Closed form PHD filtering for linear jump Markov models

Pasha, A; Vo, B; Tuan, HD; Ma, WK

Closed form PHD filtering for linear jump Markov models

Pasha, A Vo, B Tuan, HD

Ma, WK

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Publication Type:: Conference Proceeding
Citation:: 2006 9th International Conference on Information Fusion, FUSION, 2006
Issue Date:: 2006-12-01

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Field	Value	Language
dc.contributor.author	Pasha, A	en_US
dc.contributor.author	Vo, B	en_US
dc.contributor.author	Tuan, HD https://orcid.org/0000-0003-0292-6061	en_US
dc.contributor.author	Ma, WK	en_US
dc.date.issued	2006-12-01	en_US
dc.identifier.citation	2006 9th International Conference on Information Fusion, FUSION, 2006	en_US
dc.identifier.isbn	1424409535	en_US
dc.identifier.isbn	9781424409532	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16433
dc.description.abstract	In recent years there has been much interest in the probability hypothesis density (PHD) filtering approach, an attractive alternative to tracking unknown numbers of targets and their states in the presence of data association uncertainty, clutter, noise, and miss-detection. In particular, it has been discovered that the PHD filter has a closed form solution under linear Gaussian assumptions on the target dynamics and birth. This finding opens up a new direction where the PHD filter can be practically implemented in an effective and reliable fashion. However, the previous work is not general enough to handle jump Markov systems (JMS), a popular approach to modeling maneuvering targets. In this paper, a closed form solution for the PHD filter with linear JMS is derived. Our simulations demonstrate that the proposed PHD filtering algorithm provides promising performance. In particular, the algorithm is capable of tracking multiple maneuvering targets that cross each other.	en_US
dc.relation.ispartof	2006 9th International Conference on Information Fusion, FUSION	en_US
dc.relation.isbasedon	10.1109/ICIF.2006.301593	en_US
dc.title	Closed form PHD filtering for linear jump Markov models	en_US
dc.type	Conference Proceeding
utslib.for	0914 Resources Engineering and Extractive Metallurgy	en_US
dc.location.activity	Florence, Italy	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

In recent years there has been much interest in the probability hypothesis density (PHD) filtering approach, an attractive alternative to tracking unknown numbers of targets and their states in the presence of data association uncertainty, clutter, noise, and miss-detection. In particular, it has been discovered that the PHD filter has a closed form solution under linear Gaussian assumptions on the target dynamics and birth. This finding opens up a new direction where the PHD filter can be practically implemented in an effective and reliable fashion. However, the previous work is not general enough to handle jump Markov systems (JMS), a popular approach to modeling maneuvering targets. In this paper, a closed form solution for the PHD filter with linear JMS is derived. Our simulations demonstrate that the proposed PHD filtering algorithm provides promising performance. In particular, the algorithm is capable of tracking multiple maneuvering targets that cross each other.

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http://hdl.handle.net/10453/16433