A Review of Automatic Classification of Drones Using Radar

A Review of Automatic Classification of Drones Using Radar: Key Considerations, Performance Evaluation and Prospects

Ahmad, Bashar I. (University of Cambridge; Thales Land and Air Systems)
Rogers, Colin (Thales Land and Air Systems)
Harman, Stephen (Thales UK)
Dale, Holly (University of Birmingham)
Jahangir, Mohammed (University of Birmingham)
Antoniou, Michael (University of Birmingham)
Baker, Chris (University of Birmingham)
Newman, Mike (Thales UK)
Fioranelli, F. (TU Delft Microwave Sensing, Signals & Systems)

2024

Automatic target classification or recognition is a critical capability in noncooperative surveillance with radar in several defence and civilian applications. It is a well-established research field and numerous techniques exist for recognizing targets, including miniature unmanned air systems or drones (i.e., small, mini, micro, and nano platforms), from their radar signatures. These algorithms 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 article, we first review the key challenges and considerations associated with the automatic classification of miniature drones from radar data. We then present a set of important performance measures, from an end-user perspective. These are relevant to typical drone surveillance system requirements and constraints. Selected examples from real radar observations are shown for illustration. We also outline here various emerging approaches and future directions that can produce more robust drone classifiers for radar.

Airborne radar
classification
deep learning
Drones
non-cooperative surveillance
radar
Radar
Radar cross-sections
Radar tracking
Surveillance
Target tracking
unmanned air traffic management

http://resolver.tudelft.nl/uuid:756aa692-d9be-4634-b9b0-3710442dd770

https://doi.org/10.1109/MAES.2023.3335003

2024-08-27

0885-8985

IEEE Aerospace and Electronic Systems Magazine, 39 (2), 18-33

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2024 Bashar I. Ahmad, Colin Rogers, Stephen Harman, Holly Dale, Mohammed Jahangir, Michael Antoniou, Chris Baker, Mike Newman, F. Fioranelli