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Parameter estimation of a fractional Brownian motion in a white noise using wavelets

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1 Author(s)
Wen-Liang Hwang ; California Univ., Irvine, CA, USA

To discriminate the fractal parameter of a fractional Brownian motion (fBm) embedded in a white noise is equivalent to discriminating the composite singularity formed by superimposing a peak singularity upon a Dirac singularity. We use the autocorrelation of the wavelet transform coefficients to characterize the composite singularity, by formalizing this problem as a nonlinear optimization problem. We modify the internal penalty function method to efficiently estimate the parameters of the fBm in the white noise

Published in:

Speech, Image Processing and Neural Networks, 1994. Proceedings, ISSIPNN '94., 1994 International Symposium on

Date of Conference:

13-16 Apr 1994