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Automatic Classification of Drones Using Radar: Key Considerations and Performance Evaluation
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  • Bashar Ahmad ,
  • Colin Rogers ,
  • Stephen Harman ,
  • Holly Dale ,
  • Mohammed Jahangir ,
  • Michael Antoniou ,
  • Chris Baker ,
  • Mike Newman ,
  • Francesco Fioranelli
Bashar Ahmad
University of Cambridge, Thales and University of Cambridge

Corresponding Author:[email protected]

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Colin Rogers
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Stephen Harman
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Holly Dale
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Mohammed Jahangir
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Michael Antoniou
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Chris Baker
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Mike Newman
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Francesco Fioranelli
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Automatic target classification is a critical capability for non-cooperative drone surveillance radars in several defence and civilian applications. It is accordingly a well-established research field and numerous algorithms exist for recognising targets, including miniature unmanned air systems (i.e., small, mini, micro and nano platforms), from their radar signatures. They have notably benefited from advances in machine learning (e.g., deep neural networks) and are increasingly able to achieve remarkably high accuracies. Such classification results are often captured by standard, generic, object recognition metrics and originate from testing on simulated or real radar measurements of drones under high signal to noise ratios. Hence, it is difficult to assess and benchmark the performance of different classifiers under realistic operational conditions. In this paper, we first outline the key challenges and considerations associated with the automatic classification of miniature drones from radar data. We then present a set of important performance metrics, from an end-user perspective. They are relevant to typical drone surveillance system requirements and constraints. Selected examples from real radar observations are shown for illustrations. We also outline here various emerging approaches and future directions that can produce more robust drone classifiers for radar.
2023Published in IEEE Aerospace and Electronic Systems Magazine on pages 1-12. 10.1109/MAES.2023.3335003