Joint Kalman–Haar Algorithm Applied to Signal Processing

Under the analysis of signals disturbed by noise, in this paper we propose a working methodology aimed to seize the best estimate of combining Kalman filtering with the characterization that is achieved by applying a multiresolution analysis (MRA) using wavelets. From the standpoint of Kalman filter...

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Main Authors: Viegener, Alejandro, Sirne, Ricardo O., Serrano, Eduardo P., Fabio, Marcela, D'Attellis, Carlos E.
Format: artículo científico
Language: Español
Published: 2015
Online Access: http://revistas.ucr.ac.cr/index.php/matematica/article/view/2103
http://hdl.handle.net/10669/13005
id RepoKERWA13005
recordtype dspace
spelling RepoKERWA130052021-05-02T23:20:49Z Joint Kalman–Haar Algorithm Applied to Signal Processing Algoritmo conjunto Kalman–Haar aplicado al procesamiento de señales Viegener, Alejandro Sirne, Ricardo O. Sirne, Ricardo O. Serrano, Eduardo P. Serrano, Eduardo P. Fabio, Marcela Fabio, Marcela D'Attellis, Carlos E. D'Attellis, Carlos E. Under the analysis of signals disturbed by noise, in this paper we propose a working methodology aimed to seize the best estimate of combining Kalman filtering with the characterization that is achieved by applying a multiresolution analysis (MRA) using wavelets. From the standpoint of Kalman filtering this combined procedure is quasi-optimal, but the change to be made allows the simultaneous implementation of a scheme of wavelet denoising; with this decreases the computational cost of applying both procedures separately. Our proposal is to process the signal by successive non-overlapping intervals, combining the process for calculating the optimal filter with a MRA using the Haar wavelet. The method takes advantage of the combined use of both tools (Kalman-Haar) and is free from edge problems related to the signal segmentation. En el marco del análisis de señales perturbadas por ruido, en esta presentación proponemos una metodología de trabajo orientada a aprovechar la estimación óptima del filtrado de Kalman, combinándola con la caracterización que se logra aplicando un análisis de multirresoluci´on (AMR) mediante onditas (wavelets). Desde el punto de vista del filtrado de Kalman este procedimiento mixto es cuasi-óptimo, sin embargo la modificación que se introduce permite la aplicación simultánea de un esquema de eliminación de ruido con wavelets; con esto disminuye el costo computacional de aplicar ambos procedimientos por separado. Nuestra propuesta consiste en procesar la señal por intervalos sucesivos no solapados, combinando el proceso de cálculo para el filtrado óptimo con un AMR usando la ondita de Haar. El método aprovecha la utilización conjunta de ambas herramientas (Kalman- Haar) y está exento de problemas de borde relacionados con la segmentación de la señal. 2015-05-19T19:01:05Z 2015-05-19T19:01:05Z 2012-03-08 00:00:00 2015-05-19T19:01:05Z artículo científico http://revistas.ucr.ac.cr/index.php/matematica/article/view/2103 http://hdl.handle.net/10669/13005 10.15517/rmta.v19i1.2103 es Revista de Matemática: Teoría y Aplicaciones Vol. 19 Núm. 1 2012 37-47 application/pdf
institution Universidad de Costa Rica
collection Repositorio KERWA
language Español
description Under the analysis of signals disturbed by noise, in this paper we propose a working methodology aimed to seize the best estimate of combining Kalman filtering with the characterization that is achieved by applying a multiresolution analysis (MRA) using wavelets. From the standpoint of Kalman filtering this combined procedure is quasi-optimal, but the change to be made allows the simultaneous implementation of a scheme of wavelet denoising; with this decreases the computational cost of applying both procedures separately. Our proposal is to process the signal by successive non-overlapping intervals, combining the process for calculating the optimal filter with a MRA using the Haar wavelet. The method takes advantage of the combined use of both tools (Kalman-Haar) and is free from edge problems related to the signal segmentation.
format artículo científico
author Viegener, Alejandro
Sirne, Ricardo O.
Sirne, Ricardo O.
Serrano, Eduardo P.
Serrano, Eduardo P.
Fabio, Marcela
Fabio, Marcela
D'Attellis, Carlos E.
D'Attellis, Carlos E.
spellingShingle Viegener, Alejandro
Sirne, Ricardo O.
Sirne, Ricardo O.
Serrano, Eduardo P.
Serrano, Eduardo P.
Fabio, Marcela
Fabio, Marcela
D'Attellis, Carlos E.
D'Attellis, Carlos E.
Joint Kalman–Haar Algorithm Applied to Signal Processing
author_sort Viegener, Alejandro
title Joint Kalman–Haar Algorithm Applied to Signal Processing
title_short Joint Kalman–Haar Algorithm Applied to Signal Processing
title_full Joint Kalman–Haar Algorithm Applied to Signal Processing
title_fullStr Joint Kalman–Haar Algorithm Applied to Signal Processing
title_full_unstemmed Joint Kalman–Haar Algorithm Applied to Signal Processing
title_sort joint kalman–haar algorithm applied to signal processing
publishDate 2015
url http://revistas.ucr.ac.cr/index.php/matematica/article/view/2103
http://hdl.handle.net/10669/13005
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