Content uploaded by Vivek Gopal
Author content
All content in this area was uploaded by Vivek Gopal on May 10, 2021
Content may be subject to copyright.
Attribution: Vivek Gopal, “Developing an Eective Anti-Drone System for India’s Armed Forces,” ORF Issue
Brief No. 370, June 2020, Observer Research Foundation.
Observer Research Foundation (ORF) is a public policy think tank that aims to influence the formulation of policies
for building a strong and prosperous India. ORF pursues these goals by providing informed analyses and in-depth
research, and organising events that serve as platforms for stimulating and productive discussions.
Observer Research Foundation
ISBN: 978-93-90159-32-1
© 2020 Observer Research Foundation. All rights reserved. No part of this publication may be reproduced, copied, archived, retained or transmitted through print, speech or electronic media without prior written approval from ORF.
JUNE 2020
ISSUE NO. 370
Developing an Eective Anti-Drone System
for India’s Armed Forces
ViVek Gopa l
ABSTRACT e use of drones or Unmanned Aerial Vehicles (UAVs), both for military and
civilian purposes, has increased in India in the past decade. At the same time, counter-
drone systems are also being developed to address the threats posed by UAVs. How
eective are these counter-drone mechanisms? is brief explores this question, and oers
suggestions for India to reduce the growing threat from drones. Any evaluation of the
ecacy of anti-drone systems has to be conducted in view of current technologies such as
Articial Intelligence (AI), cognitive Global Positioning System avoidance, and hardware
sandboxing—and such is the aim of this brief.
ORF issue bRieF no. 370 june 2020
Developing an Eective Anti-Drone System for India’s Armed Forces
2
INTRODUCTION
In the past several years, India has been
seeing more use of drones—or small
unmanned aerial vehicles (UAVs)—for
various military and civilian purposes. ese
include reconnaissance, imaging, damage
assessment, payload delivery (lethal as well
as utilitarian) and as seen recently amidst
the COVID-19 pandemic, for contact-less
delivery of medicines. e use of drones,
however, poses threats to public security
and personal privacy.1 Analysts warn that
as Unmanned Aerial Systems (UAS) become
less expensive, easier to y, and more
adaptable for crime, terrorism or military
purposes, defence forces will increasingly be
challenged by the need to quickly detect and
identify such aircraft.2 e Small Unmanned
Aircraft System (sUAS) technologies are
continuously evolving: indeed, customised
sUAS—i.e., UAVs, micro UAVs, and
drones with their controller stations and
equipment—can operate without radio
frequency (RF) command and control links,
and can use automated target tracking,
aside from having obstacle avoidance and
software-controlled capabilities.3
is brief outlines the signicance of
the various threats posed by the increasing
use of drones. It suggests a roadmap for
addressing such threats. e brief covers
the aspects of detection, identication and
localisation techniques, as well as jamming
and other countering measures. It also looks
into the various existing anti-UAS solutions
already in the market.
DETECTION, IDENTIFICATION AND
LOCALISATION TECHNIQUES
Drones have low Radar Cross Section
(RCS), slow speed and a small size—these
characteristics make the task of detection
dicult, and thereafter, identication and
localisation even more so. In response,
governments and military forces across the
world, including in India have developed
various approaches to detect these aerial
systems. ese methods can broadly be
classied as radar, video/electro-optical (EO),
audio/acoustic, and RF-based.
Detection Techniques
Radar. Radar is a useful tool for detection
of aircraft. There are various challenges,
however. These include a drone’s low
altitude and velocity of flying, and very
small radar cross-section (RCS) which makes
it extremely difficult to distinguish noise or
clutter from the actual target.
RCS Study. Analysing the micro-doppler
signature by multi-static radar can help
in accurate drone detection and tracking.
