Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Efficient particle filtering for tracking maneuvering objects|
|Citation:||IEEE/ION Position Location and Navigation Symposium, held in Indian Wells, California, 4-6 May, 2010: pp.332-339|
|Conference Name:||IEEE/ION Position Location and Navigation Symposium (2010 : Indian Wells, California)|
|School/Discipline:||School of Computer Science|
|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.|
|Appears in Collections:||Computer Science publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.