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Linear Prediction of Discrete-Time 1/f Processes

S. Yousefi ; J. Jalden ; Thomas Eriksson (Institutionen för signaler och system, Kommunikationssystem)
IEEE Signal Processing Letters (1070-9908). Vol. 17 (2010), 11, p. 901-904.
[Artikel, refereegranskad vetenskaplig]

In this letter, the linear predictability of discrete-time stationary stochastic processes with 1/vertical bar f vertical bar(alpha)-shaped power spectral density (PSD) is considered. In particular, the spectral flatness measure (SFM)-which yields a lower bound for the normalized mean-squared-error (NMSE) of any linear one-step-ahead (OSA) predictor-is obtained analytically as a function of alpha is an element of [0, 1]. By comparing the SFM bound to the NMSE of the p-tap linear minimum-mean-square error (LMMSE) predictor, it is shown that close to optimal NMSE performance may be achieved for relatively moderate values of. The performance of the LMMSE predictor for the discrete-time fractional Gaussian noise (DFGN), which may be viewed as the conventional discrete-time counterpart of continuous-time processes with 1/vertical bar f vertical bar(alpha)-shaped PSD, shows that the DFGN is more easily predicted than the discrete-time processes considered herein.

Nyckelord: Fractional Brownian motion, fractional Gaussian noise, linear, prediction, spectral flatness measure, 1/f-process, fractional brownian-motion, image texture, noise

Denna post skapades 2010-11-15. Senast ändrad 2016-02-01.
CPL Pubid: 129068


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