FRACTAL ESTIMATION FROM NOISY DATA VIA DISCRETE FRACTIONAL GAUSSIAN-NOISE (DFGN) AND THE HAAR BASIS

被引：57

作者：

KAPLAN, LM ^{[1
]}

KUO, CCJ ^{[1
]}

机构：

[1] UNIV SO CALIF, DEPT ELECT ENGN SYST, LOS ANGELES, CA 90089 USA

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 1993年 / 41卷 / 12期

关键词：

D O I：

10.1109/78.258096

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We show that the application of the discrete wavelet transform (DWT) using the Haar basis to the increments of fractional Brownian motion (fBm), also known as discrete fractional Gaussian noise (DFGN), yields coefficients which are weakly correlated and have a variance that is exponentially related to scale. Similar results were derived by Flandrin, Tewfik, and Kim for a continuous-time fBm going through a continuous wavelet transform (CWT). The new theoretical results justify an improvement to a fractal estimation algorithm recently proposed by Wornell and Oppenheim. The performance of the new algorithm is compared with that of Wornell and Oppenheim's algorithm in numerical simulation.

引用

页码：3554 / 3562

页数：9

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