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Reseach Article

A Novel Application of Extended Kalman Filter for Efficient Information Processing in Subsurfaces

by Dimple Juneja, Atul Sharma, A.K Sharma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 17 - Number 2
Year of Publication: 2011
Authors: Dimple Juneja, Atul Sharma, A.K Sharma
10.5120/2192-2783

Dimple Juneja, Atul Sharma, A.K Sharma . A Novel Application of Extended Kalman Filter for Efficient Information Processing in Subsurfaces. International Journal of Computer Applications. 17, 2 ( March 2011), 28-32. DOI=10.5120/2192-2783

@article{ 10.5120/2192-2783,
author = { Dimple Juneja, Atul Sharma, A.K Sharma },
title = { A Novel Application of Extended Kalman Filter for Efficient Information Processing in Subsurfaces },
journal = { International Journal of Computer Applications },
issue_date = { March 2011 },
volume = { 17 },
number = { 2 },
month = { March },
year = { 2011 },
issn = { 0975-8887 },
pages = { 28-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume17/number2/2192-2783/ },
doi = { 10.5120/2192-2783 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:04:36.181400+05:30
%A Dimple Juneja
%A Atul Sharma
%A A.K Sharma
%T A Novel Application of Extended Kalman Filter for Efficient Information Processing in Subsurfaces
%J International Journal of Computer Applications
%@ 0975-8887
%V 17
%N 2
%P 28-32
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recent works indicates that innovative deployment of sensors in subsurfaces can beneficially support the production of oil and gas. The data which is sensed by such sensors is usually corrupted with noise. Filtering is desirable in such embedded systems in order to smooth out such fluctuations that otherwise would shorten the lifespan of sensors. This contribution presents a unique application of Kalman filtering technique for processing such sensitive information because sensor readings are usually imprecise due to strong variations in environment and also, computation has to be much more energy efficient than communication. Out of the various filtering algorithms available, we have chosen to apply Kalman filter, primarily because it works well both in theory and practice and moreover, it is able to minimize the variance of estimation error i.e. filters noise from the actual signal more accurately.

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Index Terms

Computer Science
Information Sciences

Keywords

Wireless Sensor Networks Kalman Filtering Algorithm Extended Kalman Filter Information Processing Estimation Error