4 Small drone RCS estimation obtained by
outdoor measurements has been analysed
and proved that passive techniques can
be used to detect and track drones.5 Back
scattering phenomenon associated with
micro motion has also been studied by
scientists. Whereas the total RCS is
important for target detection, the energy
backscattered from rotating parts like
propeller and rotors are crucial for extraction
ORF issue bRieF no. 370 june 2020
Developing an Eective Anti-Drone System for India’s Armed Forces
3
of useful micro-Doppler signatures.a
Multiple Input Single Output (MISO)
Radar Systems. A single antenna is used
for transmission, whereas four antennae are
used for reception. e concept of estimating
the minimum power requirement that must
be transmitted to reveal a target with a
specic value of RCS is possible with the radar
equation. Drones up to a range of 150 metres
can be detected using this technique.6
Multiple Input Multiple Output (MIMO)
Radar Systems. Using MIMO radar systems
further increases the probability of detection
of drones and UAVs. It introduces high
angular resolution and sensitivity for slow-
moving targets and covers a wider area with
lesser cost, as compared to conventional
phased array systems. It also exhibits
enhanced discrimination from clutter and
phase noise.7
Ku–Band (12 to 18 Giga-hertz)
Battleeld Radars. eoretical analysis
of the detection probability has been carried
out 8 in addition to the relationship between
signal to noise ratio as well as small RCS of
drones. is was based on a study sponsored
by MoD of the Slovak Republic. A 35 Giga-
hertz Frequency Modulated Continuous
Wave (FMCW) drone detection system has
also been studied, and the results obtained
have been validated in theory as well as
practice.9
Classication and Localisation Techniques
Audio. Although not a highly eective
system in the stand-alone mode due to
the omnipresent background noise which
increases the complexity of detection and
computation, it can be used as a system that
can be superimposed with other detection
techniques. During the ight of the drone, the
sound generated by the rotors can be utilised
in detection, classication and localisation
of drones. Algorithms such as Multiple
Signal Classication (MUSIC) can be used to
estimate the direction of arrival. Tetrahedron
acoustic arrays have been used in systems
to nd out the direction of arrival using
received signal strength and time dierence
of arrival method in addition to Kalman lter
for tracking.10, 11
Optical/Video/Infrared (IR). An object
can be detected based on its appearance
features12 and/or its motion features across
consecutive frames.13 For drone detection,
it is promising to combine both motion
features and appearance features—this gives
higher accuracy to the system. A thermal
imaging-based enhancement algorithm for
IR scanner system has also been proposed
by the Military Institute of Technology,
Poland.14 Even in conditions of low
visibility, the IR spectrum can be discerned
at considerable distances. However, there
are certain disadvantages with IR systems,
including: low spatial resolution, low
a Demirev V.,2017, “Drone Detection in Urban Environment – e New Challenge for the Radar Systems Designers”,
International Scientic Journal "Security & Future", WEB ISSN 2535-082X, Year 1 Issue 3, pp 114-116.
ORF issue bRieF no. 370 june 2020
Developing an Eective Anti-Drone System for India’s Armed Forces
4
image contrast, diused edges, presence of
noise, and pulse disturbances. e Inverse
Synthetic Aperture Radar (ISAR) technique
has also been employed during a study in
Korea Aerospace Research Institute, for
counter UAS systems to detect drones in
urban areas. e detailed structure, size of
the drone and the number of rotors can also
be determined using this technique of ISAR
as demonstrated in the study.15
Micro-Doppler Analysis. e micro-
doppler signature depends on parts of an
object moving and rotating in addition to the
main body motion (e.g. rotor blades) and is a
characteristic of the type of object. Research
has been centred around understanding the
micro-doppler spectra of specic commercial
drones whose rotor blades or hubs are
uniformly displaced from the platform
centre of mass. When superimposed with
radars in the X-Band (7-11 Giga-hertz) and
Ku-Band, there is higher accuracy due to the
fusion of the radar sensor data as compared
to a single radar.16,17 e cepstrum reveals,
using signal processing techniques, how
the received signals can be used for image
recognition. As shown in Figure 1, the study
carried out at Fraunhofer Institute for High
Frequency Physics and Radar Techniques
(FHR), Germany, micro-Doppler analysis
when used in conjunction with radar data,
provides good results when determining
the rotational speed of the rotors. e
individual rotor speeds can subsequently
be read from the cepstrogram or the
cepstrum. e accompanying graph shows
the cepstrum of the four rotor blades of a
drone ight. Due to the ight movement
of the drone, two of the blades are rotating
faster, therefore the higher rotations per
minute. Principal Component Analysis
(PCA) is another method used to reduce the
number of variables in data by extracting
important ones from a large pool. It reduces
the dimension (size) of the data with the
aim of retaining as much information as
possible. e PCA technique has been used
for feature extraction from time-frequency
spectrograms in addition to support vector
machines (supervised machine learning
algorithms) to recognise the drones.
