Linear Prediction of Discrete-Time 1/f Processes
Artikel i vetenskaplig tidskrift, 2010

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.

fractional Gaussian noise




spectral flatness measure

Fractional Brownian motion

image texture


fractional brownian-motion


S. Yousefi

Kungliga Tekniska Högskolan (KTH)

J. Jalden

Kungliga Tekniska Högskolan (KTH)

Thomas Eriksson

Chalmers, Signaler och system, Kommunikation, Antenner och Optiska Nätverk

IEEE Signal Processing Letters

1070-9908 (ISSN) 15582361 (eISSN)

Vol. 17 11 901-904 5557758


Data- och informationsvetenskap



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