Neuro-fuzzy learning applied to improve the trajectory reconstruction problem

Ó Pérez*, J. García, J. M. Molina

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

3 Citations (Scopus)

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 neuralnetwork 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.

Original languageEnglish
Title of host publicationCIMCA 2006: International Conference on Computational Intelligence for Modelling, Control and Automation, Jointly with IAWTIC 2006: International Conference on Intelligent Agents Web Technologies ...
Place of PublicationSydney, NSW, Australia
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-6
Number of pages6
ISBN (Print)0769527310, 9780769527314
DOIs
Publication statusPublished - 2007
Externally publishedYes
EventCIMCA 2006: International Conference on Computational Intelligence for Modelling, Control and Automation, Jointly with IAWTIC 2006: International Conference on Intelligent Agents Web Technologies and International Commerce - Sydney, NSW, Australia
Duration: 28 Nov 20061 Dec 2006

Other

OtherCIMCA 2006: International Conference on Computational Intelligence for Modelling, Control and Automation, Jointly with IAWTIC 2006: International Conference on Intelligent Agents Web Technologies and International Commerce
CountryAustralia
CitySydney, NSW
Period28/11/061/12/06

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  • Cite this

    Pérez, Ó., García, J., & Molina, J. M. (2007). Neuro-fuzzy learning applied to improve the trajectory reconstruction problem. In CIMCA 2006: International Conference on Computational Intelligence for Modelling, Control and Automation, Jointly with IAWTIC 2006: International Conference on Intelligent Agents Web Technologies ... (pp. 1-6). [4052653] Sydney, NSW, Australia: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CIMCA.2006.157