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Anti-drone Sensors, Effectors, and Systems – A Concise Overview

Authors:
Dariusz Zmysłowski at Military University of Technology
Dariusz Zmysłowski
Paweł Skokowski at Military University of Technology
Paweł Skokowski
Jan M. Kelner at Military University of Technology
Jan M. Kelner
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Abstract

The dynamic development of microelectronics and wireless communication at the turn of the 20th and 21st centuries contributed to the increase in the availability and popularity of all kinds of unmanned platforms, air, land, surface, and underwater. In the case of unmanned aerial vehicles (UAVs), also popularly known as drones, the significant (crucial) advantages are high freedom of motion (i.e., the ability to move in three planes) and the possibility of using it practically anywhere (as opposed to water platforms). These factors influenced the dynamic development of this market sector and the high availability of various models and equipment. UAVs are being used in many sectors and services, both for civil and military purposes. Widespread availability, low cost, and ease of use also favor the possibility of using civilian UAVs for criminal, smuggling, terrorist, or military purposes. Recent armed conflicts, e.g., in Nagorno-Karabakh or Ukraine, and the situation on the eastern Polish-Belarusian border clearly show this. All countries recognize the potential and threat posed by the development of unmanned platforms. Therefore, for several years, special attention has been paid to analyses and research in sensors, effectors, and anti-drone systems. It is worth emphasizing that some effectors (e.g., jammers or spoofers) may significantly affect the navigation process of neighboring objects that use global navigation satellite systems (GNSSs). On the other hand, the use of anti-drone systems is important, especially in the context of protecting institutions and facilities of companies, state administration (i.a., embassies, consulates), army, strategic importance objects (e.g., related to energy, chemical industry), or protection of mass events. In this paper, we present an overview of solutions available on the market and development directions in the field of anti-drone technology.
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1 INTRODUCTION
Drones,alsoknownasunmannedaerialvehicles
(UAVs),havebecomeubiquitousinrecentyearsdue
totheirvariousapplications,includingaerial
photography,surveying,anddeliveryservices.
However,dronescanalsobeusedfornefarious
purposes,includingespionage,terrorism,and
smuggling.Thenumberofdronesisgrowingvery
fast,andtheircommercialuseisbecomingmore
widespread,attractingahighlevelofinterestfrom
investors,industry,business,governmentinstitutions,
themilitary,agencies,specialservices,andprivate
individuals.Knowledgeandpracticeindesigningand
manufacturingdroneshavebecomemoreaccessible.
Alotofinstitutionsandindividualsaredesigningand
usingtheirdrones.Thecomponentsforconstructing
simplemicro‐classdronesareavailableinthemarket
withoutlegalrestrictions.Allofthesefactorsprevent
usingcommerciallyavailabledronesandself‐made
dronesformilitary,terrorist,orunintentionalusethat
violatesthelaw.Thedevelopmentofanti‐drone
systemshasbecometheneedofthehour.The
increasingnumberofincidentsoractsofsecurity
Anti-drone Sensors, Effectors, and Systems – A Concise
Overview
D.Zmysłowski,P.Skokowski&J.M.Kelner
M
ilitaryUniversityofTechnology,Warsaw,Poland
ABSTRACT:Thedynamicdevelopmentofmicroelectronicsandwirelesscommunicationattheturnofthe20th
and21stcenturiescontributedtotheincreaseintheavailabilityandpopularityofallkindsofunmanned
platforms,air,land,surface,andunderwater.Inthecaseofunmannedaerialvehicles(UAVs),alsopopularly
knownasdrones,thesignificant(crucial)advantagesarehighfreedomofmotion(i.e.,theabilitytomovein
threeplanes)andthepossibilityofusingitpracticallyanywhere(asopposedtowaterplatforms).Thesefactors
influencedthedynamicdevelopmentofthismarketsectorandthehighavailabilityofvariousmodelsand
equipment.UAVsarebeingusedinmanysectorsandservices,bothforcivilandmilitarypurposes.
