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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)

Abstract

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 \(95.0\%\).

Keywords

UAV Drones Classification Deep learning Surveillance radar 

Notes

Acknowledgments

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.

References

  1. 1.
    Knott, E.F., Schaeffer, J.F., Tulley, M.T.: Radar Cross Section. SciTech Publishing (2004)Google Scholar
  2. 2.
    Molchanov, P., Harmanny, R.I., de Wit, J.J., Egiazarian, K., Astola, J.: Classification of small UAVs and birds by micro-doppler signatures. Int. J. Microwave Wirel. Technol. 6(3–4), 435–444 (2014)CrossRefGoogle Scholar
  3. 3.
    Sullivan, R.: Radar Foundations for Imaging and Advanced Concepts. The Institution of Engineering and Technology (2004)Google Scholar
  4. 4.
    Tait, P.: Introduction to Radar Target Recognition. vol. 18. IET (2005)Google Scholar
  5. 5.
    Chen, V.C., Li, F., Ho, S.S., Wechsler, H.: Micro-doppler effect in radar: phenomenon, model, and simulation study. IEEE Trans. Aerosp. Electron. Syst. 42(1), 2–21 (2006)CrossRefGoogle Scholar
  6. 6.
    De Wit, J., Harmanny, R., Molchanov, P.: Radar micro-doppler feature extraction using the singular value decomposition. In: International Radar Conference 2014, pp. 1–6. IEEE (2014)Google Scholar
  7. 7.
    de Wit, J.M., Harmanny, R., Premel-Cabic, G.: Micro-doppler analysis of small UAVs. In: 9th European Radar Conference 2012, pp. 210–213. IEEE (2012)Google Scholar
  8. 8.
    Oh, B.S., Guo, X., Wan, F., Toh, K.A., Lin, Z.: Micro-Doppler mini-UAV classification using empirical-mode decomposition features. IEEE Geosci. Remote Sens. Lett. 15(2), 227–231 (2017)CrossRefGoogle Scholar
  9. 9.
    Harmanny, R., De Wit, J., Cabic, G.P.: Radar micro-Doppler feature extraction using the spectrogram and the cepstrogram. In: 11th European Radar Conference 2014, pp. 165–168. IEEE (2014)Google Scholar
  10. 10.
    Kim, B.K., Kang, H.S., Park, S.O.: Drone classification using convolutional neural networks with merged doppler images. IEEE Geosci. Remote Sens. Lett. 14(1), 38–42 (2016)CrossRefGoogle Scholar
  11. 11.
    Ghadaki, H., Dizaji, R.: Target track classification for airport surveillance radar (ASR). In: 2006 IEEE Conference on Radar, 4 pp. IEEE (2006)Google Scholar
  12. 12.
    Chen, W., Liu, J., Li, J.: Classification of UAV and bird target in low-altitude airspace with surveillance radar data. Aeronaut. J. 123(1260), 191–211 (2019)CrossRefGoogle Scholar
  13. 13.
    Mohajerin, N., Histon, J., Dizaji, R., Waslander, S.L.: Feature extraction and radar track classification for detecting UAVs in civillian airspace. In: IEEE Radar Conference 2014, pp. 0674–0679. IEEE (2014)Google Scholar
  14. 14.
    Namatēvs, I.: Deep convolutional neural networks: structure, feature extraction and training. Inf. Technol. Manag. Sci. 20(1), 40–47 (2017)Google Scholar
  15. 15.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)CrossRefGoogle Scholar
  16. 16.
    Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  17. 17.
    Mendis, G.J., Randeny, T., Wei, J., Madanayake, A.: Deep learning based doppler radar for micro UAS detection and classification. In: 2016 IEEE Military Communications Conference MILCOM 2016, pp. 924–929. IEEE (2016)Google Scholar
  18. 18.
    Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Dizaji, R.M., Ghadaki, H.: Classification system for radar and sonar applications. US Patent 7,567,203, July 2009Google Scholar
  20. 20.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  21. 21.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp. 91–99 (2015)Google Scholar

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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