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Proceedings Paper

Fractional Brownian motion and its fractal dimension estimation
Author(s): Peng Zhang; Andrew B. Martinez; Herbert S. Barad
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Paper Abstract

A mathematical model of stochastic processes -- fractional Brownian motion -- is addressed. The power-law behaviors of FBM increments are studied in detail for moments, correlation functions, and power spectra. A moment method is proposed to do model testing of fractional Brownian motion. The results of FBM model testing of six simulators show that the covariance matrix transforming algorithm can provide samples with very good approximation of self-affinity. The self-affinity of the FBM samples generated by Fourier transform filtering is not very obvious. The statistical properties of fractal dimension estimation methods are analyzed. The simulation results show that the variance method provides good performance when only estimates of variances with small time lags are used in the least-squares estimation. For the power spectrum method, the bias is not ignorable because of the aliasing and the window effect.

Paper Details

Date Published: 1 October 1991
PDF: 12 pages
Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); doi: 10.1117/12.48397
Show Author Affiliations
Peng Zhang, Tulane Univ. (United States)
Andrew B. Martinez, Tulane Univ. (United States)
Herbert S. Barad, Tulane Univ. (United States)


Published in SPIE Proceedings Vol. 1569:
Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision
Su-Shing Chen, Editor(s)

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