Fig.1- Micro-doppler Radar DroMiAn (Drone detection with micro-Doppler Analysis) (Left)
& Cepstrum of Four Rotor Blades (Right)
(Image Source: https:// www.fhr.fraunhofer.de/en/ businessunits/security/ Drone_detection_with_micro-doppler_analysis.html)
ORF issue bRieF no. 370 june 2020
Developing an Eective Anti-Drone System for India’s Armed Forces
5
JAMMING AND COUNTERING
TECHNIQUES
Defeating a drone can involve a plethora of
techniques. To name a few, it may involve
kinetic means, electronic warfare (EW) and
cyber warfare (CW) techniques, Drone vs
drone, and Directed Energy Weapons (DEW)
using high-powered microwave or LASERs.
Various integrated solutions have been
oered in the world market with respect to
defeating drones or sUAS. e noteworthy
options include RF jamming, GPS jamming,
GPS spoong, and net guns.18 While jamming
the controller link is an option, the widely
used concept involves jamming the GPS
link (bands L1 to L5) to make the drone
lose control of its “auto-home” option when
the main controller link fails (i.e., jammed).
One can also develop techniques to jam the
payload WiFi link which will also be in almost
the same frequency range as the 5 Giga-hertz
controller link (HP 47 counter UAV Jammer
does exactly this by blocking the video feeds).
A layered approach is what will be preferable
in a counter-drone system.
Boeing demonstrated the High Energy
Laser Mobile Demonstrator in 2014, which
red a 10-Kilowatt LASER and could function
eectively in dierent climatic conditions
like rain, fog or wind. e Drone Catcher
Gun is also a scalable option available to
safeguard facilities as has been researched
and demonstrated.19 e jamming of drones
can also be eected using a 3D MIMO radar.20
By using directional antennae, the power is
conned spatially, thereby allowing its use
without interference to co-located RF devices/
equipment.
e two most potent and comprehensive
repositories of counter-UAS have been
studied as part of preparing this brief.21, 22
e resulting database as part of the
aforementioned repositories is based on
open-source research of technical and policy
reports, news analyses, manufacturers’
information, interviews with government
ocials, subject matter experts, and
participation in conferences. e sheer
volume as well as the description of the
counter UAS weapons prohibits its inclusion
in this brief.
e database consists of nearly 537
systems, sold by 277 vendors and elded by
38 dierent countries, some alone and others
as part of joint ventures between two nations.
RF & radar detection have been found to
be the most common detection techniques,
followed by EO/IR systems. Jamming the
RF and GNSS signals is the most common
method, followed by a few which have
spoong capability. Some also mention the
use of a ‘sacricial drone’, or a drone that
may be used to deliberately collide with the
detected enemy drone to destroy it.
ARCHITECTURE AND
IMPLEMENTATION
Of the 537 systems discussed earlier in this
brief, unfortunately, none of the systems from
India nd a mention. is may be because
information may be classied, or there was
no participation by Indian vendors. However,
during the Republic Day parade in 2020, there
were media reports of an anti-drone weapon
having been elded.23 Based on the various
proof of concepts as well as technology
ORF issue bRieF no. 370 june 2020
Developing an Eective Anti-Drone System for India’s Armed Forces
6
demonstrators, one can now elaborate on the
system design for a modular counter-drone
weapon. e main purpose of these measures
is to exploit the vulnerabilities of drones.24
Heterogenous Sensing Unit – Detection,
Localisation and Tracking
A heterogenous sensing unit will serve the
purpose of detection and localisation of
the drone. e heterogenous system will
comprise of various sensors which can be
utilised in any weather. ere should be
RF/ radar-based sensors as the rst line of
detection, coupled with acoustic sensors.
is can be followed by an EO/IR system
layer for enhancing the resolution. e range
of detection should be a minimum of three
km and it should eectively be able to track
the drone at minimum 1 km, assuming the
drone is carrying a destructive payload.
Central Processing Unit
e Central Processing Unit should be able
to collate the feed from all the active and
passive sensors. It should thereafter carry out
analysis, derive drone features, and achieve
classication.
For the acoustic sensors, the Short Time
Fourier Transform (STFT) can be adopted
and the histogram of oriented gradients
(HOG) can be used 25 for extraction of image
features. ese are in addition to the PCA
methods described earlier.
