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A Clustering-Based Anomaly Detection for Unstable Approach in Terminal Airspace

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Artificial Intelligence in China (AIC 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 871))

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Abstract

Data-driven methods are broadly used in analyzing big data in many fields. ADS-B trajectory data provided the possibility of anomaly detection in terminal airspace. Approaching and landing accidents are usually originated from unstable approaches, which are the consequence of go-around failure. Go-around is an aborted landing process initiated in the event of an unsafe landing. In this paper, DBSCAN and HDBSCAN clustering algorithms were employed to detect spatial and energy anomalies using ADS-B trajectory data. Air traffic flow patterns are identified, thus energy safety boundaries are established. Finally, atypical scores are quantified based on the total energy, which serves as the foundation for studying, comprehending, and addressing the factors that cause unstable approach events and go-arounds. This method detects the unstable approach from the spatial and energy perspectives and quantifies the degree of anomaly. It is essential for detecting and predicting anomalies in terminal airspace, and also explores the application of data-driven methods to aviation data.

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Acknowledgments

This work was supported by the Graduate Student Innovation Program-2021YJS019 of the Civil Aviation University of China. Thanks to the OpenSky Network, ADS-B data are freely available for research. The authors would also like to thank the members of the Tianjin Key Laboratory of Advanced Signal Processing for their support of this research.

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Authors and Affiliations

  1. College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, China

    Zhongrui Xu, Xiaoguang Lu, Zhe Zhang & Zhijie Wang

  2. Tianjin Key Laboratory, Civil Aviation University of China, Tianjin, 300300, China

    Zhongrui Xu, Xiaoguang Lu, Zhe Zhang & Zhijie Wang

Authors
  1. Zhongrui Xu
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  2. Xiaoguang Lu
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  3. Zhe Zhang
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  4. Zhijie Wang
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Corresponding author

Correspondence to Xiaoguang Lu .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. Tianjin Normal University, Tianjin, China

    Wei Wang

  3. Tianjin Normal University, Tianjin, China

    Jiasong Mu

  4. Dalian University of Technology, Dalian, China

    Xin Liu

  5. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Xu, Z., Lu, X., Zhang, Z., Wang, Z. (2023). A Clustering-Based Anomaly Detection for Unstable Approach in Terminal Airspace. In: Liang, Q., Wang, W., Mu, J., Liu, X., Na, Z. (eds) Artificial Intelligence in China. AIC 2022. Lecture Notes in Electrical Engineering, vol 871. Springer, Singapore. https://doi.org/10.1007/978-981-99-1256-8_32

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  • DOI: https://doi.org/10.1007/978-981-99-1256-8_32

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-1255-1

  • Online ISBN: 978-981-99-1256-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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