On the Tracking Performance of Adaptive Filters and Their Combinations

Autor(es)

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

Afiliação do(s) autor(es)

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

• Tipo de documento: Artigo Científico
• Fonte: IEEE TRANSACTIONS ON SIGNAL PROCESSING; v. 69, p. 3104-3116, 2021.
• Citações Web of Science: 0

Resumo

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)

• Processo FAPESP: 18/12579-7 - Tecnologias habilitadores para a Internet das Coisas
• Beneficiário: Vitor Heloiz Nascimento
• Modalidade de apoio: Auxílio à Pesquisa - Temático