In the case of the RF features to be
extracted, it may be assumed that at least
two out of the following three links are active
at any given point in time, viz., controller
link or the ground control link (to include
all frequency bands as prevalent based on
the manufacturer specications); GPS link
(uplink & downlink frequency bands as
also to cover GLONASS, Bediou & possibly
IRNSS); and the payload (assumed camera/
optical device for reconnaissance) WiFi link
back to the ground control station which
may transmit the data captured in real time
to the control station. e development
and incorporation of neural networks can
be considered subsequently. By using the
logical ‘OR’ operation, it will be ensured that
probabilities of detection are high despite
the possibility of a false alarm, albeit to a
minimum.
Jamming Unit
Based on the inputs and analysis carried out
by the Central Processing Unit, the jamming
unit can be made to work in three modes akin
to air defence systems viz., Weapon Hold,
Weapon Free, and Weapon Tight. Based on
directional antennae to achieve better spatial
economy as well as meet power requirements,
a 360-degree coverage is preferable (gimbalb
based for added stabilisation) with a slaved
ser vo/propor t ional-integra l-der ivat ive
(feedback based) controller to the tracker.
A spoong unit should be made alongside
the jamming unit to take over the drone if
b e word “gimbal” is dened as a pivoted support that allows rotation of any object in a single axis. So a three-
axis gimbal allows any object mounted on the gimbal to be independent of the movement of the one holding the
gimbal. e gimbal dictates the movement of the object, not the one carrying it.
ORF issue bRieF no. 370 june 2020
Developing an Eective Anti-Drone System for India’s Armed Forces
7
desired for forensics at a later stage (Man in
the middle attackc).
Kill Unit
If the jamming unit is unable to aect the
drone operations due to any reason, the high-
power microwave unit should kick into action
to get the drone on the ground. e range
of engagement can be xed with respect to
various vulnerability aspects, such as the
installation being defended, the importance
of the area, and the vital point. An alternate
high-power LASER may also be employed
to destroy the drone. e close-in weapon
support systems (CIWSS) as used extensively
by naval vessels the world over against hostile
missiles is also another option which may be
explored.
System Integration and Implementation
e proposed system should be fabricated
in a modular fashion allowing sub-system
integration based on the felt need. e
system at the same time should be made in
two versions, viz., manpack (portable) and
vehicle-based, obviously scaling down the
parts and therefore, weight for the manpack
system (SWaP considerations). e design of
the system may be adapted from the system
as shown in Figure 2.
c A man in the middle (MITM) attack is a general term for when a perpetrator positions himself in a conversation
between a user and an application—either to eavesdrop or to impersonate one of the parties, making it appear
as if a normal exchange of information is underway. A spoofed signal in the case of a drone will impersonate the
actual signal of interest and misguide it.
Fig. 2 Envisioned Counter Drone System – Layered Architecture
Author's own.
ORF issue bRieF no. 370 june 2020
Developing an Eective Anti-Drone System for India’s Armed Forces
8
While the system as envisioned
in this brief has been trial-evaluated,
stakeholders in the anti-UAS campaign
such as Rafael (Israel-based) have come up
with the Drone-DomeTM drone detection,
neutralisation and interception system
as well as the FireflyTM miniature tactical
loitering weapon.d
d A miniature EO tactical loitering munition which serves as ears and eyes for behind cover/ beyond line of sight.
is capability as seen, may be exploited against drones also using its stand-o range as well as enhanced camera
capability.
Fig. 3- Drone Dome Brochure (Open Source) – Array of sensors and integration
Fig. 4- Firey Brochure (Open Source) – Tactical loitering weapon to ‘Kill’ Drones
(Image Credit: https://www.rafael.co.il/wp-content/uploads/2019/03/FIREFLY.pdf)
ORF issue bRieF no. 370 june 2020
Developing an Eective Anti-Drone System for India’s Armed Forces
9
While the man-portable systems should
be capable of being used by the dismounted
soldier or the infantry, the vehicle-based
system may be utilised to guard static
installations. It is recommended that being
a tri-service requirement, the fabricating
process be undertaken under the aegis of the
HQ Integrated Defence Sta or a prototype
be developed as an Army Technology Board
project. What must also be noted is that
collaboration (internal civil industry as well
as global players) in this eld will reap greater
dividends rather than a purely ‘Make in India’
approach which may take more time.
Under the gambit of the draft DPP 2020,
the innovation scheme under the ‘Make’
category/ Innovation in Defence Excellence
(iDEX)/ Technology Development Fund
initiatives may help in the long run. DGCA
will need to be incorporated to streamline
the rules of engagement (Civil Aviation
Requirements or CAR). Such ‘Aerial reat
Reduction Teams (ATRT)’ should be a tri-
services unit or outt, with a participation
by personnel from all three services, for
eective and ecient utilisation.
