A Gaussian Mixture PHD Filter for Jump Markov System Models

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dc.contributor.author Pasha, SA
dc.contributor.author Vo, B
dc.contributor.author Hoang, TD
dc.contributor.author Ma, W
dc.date.accessioned 2012-02-02T10:56:15Z
dc.date.issued 2009-01
dc.identifier.citation IEEE Transactions On Aerospace And Electronic Systems, 2009, 45 (3), pp. 919 - 936
dc.identifier.issn 0018-9251
dc.identifier.other C1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/15292
dc.description.abstract The probability hypothesis density (PHD) filter is an attractive approach to tracking an unknown and time-varying number of targets in the presence of data association uncertainty, clutter, noise, and detection uncertainty. The PHD filter admits a closed-form solution for a linear Gaussian multi-target model. However, this model is not general enough to accommodate maneuvering targets that switch between several models. In this paper, we generalize the notion of linear jump Markov systems to the multiple target case to accommodate births, deaths, and switching dynamics. We then derive a closed-form solution to the PHD recursion for the proposed linear Gaussian jump Markov multi-target model. Based on this an efficient method for tracking multiple maneuvering targets that switch between a set of linear Gaussian models is developed. An analytic implementation of the PHD filter using statistical linear regression technique is also proposed for targets that switch between a set of nonlinear models. We demonstrate through simulations that the proposed PHD filters are effective in tracking multiple maneuvering targets.
dc.publisher IEEE-Inst Electrical Electronics Engineers Inc
dc.relation.isbasedon 10.1109/TAES.2009.5259174
dc.title A Gaussian Mixture PHD Filter for Jump Markov System Models
dc.type Journal Article
dc.parent IEEE Transactions On Aerospace And Electronic Systems
dc.journal.volume 3
dc.journal.volume 45
dc.journal.number 3 en_US
dc.publocation Piscataway en_US
dc.publocation Leeds, UK
dc.identifier.startpage 919 en_US
dc.identifier.endpage 936 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.conference Global Events Congress
dc.for 0906 Electrical and Electronic Engineering
dc.personcode 110708
dc.percentage 100 en_US
dc.classification.name Electrical and Electronic Engineering en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.date.activity 2010-06-14
dc.location.activity ISI:000270225500008 en_US
dc.location.activity Leeds, UK
dc.description.keywords The probability hypothesis density (PHD) filter is an attractive approach to tracking an unknown and time-varying number of targets in the presence of data association uncertainty, clutter, noise, and detection uncertainty. The PHD filter admits a closed en_US
dc.description.keywords Sport-for-Development, Community Participation, Change Agent, Sport Event Framework, Event Outcomes
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Strength - Health Technologies
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
utslib.collection.history Uncategorised (ID: 363)
utslib.collection.history Closed (ID: 3)


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