A comparison of convolutional neural networks for low SNR radar classification of drones

Holly Dale, Chris Baker, Michail Antoniou, Mohammed Jahangir, George Atkinson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Reliable detection and tracking is required to ensure that drones are safely integrated into low altitude airspace. Radar provides a 24-hour, all-weather solution to this problem. However, the radar signatures of birds have a similar RCS to those of drones, thus a robust method of classification is needed to filter out non-drone targets and eliminate, or at least minimize to an acceptable level, false alarms. Convolutional neural networks (CNNs) have been shown to achieve high classification performance but results are only reported for high signal to noise ratio data-a luxury that is not always available to operational radar systems. In this paper, Gaussian noise is added to the test data to vary the signal to noise ratio (SNR) in order to investigate classifier robustness as a function of SNR in the context of drone classification. The performance of six CNN architectures previously established for computer vision applications are exploited and compared with each other to assess classification performance and robustness with network depth.

Original languageEnglish
Title of host publication2021 IEEE Radar Conference (RadarConf21)
Subtitle of host publicationRadar on the Move
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)9781728176093, 9781728176109 (PoD)
Publication statusPublished - 18 Jun 2021
Event2021 IEEE Radar Conference, RadarConf 2021 - Atlanta, United States
Duration: 8 May 202114 May 2021

Publication series

NameIEEE National Radar Conference - Proceedings
ISSN (Print)1097-5659


Conference2021 IEEE Radar Conference, RadarConf 2021
Country/TerritoryUnited States

Bibliographical note

Funding Information:
The authors are extremely grateful to Aveillant, a Thales company, for provision of the radar data used in this study. This work was funded by the UK Engineering and Physical Sciences Research Council.


  • birds
  • classification
  • deep learning
  • staring radar
  • UAVs

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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