By Topic

Polynomial prediction using incomplete data

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
$33 $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

1 Author(s)
P. T. Harju ; Lab. of Telecommun. Technol., Helsinki Univ. of Technol., Espoo

We derive an FIR polynomial predictor for data in which some samples are missing. The method is compared with a computationally lighter algorithm that is based on decision-driven recursion. Both schemes are found to perform almost identically well on predicting a sinusoidal signal corrupted by both impulsive and Gaussian noise

Published in:

IEEE Transactions on Signal Processing  (Volume:45 ,  Issue: 3 )