Wavelet estimation of fractional Brownian motion embedded in a noisy environment

Lei Zhang, Paul Bao, Xiaolin Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)


This correspondence proposes a wavelet-based fractional Brownian motion (fBm) signal estimation scheme. Despite the fact that wavelet transform approximately whitens the fBm processes, it is observed that statistical dependencies still exist across adjacent wavelet scales and between neighboring wavelet coefficients. These dependencies can be exploited to improve the estimation of fBm signals embedded into noise. The idea is to reorganize the wavelet coefficients into a scale-time mixture model and then carry out the minimum mean-square-error estimation (MMSE) using the model. Experiments show that the proposed scheme obtains better estimates than Wornell and Oppenheim's algorithm, in which the wavelet dependencies are not utilized.
Original languageEnglish
Pages (from-to)2194-2200
Number of pages7
JournalIEEE Transactions on Information Theory
Issue number9
Publication statusPublished - 1 Sep 2004
Externally publishedYes

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences

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