Neuro-fuzzy Learning Applied to Improve the Trajectory Reconstruction Problem

Concha, OP; Garcia, J; Molina, JM

Neuro-fuzzy Learning Applied to Improve the Trajectory Reconstruction Problem

Concha, OP Garcia, J Molina, JM

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA 2006) Jointly with International Conference on Intelligent Agents Web Technologies and International Commerce (IAWTIC 2006), 2006, pp. 1 - 6
Issue Date:: 2006-01

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Field	Value	Language
dc.contributor.author	Concha, OP	en_US
dc.contributor.author	Garcia, J	en_US
dc.contributor.author	Molina, JM	en_US
dc.contributor.editor	Masoud Mohammadian	en_US
dc.date	2006-11-29	en_US
dc.date.issued	2006-01	en_US
dc.identifier.citation	International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA 2006) Jointly with International Conference on Intelligent Agents Web Technologies and International Commerce (IAWTIC 2006), 2006, pp. 1 - 6	en_US
dc.identifier.isbn	0-7695-2731-0	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16255
dc.description.abstract	This paper presents the application of a neuro-fuzzy learning approach to classify Air Traffic Control (ATC) trajectory segments from recorded opportunity traffic. This method learns a fuzzy system using neural-network theory to determine its parameters (fuzzy sets and fuzzy rules) by processing data samples. The problem is prepared for analysing the Markovchain probabilities estimated by an Interacting Multiple Model (IMM) tracking filter operating forward and backward over available data. The performance of this data-driven classification system is compared with a more conventional approach based on transition detection on simulated and real data of representative situations. The problem's formulation for this application enabled an accurate classification of manoeuvring segments and the derivation of rules that explain the relation between input attributes and motion categories used to describe the recorded data.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA 2006) Jointly with International Conference on Intelligent Agents Web Technologies and International Commerce (IAWTIC 2006)	en_US
dc.relation.ispartof	International Conference on Intelligent Agents Web Technologies and International Commerce	en_US
dc.relation.isbasedon	10.1109/CIMCA.2006.157	en_US
dc.rights	© 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Neuro-fuzzy Learning Applied to Improve the Trajectory Reconstruction Problem	en_US
dc.type	Conference Proceeding
utslib.location	USA	en_US
utslib.location.activity	Sydney, NSW, Australia	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Sydney, NSW, Australia	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 Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.place-of-publication	USA	en_US
pubs.start-date	2006-11-29	en_US

Abstract:

This paper presents the application of a neuro-fuzzy learning approach to classify Air Traffic Control (ATC) trajectory segments from recorded opportunity traffic. This method learns a fuzzy system using neural-network theory to determine its parameters (fuzzy sets and fuzzy rules) by processing data samples. The problem is prepared for analysing the Markovchain probabilities estimated by an Interacting Multiple Model (IMM) tracking filter operating forward and backward over available data. The performance of this data-driven classification system is compared with a more conventional approach based on transition detection on simulated and real data of representative situations. The problem's formulation for this application enabled an accurate classification of manoeuvring segments and the derivation of rules that explain the relation between input attributes and motion categories used to describe the recorded data.

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http://hdl.handle.net/10453/16255