RLS Adaptive Filter With Inequality Constraints

Autor(es)

Nascimento, Vitor H. ^[1] ; Zakharov, Yuriy V. ^[2]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Dept Elect Syst Engn, BR-05508970 Sao Paulo - Brazil

^[2] Univ York, Dept Elect, York YO10 5DD, N Yorkshire - England

• Tipo de documento: Artigo Científico
• Fonte: IEEE SIGNAL PROCESSING LETTERS; v. 23, n. 5 MAY 2016.
• Citações Web of Science: 7

Resumo

In practical implementations of estimation algorithms, designers usually have information about the range in which the unknown variables must lie either due to physical constraints (such as power always being non-negative) or due to hardware constraints (such as in implementations using fixedpoint arithmetic). In this letter, we propose a fast (i.e., whose complexity grows linearly with the filter length) version of the dichotomous coordinate descent recursive least-squares (RLS) adaptive filter which can incorporate constraints on the variables. The constraints can be in the form of lower and upper bounds on each entry of the filter, or norm bounds. We compare the proposed algorithm with the recently proposed normalized non-negative least-mean-squares (N-NLMS) and projected-gradient normalized LMS (PG-NLMS) filters, which also include inequality constraints in the variables. (AU)

• Processo FAPESP: 14/50765-6 - Knowledge-aided signal processing: theory, algorithms, implementation and applications
• Beneficiário: Vitor Heloiz Nascimento
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 14/04256-2 - Algoritmos de baixo custo computacional para estimação de parâmetros
• Beneficiário: Vitor Heloiz Nascimento
• Modalidade de apoio: Auxílio à Pesquisa - Regular