<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Marta Marron</style></author><author><style face="normal" font="default" size="100%">Garcia, Juan Carlos</style></author><author><style face="normal" font="default" size="100%">Miguel Angel Sotelo</style></author><author><style face="normal" font="default" size="100%">M. Cabello</style></author><author><style face="normal" font="default" size="100%">Daniel Pizarro</style></author><author><style face="normal" font="default" size="100%">Francisco Huerta</style></author><author><style face="normal" font="default" size="100%">Jesus Cerro</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparing a Kalman Filter and a Particle Filter in a Multiple Objects Tracking Application</style></title><secondary-title><style face="normal" font="default" size="100%">2007 IEEE International Symposium on Intelligent Signal Processing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Multi-Object Tracking</style></keyword><keyword><style  face="normal" font="default" size="100%">position estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">Probabilistic Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">robotics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2007</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?asf_arn=null&amp;asf_iid=null&amp;asf_pun=4447489&amp;asf_in=null&amp;asf_rpp=null&amp;asf_iv=null&amp;asf_sp=null&amp;asf_pn=3 </style></url></web-urls><related-urls><url><style face="normal" font="default" size="100%">https://mail.geintra-uah.org/en/system/files/private/wisp07_def.pdf</style></url></related-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Alcalá de Henares, Spain</style></pub-location><pages><style face="normal" font="default" size="100%">1-6</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4244-0829-0</style></isbn><language><style face="normal" font="default" size="100%">English</style></language><abstract><style face="normal" font="default" size="100%">Two of the most important solutions in position estimation are compared, in this paper, in order to test their efficiency in a multi-tracking application in an unstructured and complex environment. A Particle Filter is extended and adapted with a clustering process in order to track a variable number of objects. The other approach is to use a Kalman Filter with an association algorithm for each of the objects to track. Both algorithms are described in the paper and the results obtained with their real-time execution in the mentioned application are shown. Finally interesting conclusions extracted from this comparison are remarked at the end.</style></abstract></record></records></xml>