UAV Classification with Deep Learning Using Surveillance Radar Data

  • Stamatios SamarasEmail author
  • Vasileios Magoulianitis
  • Anastasios Dimou
  • Dimitrios Zarpalas
  • Petros Daras
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11754)


The Unmanned Aerial Vehicle (UAV) proliferation has raised many concerns, since their potentially malicious usage renders them as a detrimental tool for a number of illegal activities. Radar based counter-UAV applications provide a robust solution for UAV detection and classification. Most of the existing research addresses the problem of UAV classification by extracting features from the time variations of the Fourier spectra. Yet, these solutions require that the UAV is illuminated by the radar for a longer time which can be only met by a tracking radar architecture. On the other hand, surveillance radar architectures don’t have such a cumbersome requirement and are generally superior in maintaining situational awareness, due their ability for constantly searching on a 360\(^{\circ }\) area for targets. Nevertheless, the available automatic UAV classification methods for this type of radar sensors are relatively inefficient. This work proposes the incorporation of the deep learning paradigm in the classification pipeline, to provide an alternative UAV classification method that can handle data from a surveillance radar. Therefore, a Deep Neural Network (DNN) model is employed to discern between UAVs and negative examples (e.g. birds, noise, etc.). The conducted experiments demonstrate the validity of the proposed method, where the overall classification accuracy can reach up to \(95.0\%\).


UAV Drones Classification Deep learning Surveillance radar 



Special thanks to IDS Ingegneria Dei Sistemi S.p.A. for providing their radar sensor, the signal processing knowledge and the assistance in the dataset creation.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Stamatios Samaras
    • 1
    Email author
  • Vasileios Magoulianitis
    • 1
  • Anastasios Dimou
    • 1
  • Dimitrios Zarpalas
    • 1
  • Petros Daras
    • 1
  1. 1.Information Technologies Institute, Centre for Research and Technology HellasThessalonikiGreece

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