Mutual information based data association

Alempijevic, A; Kodagoda, S; Dissanayake, G

Mutual information based data association

Alempijevic, A

Kodagoda, S

Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: ISSNIP 2009 - Proceedings of 2009 5th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 2009, pp. 97 - 102
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	Alempijevic, A https://orcid.org/0000-0002-1769-8041	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	ISSNIP 2009 - Proceedings of 2009 5th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 2009, pp. 97 - 102	en_US
dc.identifier.isbn	9781424435180	en_US
dc.identifier.uri	http://hdl.handle.net/10453/11503
dc.description.abstract	Relating information originating from disparate sensors without any attempt to model the environment or the behaviour of any particular object within it is a challenging task. Inspired by human perception, the focus of this paper will be on observing objects moving in space using sensors that operate based on different physical principles and the fact that motion has in principle, greater power to specify properties of an object than purely spatial information captured as a single observation in time. The contribution of this paper include the development of a novel strategy for detecting a set of signals that are statistically dependent and correspond to each other related by a common cause. Mutual Information is proposed as a measure of statistical dependence. The algorithm is evaluated through simulations and three application domains, which includes, (1.) Grouping problem in images, (2.) Data association problem in moving observers with dynamic targets, and (3.) Multi-modal sensor fusion. © 2009 IEEE.	en_US
dc.relation.ispartof	ISSNIP 2009 - Proceedings of 2009 5th International Conference on Intelligent Sensors, Sensor Networks and Information Processing	en_US
dc.relation.isbasedon	10.1109/ISSNIP.2009.5416855	en_US
dc.title	Mutual information based data association	en_US
dc.type	Conference Proceeding
utslib.for	0902 Automotive Engineering	en_US
dc.location.activity	Melbourne, Australia	en_US
dc.location.activity	Melbourne, AUSTRALIA
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 Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

Relating information originating from disparate sensors without any attempt to model the environment or the behaviour of any particular object within it is a challenging task. Inspired by human perception, the focus of this paper will be on observing objects moving in space using sensors that operate based on different physical principles and the fact that motion has in principle, greater power to specify properties of an object than purely spatial information captured as a single observation in time. The contribution of this paper include the development of a novel strategy for detecting a set of signals that are statistically dependent and correspond to each other related by a common cause. Mutual Information is proposed as a measure of statistical dependence. The algorithm is evaluated through simulations and three application domains, which includes, (1.) Grouping problem in images, (2.) Data association problem in moving observers with dynamic targets, and (3.) Multi-modal sensor fusion. © 2009 IEEE.

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http://hdl.handle.net/10453/11503