Genetic algorithm and support vector machine based aircraft intent inference algorithm in terminal area

Yang Yang; Jun Zhang; Xian Bin Cao; Kai Quan Cai

doi:10.1109/DASC.2012.6382314

Genetic algorithm and support vector machine based aircraft intent inference algorithm in terminal area

Yang Yang^*, Jun Zhang, Xian Bin Cao, Kai Quan Cai

^*Corresponding author for this work

Beihang University

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

1 Citation (Scopus)

Abstract

AII aims to infer the most likely future intent based on current aircraft motion states, therefore, it has become an essential method to enhance air traffic situational awareness [1]. Generally, aircraft motion states consist of aircraft IDs, latitude/longitude/altitude coordinates, ground speeds, accelerations and heading angles, which could be directly gained from the surveillance infrastructures like Radars and Automatic Dependent Surveillance-Broadcast (ADS-B) systems. Given current aircraft motion states, one important issue in gaining future air traffic situation prediction is to infer aircraft intent. This is significant because AII plays a fundamental role in conflict detection and avoidance, which hence determines the operational safety of air transportation system.

Original language	English
Title of host publication	31st Digital Avionics Systems Conference
Subtitle of host publication	Projecting 100 Years of Aerospace History into the Future of Avionics, DASC 2012
Pages	3D11-3D17
DOIs	https://doi.org/10.1109/DASC.2012.6382314
Publication status	Published - 2012
Externally published	Yes
Event	31st Digital Avionics Systems Conference: Projecting 100 Years of Aerospace History into the Future of Avionics, DASC 2012 - Williamsburg, VA, United States Duration: 14 Oct 2012 → 18 Oct 2012

Publication series

Name	AIAA/IEEE Digital Avionics Systems Conference - Proceedings
ISSN (Print)	2155-7195
ISSN (Electronic)	2155-7209

Conference

Conference	31st Digital Avionics Systems Conference: Projecting 100 Years of Aerospace History into the Future of Avionics, DASC 2012
Country/Territory	United States
City	Williamsburg, VA
Period	14/10/12 → 18/10/12

Access to Document

10.1109/DASC.2012.6382314

Cite this

Yang, Y., Zhang, J., Cao, X. B., & Cai, K. Q. (2012). Genetic algorithm and support vector machine based aircraft intent inference algorithm in terminal area. In 31st Digital Avionics Systems Conference: Projecting 100 Years of Aerospace History into the Future of Avionics, DASC 2012 (pp. 3D11-3D17). Article 6382314 (AIAA/IEEE Digital Avionics Systems Conference - Proceedings). https://doi.org/10.1109/DASC.2012.6382314

@inproceedings{aad83dd2f808402a99cd6590678c6ca1,

title = "Genetic algorithm and support vector machine based aircraft intent inference algorithm in terminal area",

abstract = "AII aims to infer the most likely future intent based on current aircraft motion states, therefore, it has become an essential method to enhance air traffic situational awareness [1]. Generally, aircraft motion states consist of aircraft IDs, latitude/longitude/altitude coordinates, ground speeds, accelerations and heading angles, which could be directly gained from the surveillance infrastructures like Radars and Automatic Dependent Surveillance-Broadcast (ADS-B) systems. Given current aircraft motion states, one important issue in gaining future air traffic situation prediction is to infer aircraft intent. This is significant because AII plays a fundamental role in conflict detection and avoidance, which hence determines the operational safety of air transportation system.",

author = "Yang Yang and Jun Zhang and Cao, {Xian Bin} and Cai, {Kai Quan}",

year = "2012",

doi = "10.1109/DASC.2012.6382314",

language = "English",

isbn = "9781467316996",

series = "AIAA/IEEE Digital Avionics Systems Conference - Proceedings",

pages = "3D11--3D17",

booktitle = "31st Digital Avionics Systems Conference",

note = "31st Digital Avionics Systems Conference: Projecting 100 Years of Aerospace History into the Future of Avionics, DASC 2012 ; Conference date: 14-10-2012 Through 18-10-2012",

}

Yang, Y, Zhang, J, Cao, XB & Cai, KQ 2012, Genetic algorithm and support vector machine based aircraft intent inference algorithm in terminal area. in 31st Digital Avionics Systems Conference: Projecting 100 Years of Aerospace History into the Future of Avionics, DASC 2012., 6382314, AIAA/IEEE Digital Avionics Systems Conference - Proceedings, pp. 3D11-3D17, 31st Digital Avionics Systems Conference: Projecting 100 Years of Aerospace History into the Future of Avionics, DASC 2012, Williamsburg, VA, United States, 14/10/12. https://doi.org/10.1109/DASC.2012.6382314

Genetic algorithm and support vector machine based aircraft intent inference algorithm in terminal area. / Yang, Yang; Zhang, Jun; Cao, Xian Bin et al.
31st Digital Avionics Systems Conference: Projecting 100 Years of Aerospace History into the Future of Avionics, DASC 2012. 2012. p. 3D11-3D17 6382314 (AIAA/IEEE Digital Avionics Systems Conference - Proceedings).

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

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N2 - AII aims to infer the most likely future intent based on current aircraft motion states, therefore, it has become an essential method to enhance air traffic situational awareness [1]. Generally, aircraft motion states consist of aircraft IDs, latitude/longitude/altitude coordinates, ground speeds, accelerations and heading angles, which could be directly gained from the surveillance infrastructures like Radars and Automatic Dependent Surveillance-Broadcast (ADS-B) systems. Given current aircraft motion states, one important issue in gaining future air traffic situation prediction is to infer aircraft intent. This is significant because AII plays a fundamental role in conflict detection and avoidance, which hence determines the operational safety of air transportation system.

AB - AII aims to infer the most likely future intent based on current aircraft motion states, therefore, it has become an essential method to enhance air traffic situational awareness [1]. Generally, aircraft motion states consist of aircraft IDs, latitude/longitude/altitude coordinates, ground speeds, accelerations and heading angles, which could be directly gained from the surveillance infrastructures like Radars and Automatic Dependent Surveillance-Broadcast (ADS-B) systems. Given current aircraft motion states, one important issue in gaining future air traffic situation prediction is to infer aircraft intent. This is significant because AII plays a fundamental role in conflict detection and avoidance, which hence determines the operational safety of air transportation system.

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M3 - Conference contribution

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T3 - AIAA/IEEE Digital Avionics Systems Conference - Proceedings

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Yang Y, Zhang J, Cao XB, Cai KQ. Genetic algorithm and support vector machine based aircraft intent inference algorithm in terminal area. In 31st Digital Avionics Systems Conference: Projecting 100 Years of Aerospace History into the Future of Avionics, DASC 2012. 2012. p. 3D11-3D17. 6382314. (AIAA/IEEE Digital Avionics Systems Conference - Proceedings). doi: 10.1109/DASC.2012.6382314

Genetic algorithm and support vector machine based aircraft intent inference algorithm in terminal area

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