Widespreadavailability,lowcost,andeaseofusealsofavorthepossibilityofusingcivilianUAVsforcriminal,
smuggling,terrorist,ormilitarypurposes.Recentarmedconflicts,e.g.,inNagorno‐KarabakhorUkraine,and
thesituationontheeasternPolish‐Belarusianborderclearlyshowthis.Allcountriesrecognizethepotentialand
threatposedbythedevelopmentofunmannedplatforms.Therefore,forseveralyears,specialattentionhas
beenpaidtoanalysesandresearchinsensors,effectors,andanti‐dronesystems.Itisworthemphasizingthat
someeffectors(e.g.,jammersorspoofers)maysignificantlyaffectthenavigationprocessofneighboringobjects
thatuseglobalnavigationsatellitesystems(GNSSs).Ontheotherhand,theuseofanti‐dronesystemsis
important,especiallyinthecontextofprotectinginstitutionsandfacilitiesofcompanies,stateadministration
(i.a.,embassies,consulates),army,strategicimportanceobjects(e.g.,relatedtoenergy,chemicalindustry),or
protectionofmassevents.Inthispaper,wepresentanoverviewofsolutionsavailableonthemarketand
developmentdirectionsinthefieldofanti‐dronetechnology.
http://www.transnav.eu
the International Journal
on Marine Navigation
and Safety of Sea Transportation
Volume 17
Number 2
June 2023
DOI:10.12716/1001.17.02.23
456
breachesorevenattacksusingdroneshasledtothe
emergenceofincreasinglysophisticateddrone
detectionandcountermeasureequipment.Therefore,
itisnecessarytohaveanti‐dronesystemsinplaceto
detectandneutralizethesethreats.Anti‐drone
systemscalledalsoascounterunmannedaerial
systems(C‐UASs),bothstationaryandmobile,have
alsostartedtobebuiltandoffered.
ThedynamicgrowthoftheC‐UASsectoris
evidencedalsobytheongoingresearchand
developmentinthisarea.Resultsofsearchingfor
worksrelatedtothekeyword‘anti‐drone’intheIEEE
Xploreinternationaltechnicalpublicationsdatabase
[1],thedatabaseprovides45works(asof10March
2023),includingonebook,twojournalarticles,six
scientificjournalarticles,and36conferencepapers.
Alltheseworksarefromtheperiod2017–2022.
Oneoftheprimarymethodsofdetectingdronesis
usingsensors.Thereareseveraltypesofsensorsthat
canbeusedforthispurpose,includingacoustic
sensors,radarsystems,andopticalsensors.Oncea
dronehasbeendetected,itisnecessarytoneutralize
thethreat.Thereareseveraltypesofeffectorsthatcan
beusedforthispurpose,includingjammingsystems,
directedenergyweapons,andnets.
ThispaperpresentsthegeneralconceptofC‐UAS,
itsfunctionalities,andmainelements.Thisclassof
systemsstartedtobemoreimportantthesedays.We
decidedtomakeahigh‐levelfunctionaloverview
whichcanbeakindofguidancearoundanti‐drone
systemsandtheirelements.Weintendedto
characterizethetopicassimplyaspossible,tobea
quickstartguideforpeoplenotwell‐orientedinthis
areaofknowledge.Webelieveitwillhelpthemin
furtherpersonalinvestigations.Section2presents
reasonsfordesigning,deploying,andusingdrone
countermeasures.Weconsideredconstructingand
functionalaspectsofexistingandfuturedronesas
platformsthatcouldbepotentialthreatstopeopleand
states,especiallyforcriticalinfrastructure.How
effectivecouldbeaconcreteC‐UASdependsonits
sensorsandeffectors.Section3describesthemost
oftenimplementedcost‐ andtarget‐effectivesensors
asacousticradiofrequency(RF),radar,andoptical
sensors.Section4showsanti‐droneeffectorssuchas
jammingsystems,directedenergyweapons,andnets.
InSection5,bothfixedandmobilesystemshavebeen
described.Wealsopresentedselectedvendorsofanti‐
UAS.Thepaperisfinishedwiththeconclusions.
2 FUNCTIONALCONCEPTOFANTI‐DRONE
SYSTEM
Moderndronesareunmannedflyingobjects,which
aremechanicaldeviceswith:
drivebasedonengines:electric,combustion
(pistonorturbine)oroperatinginhybridsystems;
avionicsprovidingcontrolandflightcontrol,
whichareelectromechanicalandelectronic
systems;
navigationdevicesandsystems,mainlygyroscopic
–inertial()andelectronic–satellite,i.e.,basedon
globalnavigationsatellitesystem(GNSS),
electronicdevicesandradiocommunication
systems,providingsupportforremotecontroland
flightcontrolaswellastransferringofdatafrom
sensorsandcontrolofeffectors;
mainlyelectronicsensors:
formultispectralimaging(visiblerange,
thermalrange,forwardlookinginfrared
(FLIR)),
forradio‐electronicsurveillance,
others;
effectorsforaffectingobjects:
RFjammersandspoofers‐electronicdevices,
explosives‐bombsandguidedmissiles,
others.
