Study of radar signatures of drones equipped with threat payloads

Samiur Rahman; Duncan A. Robertson; Adam M. Robertson; Mark A. Govoni

doi:10.14339/STO-MP-MSG-SET-183

Study of radar signatures of drones equipped with threat payloads

Samiur Rahman, Duncan A. Robertson^*, Adam M. Robertson^*, Mark A. Govoni^*

^*Corresponding author for this work

School of Physics and Astronomy

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Commercial or customised drones with the ability to carry payloads have the potential to cause security threats so the need to accurately detect and identify them with suitable sensors has increased in recent times. Radar sensors are well capable of detecting and classifying a drone by using the unique signatures produced from both the stationary and rotating parts of the target. In this study we have examined the radar signatures of drones carrying different types of payloads which simulate the following three hazardous scenarios: 1) liquid spray, 2) Inertial forces simulating a gun recoil effect, and 3) heavy payloads. The main objective was to model the radar signatures of these scenarios and analyse the characteristic signatures. Two radars, operating at 24 GHz and 94 GHz, have been used to collect data to validate the modelling. The results of the study demonstrate that the payloads produce unique radar return signals, mainly in the Doppler domain, which can be used for robust classification.

Original language	English
Title of host publication	Meetings Proceedings RDP Drone Detectability
Subtitle of host publication	Modelling the Relevant Signature
Publisher	NATO Science and Technology Organization
Number of pages	16
ISBN (Electronic)	9789283723578
DOIs	https://doi.org/10.14339/STO-MP-MSG-SET-183
Publication status	Published - 7 Jul 2021
Event	NATO Meeting Drone Detectability: Modelling the Relevant Signature - Duration: 27 Apr 2021 → … Conference number: MSG-SET-183

Conference

Conference	NATO Meeting Drone Detectability: Modelling the Relevant Signature
Period	27/04/21 → …

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10.14339/STO-MP-MSG-SET-183

MP-MSG-SET-183-05
Copyright © 2021 the Author(s). 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 final published version of the work, which was originally published at https://doi.org/10.14339/STO-MP-MSG-SET-183.
Final published version, 1.05 MBLicence: Other

Cite this

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title = "Study of radar signatures of drones equipped with threat payloads",

abstract = "Commercial or customised drones with the ability to carry payloads have the potential to cause security threats so the need to accurately detect and identify them with suitable sensors has increased in recent times. Radar sensors are well capable of detecting and classifying a drone by using the unique signatures produced from both the stationary and rotating parts of the target. In this study we have examined the radar signatures of drones carrying different types of payloads which simulate the following three hazardous scenarios: 1) liquid spray, 2) Inertial forces simulating a gun recoil effect, and 3) heavy payloads. The main objective was to model the radar signatures of these scenarios and analyse the characteristic signatures. Two radars, operating at 24 GHz and 94 GHz, have been used to collect data to validate the modelling. The results of the study demonstrate that the payloads produce unique radar return signals, mainly in the Doppler domain, which can be used for robust classification.",

author = "Samiur Rahman and Robertson, {Duncan A.} and Robertson, {Adam M.} and Govoni, {Mark A.}",

note = "The authors acknowledge the funding received by the Army Research Laboratory under Cooperative Agreement Number: W911NF-19-2-0075.; NATO Meeting Drone Detectability: Modelling the Relevant Signature ; Conference date: 27-04-2021",

year = "2021",

month = jul,

day = "7",

doi = "10.14339/STO-MP-MSG-SET-183",

language = "English",

booktitle = "Meetings Proceedings RDP Drone Detectability",

publisher = "NATO Science and Technology Organization",

}

Rahman, S, Robertson, DA, Robertson, AM & Govoni, MA 2021, Study of radar signatures of drones equipped with threat payloads. in Meetings Proceedings RDP Drone Detectability: Modelling the Relevant Signature., MP-MSG-SET-183-05, NATO Science and Technology Organization, NATO Meeting Drone Detectability: Modelling the Relevant Signature, 27/04/21. https://doi.org/10.14339/STO-MP-MSG-SET-183

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AU - Robertson, Duncan A.

AU - Robertson, Adam M.

AU - Govoni, Mark A.

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PY - 2021/7/7

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N2 - Commercial or customised drones with the ability to carry payloads have the potential to cause security threats so the need to accurately detect and identify them with suitable sensors has increased in recent times. Radar sensors are well capable of detecting and classifying a drone by using the unique signatures produced from both the stationary and rotating parts of the target. In this study we have examined the radar signatures of drones carrying different types of payloads which simulate the following three hazardous scenarios: 1) liquid spray, 2) Inertial forces simulating a gun recoil effect, and 3) heavy payloads. The main objective was to model the radar signatures of these scenarios and analyse the characteristic signatures. Two radars, operating at 24 GHz and 94 GHz, have been used to collect data to validate the modelling. The results of the study demonstrate that the payloads produce unique radar return signals, mainly in the Doppler domain, which can be used for robust classification.

AB - Commercial or customised drones with the ability to carry payloads have the potential to cause security threats so the need to accurately detect and identify them with suitable sensors has increased in recent times. Radar sensors are well capable of detecting and classifying a drone by using the unique signatures produced from both the stationary and rotating parts of the target. In this study we have examined the radar signatures of drones carrying different types of payloads which simulate the following three hazardous scenarios: 1) liquid spray, 2) Inertial forces simulating a gun recoil effect, and 3) heavy payloads. The main objective was to model the radar signatures of these scenarios and analyse the characteristic signatures. Two radars, operating at 24 GHz and 94 GHz, have been used to collect data to validate the modelling. The results of the study demonstrate that the payloads produce unique radar return signals, mainly in the Doppler domain, which can be used for robust classification.

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ER -

Study of radar signatures of drones equipped with threat payloads

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