By Topic

Linear Prediction of Discrete-Time 1/f Processes

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Yousefi, Siamak ; KTH Signal Process. Lab., R. Inst. of Technol., Stockholm, Sweden ; Jalden, J. ; Eriksson, T.

In this letter, the linear predictability of discrete-time stationary stochastic processes with 1/|f|α-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 α ∈ [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 p. 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/|f|α-shaped PSD, shows that the DFGN is more easily predicted than the discrete-time processes considered herein.

Published in:

Signal Processing Letters, IEEE  (Volume:17 ,  Issue: 11 )