Theprincipleofoperation,architecture,and
proceduresofusinganti‐dronedevicesandsystems
arecloselyrelatedtothefunctions,operationaluse,
andtechnicalparametersofmoderndrones.To
effectivelycounteractthepotentiallyharmfulimpact
ofdrones,theanti‐dronesystemshouldidentify
drones,classifythem,assesstheiractivity,and
countermeasurethemiftheyhavebeenidentifiedasa
threattotheprotectedfacility.Theimportantroleof
unambiguousidentificationandclassificationof
objectsobservedbytheanti‐UASsystemshouldbe
considered.Practiceshowsthatmeredetectionofan
objectisinsufficientbecausetheradarthatdetects
dronescanalsorecognizeotherflyingobjectsasbirds.
Inaddition,ownflyingobjectssuchasplanesand
dronescanmoveintheprotectedspace.Itmakes
identificationandclassificationoftheelementary
functionsofmodernC‐UASdevices.
Theroleofthesefunctionswillgrowinthefuture,
mainlytoprotectcriticalinfrastructureanditsmobile
elements(e.g.,oilterminals,gasterminals,tankers,
gascarriers,etc.).Thetechnologythatclassifies
droneswillusuallybeabletoseparatethemfrom
otherobjects–suchasplanes,trains,andcars.The
nextstepistoidentifytheclassifiedobjects.Some
devicescanassessaspecificdronemodelandidentify
theradioemissionsignatureoftheUAV(i.e.,digital
fingerprint)oritscontrolsorcontrollers.
TheelementarycriterionforevaluatingtheC‐UAS
systemistheeffectivenessofprotectionagainst
dronesprovidedtoagivenfacility.Thesystemmust
beappropriatefromtheviewpointofthe
characteristicsoftheprotectedfacility(location,size,
technologicalprocesses,functionsperformed,and
activitiesperformed).
Anti‐dronedevicesandsystemsarebeing
constructedtoeliminatethreatsresultingfromthe
impactofdronesonprotectedfacilities.After
detectingandidentifyingadroneasapotentially
dangerousobject,theiroperationshouldenableits
disposalbyeliminatingit(physicaldestruction)or
preventingitsdestructiveimpactontheprotected
location(turningaround,changingtheflight
parameterssothatitisimpossibletoreachthetarget).
ItmeansthatC‐UASdevicesandsystemsaffectthe
droningengineandalldevicesandsystems,bothon‐
boardandsensorsandeffectors.Itleadstotakinginto
accountinthedesign,implementation,and
operationaluseofanti‐dronesystemsmanyaspects
relatedtodronesasriskfactorsfortheprotected
objects,thetechnologyoftheirproduction,tacticsof
theiruse,butalsothedevelopmenttrendsofthe
informationandcommunicationtechnologies(ICT),
aerospace,andbreakthroughtechnologies,because
457
theyarechosenfortheconstructionandoperation
supportofdrones.
Aholistic,multi‐facetedapproachalsorequiresthe
veryselectionoftechnicallyeffectivesolutionsfor
anti‐UASdevicesandsystems,consideringthe
followingissues[2,3]:
thedesignofbroadbandradiodetectorsand
directionfinders,coveringthedroneʹs
communicationandnavigationbands,
developmentanduseofoperationallyeffective
hemisphericradars,operatinginXandSbands
andusingthemicro‐Dopplereffecttoidentifyand
distinguishdronesfromotherobjects(birds),high‐
resolution,multi‐spectralcameras(visiblerange,
thermalrange,FLIR)
highlyeffective&sensitiveacousticmicrophones
andanalyzersforaudioidentificationofdrones,
artificialintelligence(AI)algorithmstosupport
identificationprocesses,classification,
acreationofsituationalimagerytofacilitatethe
handlingofhazardoussituations,
customizedsysteminterface‐customizedhuman
machineinterface(HMI),
highlyperformativeeffectorsusage:RFjammers
andspoofers,lasersystems,highpowerenergy
(HPE)systems[4–6],C‐UASnets,anti‐aircraft
weaponscapableoffightingdrones,etc.
Thefunctionalityconceptofthemodernanti‐drone
systemispresentedinFigure1.
Figure1.Functionalconceptofanti‐dronesystem(source:
[7,8]).
3 ANTI‐DRONESENSORS
3.1 Acousticsensors
Acousticsensorsusemicrophonestodetectthesound
ofdrones.Thesoundofadroneʹspropellersisdistinct
andcanbeusedtodetectitspresence.Acoustic
sensorseffectivelydetectdronesatcloserange,but
theireffectivenessdiminishesasthedronemoves
furtheraway.Figure2depictsexemplaryprecision
acousticdetectionsystem.
