Feature LMS Algorithm for Bandpass System Models

Autor(es)

Diniz, Paulo S. R. ; Yazdanpanah, Named ; Lima, Markus V. S. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO); v. N/A, p. 5-pg., 2019-01-01.

Resumo

Sparse representations of model parameters have been widely studied. In the adaptive filtering literature, most studies address the cases where the sparsity is directly observed, therefore, there is a growing interest in developing strategies to exploit hidden sparsity. Recently, the feature LMS (F-LMS) algorithm was proposed to expose the sparsity of models with low- and high-frequency contents. In this paper, the F-LMS algorithm is extended to expose hidden sparsity related to models with bandpass spectrum, including the cases of narrowband and broader passband sources. Some simulation results show that the proposed approaches lead to F-LMS algorithms with fast convergence, low misadjustment after convergence, and low computational cost. (AU)

• Processo FAPESP: 19/06280-1 - Integração, transformação, aumento de dados e controle de qualidade para representações intermediárias
• Beneficiário: Hamed Yazdanpanah
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 15/22308-2 - Representações intermediárias em Ciência Computacional para descoberta de conhecimento
• Beneficiário: Roberto Marcondes Cesar Junior
• Modalidade de apoio: Auxílio à Pesquisa - Temático