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Multiple Object Tracking in Wireless Sensor Network based on the Mixture of KF and MLE

Published on November 2012 by P. Ganeshwari, S. Diwakaran
journal_cover_thumbnail

International Conference on Electronics, Communication and Information systems
Foundation of Computer Science USA
ICECI - Number 2
November 2012
Authors: P. Ganeshwari, S. Diwakaran
58674493-e496-47c2-95fb-bfaf14200191

P. Ganeshwari, S. Diwakaran . Multiple Object Tracking in Wireless Sensor Network based on the Mixture of KF and MLE. International Conference on Electronics, Communication and Information systems. ICECI, 2 (November 2012), 17-20.

@article{
author = { P. Ganeshwari, S. Diwakaran },
title = { Multiple Object Tracking in Wireless Sensor Network based on the Mixture of KF and MLE },
journal = { International Conference on Electronics, Communication and Information systems },
issue_date = { November 2012 },
volume = { ICECI },
number = { 2 },
month = { November },
year = { 2012 },
issn = 0975-8887,
pages = { 17-20 },
numpages = 4,
url = { /specialissues/iceci/number2/9468-1016/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 International Conference on Electronics, Communication and Information systems
%A P. Ganeshwari
%A S. Diwakaran
%T Multiple Object Tracking in Wireless Sensor Network based on the Mixture of KF and MLE
%J International Conference on Electronics, Communication and Information systems
%@ 0975-8887
%V ICECI
%N 2
%P 17-20
%D 2012
%I International Journal of Computer Applications
Abstract

Wireless Sensor Networks (WSNs) has wide variety of applications and object tracking is one among that applications. In this paper, tracking of multiple objects is considered. The Extended Kalman Filter (EKF) algorithms are mostly used to get a linear form from the nonlinear states and then applying the standard Kalman filter for updating the state estimate. However, a significant drawback of the EKF algorithms is that the resulting estimation of the state diverges from its original state in many applications. In order to solve this problem one measurement conversion method Maximum Likelihood Estimation (MLE) is proposed in this paper. The analysis is based on object tracking in multiple sensor system. The object tracking is difficult because sensors have to be send data from other sensors to the head with same timestamps. This is the well known data association problem and a method which is combination of Kalman Filter (KF) and Maximum Likelihood Method is proposed to solve this problem.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Wireless Sensor Networks (wsns) Object Tracking Extended Kalman Filter (ekf) Kalman Filter (kf)