CHALLENGES
e challenges being faced today with
respect to counter-drone technology
is manifold as drone technology is
itself growing by leaps and bounds.
Reconnaissance, Intelligence, Surveillance
& Target Acquisition (RISTA) & Electronic
Warfare systems will have to work together
to tackle the threat of both stand-alone
and drone swarms. Some of the pertinent
technological challenges are being highlighted
which will need immediate attention to be
considered when the anti-drone weapon is
conceptualised.
Heterogenous Sensor Fusion. e variety
of protocols used for communication
between sensors poses a problem during
their integration into a networked system.
Energy Ecient Sensors. Power
management considerations will always
be towering as the employment of such
systems is going to be all-day and all-night.
Solar power sources and fuel cells are the
technologies which may be tapped.
Multiple Drone Detection and
Localisation. Technical challenges will
be compounded when trying to detect
and localise swarms wherein it will not be
necessary that all the drones are having a
viable RF signature. Progress has been made
in using millimetre -wave radar as well as
measuring the turbulence caused by the
drone rotors.
This threat is evolving every three to six months
– it is just that adaptive. This is going to be a
continuing challenge due to the adaptive nature
of the problem of being able to use small drones
in so many different ways and you cannot rely
on one technique to respond to them.
- Vayl S. Oxford, Director, U.S. Defence Threat
Reduction Agency, March 2019.
ORF issue bRieF no. 370 june 2020
Developing an Eective Anti-Drone System for India’s Armed Forces
10
Drone Signature Database. Extensive
studies are to be conducted to have a
comprehensive database. Inter-agency
cooperation may help mitigate the issues
involved.
Identification Friend or Foe (IFF). T h e
IFF shall always remain challenging with the
use of drones for all unethical purposes by
non-state actors. However, research has been
carried out in this eld using a relay drone
with beacon system.26
Hardware Sandboxing. With systems in
place to reduce the eectiveness of drone
jamming (sandboxing), novel kinetic kill
methods will have to be developed.27
Prevention Against Jamming (JAM-
ME) Techniques. Jamming is now being
leveraged for drone missions into
completion.28
Cognitive Avoidance in 4th Generation
GPS. GPS systems are becoming increasingly
more interference-proof. Long Term
Evolution or LTE may soon be used to operate
drones at theoretically unlimited ranges
without RF links, i.e., cellular base stations
will provide theoretically unlimited range.29
New ight modes that have been
introduced in UAVs or drones which include
obstacle avoidance using ultrasonic sensors,
terrain mapping cameras integrated as
SoC, follow me, tapy, active track and
sports mode. Better battery systems have
been incorporated giving the drone larger
endurance (5230 mAh battery on board the
DJI Phantom 4).
CONCLUSION
Endless possibilities exist in designing a
system to counter drones which may extend
later to UAVs as well as collaborative swarms.
A committee needs to be formulated at the
apex level which has stakeholders from the
defence forces in addition to judiciary, the
academia, and industry (to include Defence
Public Sector Undertakings).
e anti-drone systems will denitely
be expensive owing to the technology
involved. However, costs involved should
not thwart the innovations. Better rules
and regulations need to be formulated to
regulate the Unmanned Trac Management
(UTM). A future roadmap has already been
laid out for CAR 2.0.30 ere is nothing that
stops India from nally having a drone vs
drone ‘dogght’ or a swarm-against-swarm
type of architecture. A situation where
drones are “scrambled” to engage and nullify
a threat will no longer be mere material for
ction. Indeed, drones being used as a ‘loyal
wingman’ are already under test. Systems
like HyperTechTraxerTM (UK registered
rm, based in Finland) which use non-
lethal weapons as well as swarms to monitor
sensitive installations have also been made
available in the world market.
e strategy to defend space must
be based on adapting to the need of the
specic environment where the deployment
ORF issue bRieF no. 370 june 2020
Developing an Eective Anti-Drone System for India’s Armed Forces
11
of the counter-drone system is intended.
Integration of various sensors and nally
redundancy for sensors in number is also
imperative. ere is a denite need to align
the timeframe taxonomy with the prevailing
ABOUT THE AUTHOR
Lt Col Vivek Gopal is an Instructor at a premier training institute.
threat at all levels. Disruptive techniques
that have been brought out in this brief
will also pave the way for future research
and development of whole some systems to
thwart adversarial designs.
