Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/76763
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Type: Conference paper
Title: Efficient particle filtering for tracking maneuvering objects
Author: Sathyan, Thuraiappah
Hedley, Mark
Citation: IEEE/ION Position Location and Navigation Symposium, held in Indian Wells, California, 4-6 May, 2010: pp.332-339
Publisher: IEEE
Issue Date: 2010
ISBN: 9781424450374
9781424450367
ISSN: 2153-358X
Conference Name: IEEE/ION Position Location and Navigation Symposium (2010 : Indian Wells, California)
PLANS 2010
School/Discipline: School of Computer Science
Statement of
Responsibility: 
Thuraiappah Sathyan and Mark Heldley
Abstract: Accurate tracking of elite athletes for performance monitoring allows sports scientists to optimize training to gain a competitive edge. An important challenge in this application is that the maneuverability of the athletes is high and the traditional Kalman filter (KF) will not provide satisfactory tracking accuracy. Further, high update rates, of the order of tens of updates per second for each player, are often required and hence, the tracking algorithm considered should be computationally efficient. In this paper we propose a computationally efficient multiple model particle filter (MM-PF) algorithm for tracking maneuvering objects. It uses a Gaussian proposal density based on the unscented KF and a deterministic sampling technique and provides tracking accuracy similar to that of the augmented MM-PF, but with much lower computational cost. The performance of the proposed algorithm was verified using simulations and data collected in field trials. The trials were conducted with the Australian Institute of Sport using a localization system we have designed.
Keywords: RF-positioning and tracking; maneuvering object; particle filtering; unscented transformation
Rights: © Copyright 2010 IEEE - All rights reserved.
RMID: 0020121752
DOI: 10.1109/PLANS.2010.5507298
Appears in Collections:Computer Science publications

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