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.<>


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    Title :

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


    Contributors:


    Publication date :

    1994-01-01


    Size :

    283361 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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