ORF issue bRieF no. 370 june 2020
Developing an Eective Anti-Drone System for India’s Armed Forces
12
ENDNOTES
1 Shi, Xiufang, Chaoqun Yang, Weige Xie, Chao Liang, Zhiguo Shi, and Jiming Chen. 2018. "Anti-
Drone System With Multiple Surveillance Technologies: Architecture, Implementation, And
Challenges". IEEE Communications Magazine 56 (4): 68-74. doi:10.1109/mcom.2018.1700430.
(National Academies of Sciences, Engineering, and Medicine. 2018. Aeronautics 2050:
Proceedings of a Workshop – In Brief. 2018)
2 Haviv, Haim. 2019. "Drone reat And CUAS Technology: White Paper."
3 "Counter Unmanned Aircraft System (CUAS) Capability For Battalion – And – Below
Operations:Abbreviated Version Of A Restricted Report". 2020. National Academy of Sciences.
Accessed March 18.
4 Shi, Xiufang, Chaoqun Yang, Weige Xie, Chao Liang, Zhiguo Shi, and Jiming Chen. 2018. "Anti-
Drone System With Multiple Surveillance Technologies: Architecture, Implementation, And
Challenges". IEEE Communications Magazine 56 (4): 68. doi:10.1109/mcom.2018.1700430.
5 Rzewuski, S, K Kulpa, P Kopyt, B Salski, K Borowiec, and M Malanowski. 2018. "Drone RCS
Estimation Using Simple Experimental Measurement In e WIFI Bands 2018 , 695-698".
In 22Nd International Microwave And Radar Conference (MIKON).
6 Sacco, Giulia, Erika Pittella, Stefano Pisa and Emanuele Piuzzi. “A MISO Radar System
for Drone Localization.” 2018 5th IEEE International Workshop on Metrology for AeroSpace
(MetroAeroSpace) (2018): 549-553.
7 Alnuaim, Tareq, Mubashir Alam and Abdulrazaq Aldowesh. “Low-Cost Implementation of a
Multiple-Input Multiple-Output Radar Prototype for Drone Detection.” 2019 International
Symposium ELMAR (2019): 183-186.
8 J. Ochodnický, Z. Matousek, M. Babjak and J. Kurty, "Drone detection by Ku-band battleeld
radar," 2017 International Conference on Military Technologies (ICMT), Brno, 2017, pp. 613-
616, doi: 10.1109/MILTECHS.2017.7988830.
9 J. Drozdowicz et al., "35 GHz FMCW drone detection system," 2016 17th International Radar
Symposium (IRS), Krakow, 2016, pp. 1-4, doi: 10.1109/IRS.2016.7497351.
10 Chang, Xianyu & Yang, Chaoqun & Wu, Junfeng & Shi, Xiufang & Shi, Zhiguo. (2018). A
Surveillance System for Drone Localization and Tracking Using Acoustic Arrays. 10.1109/
SAM.2018.8448409.
11 C. Yang, Z. Wu, X. Chang, X. Shi, J. Wo and Z. Shi, "DOA Estimation Using Amateur Drones
Harmonic Acoustic Signals," 2018 IEEE 10th Sensor Array and Multichannel Signal Processing
Workshop (SAM), Sheeld, 2018, pp. 587-591, doi: 10.1109/SAM.2018.8448797.
12 Z. Zhang, Y. Cao, M. Ding, L. Zhuang and W. Yao, "An intruder detection algorithm for vision
based sense and avoid system," 2016 International Conference on Unmanned Aircraft Systems
(ICUAS), Arlington, VA, 2016, pp. 550-556, doi: 10.1109/ICUAS.2016.7502521.
13 Ganti, Sai Ram and Yoohwan Kim. “Implementation of detection and tracking mechanism
ORF issue bRieF no. 370 june 2020
Developing an Eective Anti-Drone System for India’s Armed Forces
13
for small UAS.” 2016 International Conference on Unmanned Aircraft Systems (ICUAS) (2016):
1254-1260.
14 T. Sosnowski, G. Bieszczad, H. Madura and M. Kastek, "ermovision system for ying objects
detection," 2018 Baltic URSI Symposium (URSI), Poznan, 2018, pp. 141-144, doi: 10.23919/
URSI.2018.8406757.
