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Proceedings Paper

Fractional Brownian motion: a maximum-likelihood estimator for blurred data
Author(s): Rachid Harba; William J. Ohley; Stephan Hoefer
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Paper Abstract

Fractional Brownian motion is a useful tool to describe many objects and phenomena. But in the case of real data, the estimation of the H parameter is corrupted by noise and sometimes blur. The maximum likelihood estimation of H can take into account these perturbations. This communication deals with the problem of the blur which is modeled by a low pass filter. It is then possible to rewrite the autocorrelation function of the data and the estimation of H is performed. The Cramer-Rao lower bound (CRLB) is stated. Finally, synthetic data permit the proof that the estimation of H is possible even if the signal is blurred. The variance of the estimates is compared with the CRLB and shows the quality of the results.

Paper Details

Date Published: 1 November 1992
PDF: 6 pages
Proc. SPIE 1818, Visual Communications and Image Processing '92, (1 November 1992); doi: 10.1117/12.131508
Show Author Affiliations
Rachid Harba, Univ. d'Orleans (France)
William J. Ohley, Univ. of Rhode Island (United States)
Stephan Hoefer, Univ. Kaiserslautern (Germany)


Published in SPIE Proceedings Vol. 1818:
Visual Communications and Image Processing '92
Petros Maragos, Editor(s)

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