Radar signatures of drones equipped with liquid spray payloads
MetadataShow full item record
The widespread availability of cheap and robust commercial drones has increased the likelihood of these being used for malicious purposes. In some cases they may be equipped with threat payloads. This study reports on the distinctive radar signatures of drones spraying liquid, analogous to a drone delivering chemical weapons, for example. A commercially available crop spraying drone has been used as the basis for liquid droplet radar backscatter modelling and for experimental data acquisition. The spray nozzle droplet parameters were used to model the radar cross section (RCS) and the signal-to-noise ratio (SNR) of the liquid droplets at X-, K- and W-bands, using the Rayleigh approximation. Additionally, experimental data have been obtained simultaneously with 24 GHz and 94 GHz radars. The processed results show that they are in very good agreement with the model. It is clearly demonstrated that at W-band (94 GHz), the liquid spray produces strong micro-Doppler signatures observed from the range-Doppler plots whereas no such detection was possible at K-band (24 GHz). The experimental results validate the hypothesis that millimeter-wave radar offers superior sensitivity than lower frequency bands to reflections from liquid spray droplets of <<0.5 mm size. Hence, a millimeter-wave radar system can potentially be used for classifying a drone with a liquid spray payload.
Rahman , S , Robertson , D A & Govoni , M 2020 , Radar signatures of drones equipped with liquid spray payloads . in 2020 IEEE Radar Conference (RadarConf20) . , 9266374 , Proceedings of the IEEE National Radar Conference , IEEE , 2020 IEEE Radar Conference (RadarConf) , Florence , Italy , 21/09/20 . https://doi.org/10.1109/RadarConf2043947.2020.9266374conference
2020 IEEE Radar Conference (RadarConf20)
Copyright © 2020 IEEE. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1109/RadarConf2043947.2020.9266374.
DescriptionFunding: Army Research Laboratory under Cooperative Agreement Number: W911NF-19-2-0075.
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.