15 K. Lee et al., "Identication of a ying multi-rotor platform by high resolution ISAR through
an experimental analysis," International Conference on Radar Systems (Radar 2017), Belfast,
2017, pp. 1-5, doi: 10.1049/cp.2017.0435.
16 M. A. Govoni, "Micro-Doppler signal decomposition of small commercial drones," 2017
IEEE Radar Conference (RadarConf), Seattle, WA, 2017, pp. 0425-0429, doi: 10.1109/
RADAR.2017.7944240.
17 P. Zhang, L. Yang, G. Chen and G. Li, "Classication of drones based on micro-Doppler signatures
with dual-band radar sensors," 2017 Progress in Electromagnetics Research Symposium - Fall
(PIERS - FALL), Singapore, 2017, pp. 638-643, doi: 10.1109/PIERS-FALL.2017.8293214.
18 L3 Technologies. “Countering the drone threat to commercial airports” accessed at https://
www.l3-droneguardian.com/white-papers/L3_Technologies_Whitepaper-Countering_
Drones_at_Commercial_Airports.pdf
19 Krishna Kumar Karothiya, Abhishek Yadav, Abhyuday Mishra, Ankush, Anshul Gupta,
Anshul Katiyar, DRONE CATCHER GUN, GLOBAL JOURNAL FOR RESEARCH ANALYSIS:
VOLUME-7, ISSUE-2, FEBRUARY-2018.
20 T. Multerer et al., "Low-cost jamming system against small drones using a 3D MIMO radar
based tracking," 2017 European Radar Conference (EURAD), Nuremberg, 2017, pp. 299-302,
doi: 10.23919/EURAD.2017.8249206.
21 "Counter-Drone Systems". 2020. Dronecenter.Bard.Edu. https://dronecenter.bard.edu/
projects/counter-drone-systems-project/.
22 Butterworth-Hayes, Philip. 2018. "Unmanned Airspace: e Latest Counter-UAS Industry
Directory - Free Download". Unmanned Airspace. https://www.unmannedairspace.info/
counter-uas-industry-directory/.
23 2020. https://epaper.hindustantimes.com/%20Home/MSharearticle?OrgId=%203250a44d.
24 N. M. Rodday, R. d. O. Schmidt and A. Pras, "Exploring security vulnerabilities of unmanned
aerial vehicles," NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management
Symposium, Istanbul, 2016, pp. 993-994, doi: 10.1109/NOMS.2016.7502939.
25 N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," 2005 IEEE
Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), San
Diego, CA, USA, 2005, pp. 886-893 vol. 1, doi: 10.1109/CVPR.2005.177.
26 Chang, Yunseok. “A Drone IFF and Tracking Algorithm with the Relay Drone and the Beacon
System.” (2019).
ORF issue bRieF no. 370 june 2020
Developing an Eective Anti-Drone System for India’s Armed Forces
14
27 J. Mead, C. Bobda and T. J. Whitaker, "Defeating drone jamming with hardware sandboxing,"
2016 IEEE Asian Hardware-Oriented Security and Trust (AsianHOST), Yilan, 2016, pp. 1-6,
doi: 10.1109/AsianHOST.2016.7835557.
28 R. Di Pietro, G. Oligeri and P. Tedeschi, "JAM-ME: Exploiting Jamming to Accomplish
Drone Mission," 2019 IEEE Conference on Communications and Network Security (CNS),
Washington DC, DC, USA, 2019, pp. 1-9, doi: 10.1109/CNS.2019.8802717.
29 B. Kelley, G. De La Torre Parra and D. Akopian, "Cognitive interference avoidance in 4th
generation GPS," 2015 10th System of Systems Engineering Conference (SoSE), San Antonio,
TX, 2015, pp. 410-415, doi: 10.1109/SYSOSE.2015.7151973.
30 "DRONE INNOVATION 2019 & CONTEMPORARY CHALLENGES". 2019. Bprd.Nic.In.
https://bprd.nic.in/WriteReadData/userles/le/201907010629477222182ReportonDrone
Innovation2019andContemporaryChallenges.pdf.
ORF issue bRieF no. 370 june 2020
Developing an Eective Anti-Drone System for India’s Armed Forces
15
Developing an Eective Anti-Drone System for India’s Armed Forces
20, Rouse Avenue Institutional Area, New Delhi - 110 002, INDIA
Ph. : +91-11-35332000. Fax : +91-11-35332005.
E-mail: contactus@orfonline.org
Website: www.orfonline.org
