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Real-time Classification of Aircrafts Manoeuvers

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Abstract

Whether it be in air defense applications or for air traffic control it is highly desirable to be able to assess in real time the type of aircraft one is dealing with. This task may prove useful when the object refuses to cooperate or to confront the transmitted information with the observed trajectory. In the present paper we advocate an approach based on position (radar) measurements only, for versatility. Besides, we propose to focus on rotation-invariant kinematic trajectory features such as absolute velocity, curvature, torsion and parameters alike, as relevant distinctive features to automatically classify aircraft trajectories in real time. Those features are fed into convolutional neural networks that are state of the art for time series classification. Notably they seamlessly handle trajectories of variable length and hence may be used in real time. The constructed classifiers are trained with real data collected from publicly available information transmitted by the aircrafts. This allows for benchmarking of the proposed learning algorithms, as well as discussion on the best possible achievable accuracy.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank THALES LAS and especially Frederic Barbaresco and Philippe Chopin, but also the Mathematics Foundation Jacques Hadamard (PGMO project) for their involvement in this project.

Funding

The research leading to these results received funding from Mathematics Foundation Jacques Hadamard (PGMO project) and the French Directorate General of Armaments (DGA).

Author information

Authors and Affiliations

  1. Center for Robotics (CAOR), Mines Paris, PSL University, Paris, France

    Jouaber Sami & Bonnabel Silvère

  2. LAS, THALES, Massy, France

    Jouaber Sami & Pilté Marion

  3. ISEA, UNC, Nouméa, France

    Bonnabel Silvère

  4. Centre de Morphologie Mathématique (CMM), Mines Paris, PSL University, Fontainebleau, France

    Velasco-Forero Santiago & Angulo Jesus

Authors
  1. Jouaber Sami
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  2. Bonnabel Silvère
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  3. Velasco-Forero Santiago
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  4. Pilté Marion
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  5. Angulo Jesus
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Contributions

Conceptualization, S.J., S.V-F, S.B. and M.P.; Data curation, S.J.; Funding acquisition, S.V-F, S.B. and J.A; Methodology, S.J., S.B., S.V-F., and J.A.; Writing - original draft preparation, S.J.; Writing - review and editing, S.J., S.V-F. and S.B.; Resources, S.V-F., M.P.; Supervision, S.V-F., M.P., S.B.; Visualization, S.J.; All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Jouaber Sami.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Sami, J., Silvère, B., Santiago, VF. et al. Real-time Classification of Aircrafts Manoeuvers. J Sign Process Syst 95, 425–434 (2023). https://doi.org/10.1007/s11265-022-01823-x

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  • DOI: https://doi.org/10.1007/s11265-022-01823-x

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