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A concise overview | Gerald Poppinga, October 3rd 2019
C-UAS Detection, Tracking and Intent
Drone on the Rise as a ThreatCivil Military
EUROCONTROL "Drones’ incursions at the airports" workshop - © NLR 2019 All Rights Reserved 2The Guardian, 14 sept 2019
BBC News,27 Sept 2019
Spiegel Online, 16 Sept 2013
Reuters, 3 July2018
Al Jazeera, 10 Jan 2019
CNBC, 11 Jan 2018
CBS news, 21 Feb 2017
DefenseOne, 2 July 2019
Washington Post, 23 Sept. 2016
USA today, 7 Aug 2018New York Times, April 13 2017
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Counter-UAS Program in the Netherlands
Strategic Cooperation
The Counter-UAS Challenge
Counter measures against Unmanned Aircraft Systems (UAS) are often technologically immature, scarce and expensive. Therefore research and development is required for effective and affordable counter measures for both civil and military use.
Countermeasures should consist of layered capabilities for rapid (real-time) detection, tracking, classification, identification, and neutralization of hostile UAS with minimal collateral damage.
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C-UAS Activities taking place
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Dilemma sessions, Table Top eXcersises
OT&E’s
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Agenda for the remainder of this Presentation
• The C-UAS protection model• Detection, Tracking and Intent• Additional considerations• Conclusion
The C-UAS protection model
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EUROCONTROL "Drones’ incursions at the airports" workshop - © NLR 2019 All Rights Reserved 8
The Netherlands’ Counter UAS Protection Model
1 • Prevention
2 • Detection/Tracking
3 • Classification/Identification/Intent
4 • Decision making
5 • Neutralization
6 • Forensics
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Prevention
1 • Prevention
2 • Detection/Tracking
3 • Classification/Identification/Intent
4 • Decision making
5 • Neutralization
6 • Forensics
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Detection, Tracking, Classification, Identification, Intent
1 • Prevention
2 • Detection/Tracking
3 • Classification/Identification/Intent
4 • Decision making
5 • Neutralization
6 • Forensics
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Decision Making
Options: • Real time decision support• Operator support• Interoperability• Common operational picture• Rules of engagement
1 • Prevention
2 • Detection/Tracking
3 • Classification/Identification/Intent
4 • Decision making
5 • Neutralization
6 • Forensics
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Neutralization
Options: • Jamming/Spoofing• Kinetic
• Capture• Collision• Destruction
• Directed Energy• High Power Microwave• ElectroMagnetic Pulse• Laser
1 • Prevention
2 • Detection/Tracking
3 • Classification/Identification/Intent
4 • Decision making
5 • Neutralization
6 • Forensics
Photo courtesy of TNO
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Forensics
Aspects: • Content / Log file analysis• Mode of operation research• Decryption• ...
DNA Analysis, DoD photo by Fred W. Baker III
1 • Prevention
2 • Detection/Tracking
3 • Classification/Identification/Intent
4 • Decision making
5 • Neutralization
6 • Forensics
Detection, Tracking and Intent
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“Drone” is a collective name
Many additional differentiating factors :- type: Helicopter, Multi-rotor, Fixed
Wing,...- Materiel: plastic, metal, wood, ...- Propulsion: None (glider), Electric
Engine, Piston Engine, Turbine,...- Control/Datalink: None, ISM specific
band, 3G, 4G, 5G, other... - Navigation: None, Magnetic compass,
Camera image processing, INS, GPS, ..
- Numbers: single, multiple, swarm,..EUROCONTROL "Drones’ incursions at the airports" workshop - © NLR 2019 All Rights Reserved 15
EUROCONTROL "Drones’ incursions at the airports" workshop - © NLR 2019 All Rights Reserved
Detect and Track Modalities
Various Detection/Tracking modalities:• Human observers• Acoustics• Electro Optics• ESM• Radar• Laser• ...
Many aspects influence the detect- & track-ability per modalities for each different type of drone
OnyxStar HYDRA-12 by Cargyrak, used under CC BY-SA 4.0, adapted version.
Dr.One VTOL drone
Lammergier Fixed Wing
1616
Detection & Tracking – Human Observers
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Britain's Home Front 1939 - 1945- Roof Spotters An acoustic aircraft detection apparatus in the USA in 1921
Depends on many aspects:• UAV: Size, Shape, Colors,
Sound,...• Observer: Eyesight, Hearing,
Search technique, ...• Conditions: Weather, Time of
Day, Time of Year, ...Training might help to improve detection qualities
ROTARY WING FIXED WINGNano ( < 0.5 kg) 100 100Micro (0.5 - 2 kg) 200 500Mini (2 - 20 kg) 300 1000Small (20 - 150 kg) 800 1200Notional maximal visual detection ranges (m)** Based on a literature study, assessments and experience, for a generic
“representative” type of drone under “normal” conditions.
Parrot Anafi(320 gr.) (100 m)foto by KlausFoehl, used under CC BY-SA 4.0, clipped version.
Yuneec Typhoon H(1.7 Kg) (200 m)foto by SkylarkCoder, used under CC BY-SA 4.0, clipped version.
DJI M600pro (MTOW 15.5 kg) (300m))foto by Mc clapurhands, used under CC BY-SA 4.0, clipped version.
