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UAV Airframe Classification Using Acceleration Spectrograms

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18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) (SOCO 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 750))

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Abstract

Unmanned Aerial Vehicles (UAVs) are a highly innovative technology that is subject to strict regulations due to their potentially hazardous characteristics and a lack of legislative framework for their safe operation. To overcome these challenges, the Unmanned Air System Traffic Management (UTM) initiatives aim to establish validation and monitoring techniques for drone trajectories both prior to and during flight. In the UTM framework, drones will collaborate through systems similar to those used for ship and aircraft vehicles, such as Automatic Identification System (AIS) and Automatic Dependent Surveillance-Broadcast (ADSB). This paper presents an approach in the use of machine learning to gain insights into their kinematic behavior of UAV with the objective of detecting the drone airframe and classifying drones according to their characteristics.

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Acknowledgements

This work was funded by CDTI (Centro para el Desarrollo Tecnológico Industrial E.P.E.), CNU/1308/2018, 28 November; the public research projects of the Spanish Ministry of Science and Innovation PID2020-118249RB-C22 and PDC2021-121567-C22 - AEI/10.13039/501100011033; the project under the call PEICTI 2021-2023 with identifier TED2021-131520B-C22; and the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M17) and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation).

Author information

Authors and Affiliations

  1. Group GIAA, University Carlos III of Madrid, Madrid, Spain

    David Sánchez Pedroche, Daniel Amigo Herrero, Jesús García & José M. Molina

  2. University Carlos III of Madrid, Madrid, Spain

    Francisco Fariña Salguero

Authors
  1. David Sánchez Pedroche
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  2. Francisco Fariña Salguero
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  3. Daniel Amigo Herrero
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  4. Jesús García
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  5. José M. Molina
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Corresponding author

Correspondence to David Sánchez Pedroche .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. School of Industrial, Computer, University of Leon, León, Spain

    Hilde Pérez García

  3. Department of Mechanical Engineering, University of La Rioja, Logroño, Spain

    Francisco Javier Martínez de Pisón

  4. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  5. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  6. Applied Computational Intelligence, University of Burgos, Burgos, Spain

    Álvaro Herrero

  7. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    José Luis Calvo Rolle

  8. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    Héctor Quintián

  9. Faculty of Science, University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Pedroche, D.S., Salguero, F.F., Herrero, D.A., García, J., Molina, J.M. (2023). UAV Airframe Classification Using Acceleration Spectrograms. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 750. Springer, Cham. https://doi.org/10.1007/978-3-031-42536-3_4

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