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Research on Resampling and Clustering Method of Aircraft Flight Trajectory

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Abstract

In order to efficiently mine the flight trajectory information in the terminal area and grasp the spatial distribution characteristics of the approaching traffic flow in the terminal area, this paper develops a flight trajectory data attribute correlation analysis model, and establishes a flight trajectory feature extraction model based on the data attribute correlation and improved k-means. Using the t-distributed stochastic neighbor embedding (t-SNE) method and density peaks clustering approach (DPCA), the prevailing traffic flow is then extracted. Finally, the real flight trajectory data in the terminal area are verified by analyzing the model parameters. The findings indicate that 788 flight trajectories are compressed to 100 trajectory points, divided into seven clusters, and seven prevailing traffic flows are extracted. Compared with the traditional flight trajectory clustering method, the accuracy of the method is improved by compressing the flight trajectory data scale and reducing the dimensionality. Simultaneously, the application of the DPCA method achieves a more detailed recognition of the flight trajectory.

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Acknowledgements

This work was supported by the Fundamental Research Funds for the Central Universities and CAUC special fund under Grant 3122019129,and the National Key Research and Development Program of China (2020YFB1600101).

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Authors and Affiliations

  1. School of Navigation & Information, Air Force Engineering University, Xi’an, China

    Zhi-sen Wang

  2. College of Air Traffic Management, Civil Aviation University of China, Tianjin, 300300, China

    Zhi-sen Wang, Zhao-yue Zhang & Zhe Cui

  3. School of Aeronautics, Northwestern Polytechnical University, Xi’an Shaanxi, 710129, China

    Zhao-yue Zhang

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  1. Zhi-sen Wang
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  3. Zhe Cui
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Correspondence to Zhi-sen Wang.

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Wang, Zs., Zhang, Zy. & Cui, Z. Research on Resampling and Clustering Method of Aircraft Flight Trajectory. J Sign Process Syst 95, 319–331 (2023). https://doi.org/10.1007/s11265-022-01809-9

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  • DOI: https://doi.org/10.1007/s11265-022-01809-9

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