Research Article
RFID Multi-target Tracking Using the Probability Hypothesis Density Algorithm for a Health Care Application
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@INPROCEEDINGS{10.1007/978-3-642-32304-1_9, author={Jiandan Chen and Iyeyinka Olayanju and Olabode Ojelabi and Wlodek Kulesza}, title={RFID Multi-target Tracking Using the Probability Hypothesis Density Algorithm for a Health Care Application}, proceedings={IT Revolutions. Third International ICST Conference, C\^{o}rdoba, Spain, March 23-25, 2011, Revised Selected Papers}, proceedings_a={IT REVOLUTIONS}, year={2012}, month={10}, keywords={Human Tracking Probability Hypothesis Density Radio Frequency Identification}, doi={10.1007/978-3-642-32304-1_9} }- Jiandan Chen
Iyeyinka Olayanju
Olabode Ojelabi
Wlodek Kulesza
Year: 2012
RFID Multi-target Tracking Using the Probability Hypothesis Density Algorithm for a Health Care Application
IT REVOLUTIONS
Springer
DOI: 10.1007/978-3-642-32304-1_9
Abstract
The intelligent multi-sensor system is a system for target detection, identification and information processing for human activities surveillance and ambient assisted living. This paper describes RFID multi-target tracking using the Gaussian Mixture Probability Hypothesis Density, GM-PHD, algorithm. The multi target tracking ability of the proposed solution is demonstrated in a simulation and real environment. A performance comparison of the Levenberg-Marquardt algorithm with and without the GM-PHD filter shows that the GM-PHD algorithm improves the accuracy of tracking and target position estimation significantly. This improvement is demonstrated by a simulation and by a physical experiment.
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