By Topic

Applications of adaptive digital filtering to the data processing for the environmental system

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)
Kikuchi, A. ; University of Tokushima, Tokushimsa, Japan ; Omatu, S. ; Soeda, T.

In this paper a least mean-square (LMS) adaptive digital filter (ADF) is used in order to detect the extraordinary levels of air pollution data, to predict the future air pollution levels, and to identify the unknown parameters in the environmental system. The technique used here is based on the recursive adaptive digital filtering method proposed by White, which is an extension of the usual ADF by Widrow. For the Oxdata developed at Sooka, Koshigaya, Kasukabe, and Kawaguchi, Japan, the extraordinary levels of the Oxdata are detected by using the recursive ADF. For the SO2data at Komatsushima, Japan, the predicted values of the SO2levels are obtained by using the ADF as the predictor. Finally, a new identification method is proposed to find the unknown parameters of the AR, MA, and ARMA processes and is applied to identify the environmental system.

Published in:

Acoustics, Speech and Signal Processing, IEEE Transactions on  (Volume:27 ,  Issue: 6 )