69, p. 3104-3116, 2021.
• Web of Science Citations: 0

Abstract

Combinations of adaptive filters have attracted attention as a simple solution to improve filter performance, including tracking properties. In this paper, we consider combinations of LMS and RLS filters, and study their performance for tracking time-varying solutions. Modeling the variation of the parameter vector to be estimated as a first order autoregressive (AR) model, we show that a convex combination between one LMS and one RLS filters with their optimum settings may have a tracking performance close to the optimal excess mean-square error (EMSE) and mean-square deviation (MSD) obtained via Kalman filter, but with lower computational complexity (linear in the filter length instead of quadratic - in the case of diagonal matrices in the Kalman model - or cubic, for general Kalman models). (AU)

• FAPESP's process: 18/12579-7 - ELIOT: enabling technologies for IoT
• Grantee: Vitor Heloiz Nascimento
• Support Opportunities: Research Projects - Thematic Grants