1
2
3
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Human Observers – Notional Visual Detection Ranges
The notional maximal visual detection ranges visualized in Google Earth
Example systems
Detection & Tracking – Acoustics, E/O and ESM
Acoustic• Single/multiple microphone(s)• Acoustic radarElectro Optics (E/O)• IR • Visual Range• UVElectromagnetic Spectrum Monitoring• specific bandS (Wi-Fi, RC, ...)• 3G, 4G, 5G• other frequencies
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Acoustics Electro Optics ESMNano ( < 0.5 kg) 150 100 1500Micro (0.5 - 2 kg) 250 250 1500Mini (2 - 20 kg) 350 400 1500Small (20 - 150 kg) 500 1000 1500Notional maximal detection ranges (m)*
Depending on many conditions, e.g. weather, time of day/year, EMS noise levels, transmission power, # of transmitters
* Based on a literature study, assessments and experience, for a generic “representative” type of drone under “normal” conditions.
Notional Detection Ranges For A Micro Drone
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Example micro drone system
DJI Mavic Pro(~750 gr) foto by thoroughlyreviewed.com, used under CC BY 2.0, clipped version.
1. Acoustics& E/O (250 m)2. ESM(1500 m)*, **
* If the signal is know, the maximal detection range can be much larger** if the drone flies autonomously, there might not be a RF signature
1
2
Detection & Tracking – Radar and Laser
• Active Radar– Pulse Doppler– Continuous-Wave– Active Phased Array– MIMO Phased Array or UWB– Other
• Passive Radar• Laser Enabled
– LIDAR/LADAR
Active RadarROTARY
WINGActive RadarFIXED WING Laser Enabled
Nano ( < 0.5 kg) 3000 6000 300Micro (0.5 - 2 kg) 3000 6000 300Mini (2 - 20 kg) 3000 6000 300Small (20 - 150 kg) 10000 10000 2000
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Some remarks:• Radar signatures of drones can be very small
and are therefore often ignored (like birds) • A secondary radar requires a drone to have a
transponder in order for the radar to be able to detect the drone
Notional maximal detection ranges (m)** Based on a literature study, assessments and experience, for a generic “representative” type of drone under “normal” conditions.
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The Detect and Track Optimization Puzzle
Many aspects influence the detectability/track-ability • Drone type, shape, materials, ...• Detection/tracking modalities• Landscape & Buildings• Weather conditions• ...
Goal: to maximally mitigate drone related risks for an airport with respect to flight safety and economical concerns, with available and affordable means, within the operational constraints.
+
E.g. E/O: Various orientations, weather conditions, camera blurs, etc.
DNN’s are a Black box• Generalization• Impressive results on test sets• Results on real life data may vary• Potentially susceptible to Adverserial
Attacks
Classification/Identification
Sensor Fusion Deep Neural Networks (DNNs)
Requires large data/training sets: images, sound (profiles), radar (Doppler) signatures, ESM characteristics, etc.
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Visual
Infrared
Supervised Learning
Classifier
Intent
Correlate specific characteristics with other data (e.g. Classification/Identification data) and detect Discrepancies.
Options: • Object/payload recognition• Flight Path Analysis• ...
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From: Drone classification by payload-speed analysis, from: Conceptual study of an Anti-Drone Drone through the coupling of design process and interception strategy Simulations, Onera 2016
Fox News, January 25, 2017
Additional Considerations and Conclusion
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No Silver Bullet - Consideration
Many different partial solutions, cherry picking might speed things up: • Sensor X of Vendor C• Sensor Y of Vendor D• Intervention Solution P of Vendor H• Intervention Solution Q of Vendor I
Required: a Common (Open Source) Backbone Inspired by the Robot Operating System (ROS): “a collection of tools, libraries, and conventions that aim to simplify the task of creating complex and robust C-UAS behavior across a wide variety of C-UAS elements”
10 oz silver bullet by Money Metals,Used under CC-BY-2.0, cropped version
The Dutch C-UAS Nucleus’ view on C-UAS
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Test (operationally) before you buy
Test for some of the operationally highly relevant aspects, e.g.:• Detection probability
– False Negatives– False Positives
Challenge: - Simulation of representative
malicious behavior requires support of the responsible CAAand National Telecom Authority
- Realistic testing could result in collateral damage
There’s more - DOTMLPFI-P
• Doctrine• Organization• Training• Material• Leadership and Education• Personnel• Facilities• Interoperability• Policy
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Tip of the iceberg by Uwe KilsUsed under CC BY-SA 3.0, original version
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Conclusions & Recommendations
There’s no silver bullet, not expected for quite some time. Each location/setting requires a complex detection means puzzle to be completed for optimal risk mitigation
Much to do by many• Science – Fill in the Gaps in Knowledge• Developers – Keep Developing & Improving C-UAS Systems• Overarching Party – Create a common (open source) Backbone • Operations - Make clear what you do and do not need, help by facilitating operational tests and share the results
1• Prevention
2• Detection/Tracking
3• Classification/Identification/Intent
4• Decision making
5• Neutralization
6• Forensics
Fully engaged in C-UAS!
NLR AmsterdamAnthony Fokkerweg 21059 CM AmsterdamThe Netherlands
p ) +31 88 511 31 13 e ) [email protected] i ) www.nlr.org
NLR MarknesseVoorsterweg 318316 PR MarknesseThe Netherlands
p ) +31 88 511 44 44 e ) [email protected] i ) www.nlr.org
Fully engagedRoyal Netherlands Aerospace Centre
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