Feature-weighted track-to-track association based on Adaptive Fuzzy C-Shell cluster

Zhemin Zhang; Chen Chen

doi:10.1109/ICICIP.2015.7388162

Feature-weighted track-to-track association based on Adaptive Fuzzy C-Shell cluster

Zhemin Zhang, Chen Chen

School of Automation

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

The traditional track-to-track association (track fusion) algorithm mostly focuses on single and straight tracks, while the tracks generated by maneuvering flight, like curves, have not been researched deeply. This paper reviews current techniques of track-to-track association and improves a method, based on Adaptive Fuzzy C-Shell cluster (AFCS), which can be used among those situations where target leaves curve-like tracks. This method collects data from distributed multi-sensors network to generate track features, then uses feature-weighted AFCS algorithm to achieve track fusion. The experiment shows the proposed approach performed well under certain circumstances.

Original language	English
Title of host publication	Proceedings of 6th International Conference on Intelligent Control and Information Processing, ICICIP 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	161-165
Number of pages	5
ISBN (Electronic)	9781479917174
DOIs	https://doi.org/10.1109/ICICIP.2015.7388162
Publication status	Published - 20 Jan 2016
Event	6th International Conference on Intelligent Control and Information Processing, ICICIP 2015 - Wuhan, Hubei, China Duration: 26 Nov 2015 → 28 Nov 2015

Publication series

Name	Proceedings of 6th International Conference on Intelligent Control and Information Processing, ICICIP 2015

Conference

Conference	6th International Conference on Intelligent Control and Information Processing, ICICIP 2015
Country/Territory	China
City	Wuhan, Hubei
Period	26/11/15 → 28/11/15

Keywords

data fusion
feature selection
fuzzy clustering
track-to-track association

Access to Document

10.1109/ICICIP.2015.7388162

Cite this

Zhang, Z., & Chen, C. (2016). Feature-weighted track-to-track association based on Adaptive Fuzzy C-Shell cluster. In Proceedings of 6th International Conference on Intelligent Control and Information Processing, ICICIP 2015 (pp. 161-165). Article 7388162 (Proceedings of 6th International Conference on Intelligent Control and Information Processing, ICICIP 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICICIP.2015.7388162

Zhang, Zhemin ; Chen, Chen. / Feature-weighted track-to-track association based on Adaptive Fuzzy C-Shell cluster. Proceedings of 6th International Conference on Intelligent Control and Information Processing, ICICIP 2015. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 161-165 (Proceedings of 6th International Conference on Intelligent Control and Information Processing, ICICIP 2015).

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title = "Feature-weighted track-to-track association based on Adaptive Fuzzy C-Shell cluster",

abstract = "The traditional track-to-track association (track fusion) algorithm mostly focuses on single and straight tracks, while the tracks generated by maneuvering flight, like curves, have not been researched deeply. This paper reviews current techniques of track-to-track association and improves a method, based on Adaptive Fuzzy C-Shell cluster (AFCS), which can be used among those situations where target leaves curve-like tracks. This method collects data from distributed multi-sensors network to generate track features, then uses feature-weighted AFCS algorithm to achieve track fusion. The experiment shows the proposed approach performed well under certain circumstances.",

keywords = "data fusion, feature selection, fuzzy clustering, track-to-track association",

author = "Zhemin Zhang and Chen Chen",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 6th International Conference on Intelligent Control and Information Processing, ICICIP 2015 ; Conference date: 26-11-2015 Through 28-11-2015",

year = "2016",

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doi = "10.1109/ICICIP.2015.7388162",

language = "English",

series = "Proceedings of 6th International Conference on Intelligent Control and Information Processing, ICICIP 2015",

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Zhang, Z & Chen, C 2016, Feature-weighted track-to-track association based on Adaptive Fuzzy C-Shell cluster. in Proceedings of 6th International Conference on Intelligent Control and Information Processing, ICICIP 2015., 7388162, Proceedings of 6th International Conference on Intelligent Control and Information Processing, ICICIP 2015, Institute of Electrical and Electronics Engineers Inc., pp. 161-165, 6th International Conference on Intelligent Control and Information Processing, ICICIP 2015, Wuhan, Hubei, China, 26/11/15. https://doi.org/10.1109/ICICIP.2015.7388162

Feature-weighted track-to-track association based on Adaptive Fuzzy C-Shell cluster. / Zhang, Zhemin; Chen, Chen.
Proceedings of 6th International Conference on Intelligent Control and Information Processing, ICICIP 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 161-165 7388162 (Proceedings of 6th International Conference on Intelligent Control and Information Processing, ICICIP 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - The traditional track-to-track association (track fusion) algorithm mostly focuses on single and straight tracks, while the tracks generated by maneuvering flight, like curves, have not been researched deeply. This paper reviews current techniques of track-to-track association and improves a method, based on Adaptive Fuzzy C-Shell cluster (AFCS), which can be used among those situations where target leaves curve-like tracks. This method collects data from distributed multi-sensors network to generate track features, then uses feature-weighted AFCS algorithm to achieve track fusion. The experiment shows the proposed approach performed well under certain circumstances.

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Zhang Z, Chen C. Feature-weighted track-to-track association based on Adaptive Fuzzy C-Shell cluster. In Proceedings of 6th International Conference on Intelligent Control and Information Processing, ICICIP 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 161-165. 7388162. (Proceedings of 6th International Conference on Intelligent Control and Information Processing, ICICIP 2015). doi: 10.1109/ICICIP.2015.7388162

Feature-weighted track-to-track association based on Adaptive Fuzzy C-Shell cluster

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