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Kalman Filter Tracking

by Nasser H. Ali, Ghassan M. Hassan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 89 - Number 9
Year of Publication: 2014
Authors: Nasser H. Ali, Ghassan M. Hassan
10.5120/15530-4315

Nasser H. Ali, Ghassan M. Hassan . Kalman Filter Tracking. International Journal of Computer Applications. 89, 9 ( March 2014), 15-18. DOI=10.5120/15530-4315

@article{ 10.5120/15530-4315,
author = { Nasser H. Ali, Ghassan M. Hassan },
title = { Kalman Filter Tracking },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 89 },
number = { 9 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 15-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume89/number9/15530-4315/ },
doi = { 10.5120/15530-4315 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:08:47.851600+05:30
%A Nasser H. Ali
%A Ghassan M. Hassan
%T Kalman Filter Tracking
%J International Journal of Computer Applications
%@ 0975-8887
%V 89
%N 9
%P 15-18
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Kalman filter estimates the state of a dynamic system, even if the precise form of the system is unknown. The filter is very powerful in the sense that it supports estimations of past and even future states. The description of the standard Kalman filter and its algorithms with the two main steps, the prediction step and the correction step. Furthermore the extended Kalman filter is discussed, which represents the conversion of the Kalman filter to nonlinear systems. Finally these filter was tested on aircraft tracking, and sinus wave using MATLAB.

References
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Index Terms

Computer Science
Information Sciences

Keywords

KF EKF Prediction dynamic model state vector

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