Um algoritmo kernel baseado na ortogonalização de Gram-Schmidt para redes de difusão adaptativas

André Amaro Bueno, Daniel Tiglea, Renato Candido, Magno T. M. Silva

DOI: 10.14209/sbrt.2021.1570731661

Evento: XXXIX Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2021)

Keywords: Redes de difusão adaptativas filtragem adaptativa baseada em núcleo esparsificação de dicionário processamento não linear de sinais

Abstract

Adaptive diffusion networks have attracted attention in the scientific community as an efficient solution for distributed estimation of signals. They also have been employed in nonlinear estimation problems with distributed kernel adaptive algorithms. These solutions present a high computational cost due to the dictionary of kernel algorithms. In this paper, we propose a reduced-cost kernel algorithm for adaptive diffusion networks. It is based on the Gram-Schmidt orthogonalization process, used to span the vector space of the mapped vectors contained in the dictionary, which leads to a dictionary with a reduced cardinality. By means of simulations, we observe an advantageous computational savings in comparison to classical techniques.

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