On deconvolution methods

Fuente: arXiv
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Autori principali: Ramm, Alexander G., Galstian, A.
Natura: Preprint
Pubblicazione: 2003
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_version_ 1866912655440084992
author Ramm, Alexander G.
Galstian, A.
author_facet Ramm, Alexander G.
Galstian, A.
contents Several methods for solving efficiently the one-dimensional deconvolution problem are proposed. The problem is to solve the Volterra equation ${\mathbf k} u:=\int_0^t k(t-s)u(s)ds=g(t),\quad 0\leq t\leq T$. The data, $g(t)$, are noisy. Of special practical interest is the case when the data are noisy and known at a discrete set of times. A general approach to the deconvolution problem is proposed: represent ${\mathbf k}=A(I+S)$, where a method for a stable inversion of $A$ is known, $S$ is a compact operator, and $I+S$ is injective. This method is illustrated by examples: smooth kernels $k(t)$, and weakly singular kernels, corresponding to Abel-type of integral equations, are considered. A recursive estimation scheme for solving deconvolution problem with noisy discrete data is justified mathematically, its convergence is proved, and error estimates are obtained for the proposed deconvolution method.
format Preprint
id arxiv_https___arxiv_org_abs_math_0301382
institution arXiv
publishDate 2003
record_format arxiv
spellingShingle On deconvolution methods
Ramm, Alexander G.
Galstian, A.
Numerical Analysis
45D05, 45L05, 45P05, 65R20, 65R30
Several methods for solving efficiently the one-dimensional deconvolution problem are proposed. The problem is to solve the Volterra equation ${\mathbf k} u:=\int_0^t k(t-s)u(s)ds=g(t),\quad 0\leq t\leq T$. The data, $g(t)$, are noisy. Of special practical interest is the case when the data are noisy and known at a discrete set of times. A general approach to the deconvolution problem is proposed: represent ${\mathbf k}=A(I+S)$, where a method for a stable inversion of $A$ is known, $S$ is a compact operator, and $I+S$ is injective. This method is illustrated by examples: smooth kernels $k(t)$, and weakly singular kernels, corresponding to Abel-type of integral equations, are considered. A recursive estimation scheme for solving deconvolution problem with noisy discrete data is justified mathematically, its convergence is proved, and error estimates are obtained for the proposed deconvolution method.
title On deconvolution methods
topic Numerical Analysis
45D05, 45L05, 45P05, 65R20, 65R30
url https://arxiv.org/abs/math/0301382