3.2 RFsensorsanddirectionfinders
RFsensorsanddirectionfindersarecriticalsensors
usedinC‐UAStodetectandlocatedrones.RFsensors
detecttheelectromagneticsignalsemittedbydrones,
whiledirectionfindersprovidethedirectionofthe
droneʹssource.Together,thesesensorsenableC‐UAS
systemstoquicklyidentifyandlocatedrones,
allowingoperatorstotakenecessarycountermeasures
tomitigatepotentialthreats.Withtheincreasing
proliferationofdrones,RFsensors,anddirection
findersarebecomingmorecrucialinensuringthe
safetyofcriticalinfrastructure,facilities,andevents
[7,8,10,11].Conceptofdronepositiontrackingby
multipleRFscannersisshowninFigure3.
Figure2.DiscovairG2acousticdetector(source:[9]).
Figure3.DronepositiontrackingbymultipleRFscanners
(source:[2]).
3.3 Radarsystems
Radarsystemsuseradiowavestodetectdrones.
Radarcandetectdronesatadistanceandcantrack
theirmovements.Thereareseveraltypesofradar
systems,includingpulsedradar,continuous‐wave
radar,andfrequency‐modulatedcontinuous‐wave
radar.Eachtypeofradarhasitsadvantagesand
disadvantages,andthechoiceofradarsystem
dependsonthespecificapplication[7,11–18].Samples
ofradarfordronedetectionareillustratedinFigures4
and5.
458
Figure4.RadarR8SS‐3DmadebyTeledyneFLIR(source:
[18]).
Figure5.RadarMESA‐SSRmadebyEchodyne(source:
[16]).
3.4 Opticalsensors
Opticalsensorsusecamerastodetectdrones.Optical
sensorscandetectdronesatadistanceandcan
providevisualconfirmationofthedroneʹspresence.
Opticalsensorscanbeusedduringthedayoratnight
usinginfraredcameras[3,19].Theopticaldetection
systemmadebyAroniaispresentedinFigure6.
Figure6.TheopticaldetectionsystemmadebyAronia
(source:[19]).
4 ANTI‐DRONEEFFECTORS
4.1 Jammingsystems
Jammingsystemsdisruptthecommunicationbetween
thedroneanditsoperator.Thejammingsystemsends
outasignalthatinterfereswiththedroneʹscontrol
signal,causingittolosecontrolandcrash.Jamming
systemsareeffectiveagainstdronesthatare
controlledbyremoteoperator[7,8,10,16,20–22].
Figures7and8presentthecommercialjammer
examples.
Figure7.JammerR&S®ARDN‐GSGNSSmadebyRohde&
Schwarz(source:[8]).
Figure8.JammerSKYNETmadebyLONGBOW+(source:
[22]).
4.2 Directedenergyweapons
Directedenergyweaponsusehigh‐poweredlasersto
disableordestroydrones.Thelaserbeamcandamage
thedroneʹselectronicsorcauseittocrash.Directed
energyweaponsareeffectiveagainstdronesthatare
withinlineofsight[5,6,20,23].Figure9illustratesthe
visionofdestroyingdronesbyamilitarysystem
basedonahigh‐energylaser.
459
Figure9.High‐poweredlaserinaction(source:[6]).
4.3 Nets
Netscanbeusedtocapturedronesmid‐flight.Thenet
islaunchedfromadevicethatcanbehand‐held
[17,24],ormountedonavehicle[24].Thenet
entanglesthedrone,causingittolosecontroland
crash.Netsareeffectiveagainstdronesthatareflying
atlowaltitudes.
Figure10.NetcannonmadebyOpenWorksEngineering
(source:[24]).
5 ANTI‐DRONESYSTEMS
Anti‐dronesystemsarecomposedofsensorsand
effectorsthatworktogethertodetectandneutralize
drones.Thereareseveraltypesofanti‐dronesystems,
includingfixedsystemsandmobilesystems[2,3,10–
13,17,19,25–29].
5.1 Fixedsystems
Fixedanti‐dronesystems(seeFigure11)areinstalled
inaspecificlocationandaredesignedtoprotectthat
location.Fixedsystemsarecommonlyusedtoprotect
criticalinfrastructure,suchasairports,powerplants,
andgovernmentbuildings.
Figure11.ContainerversionofGUARDIONsystem(source:
[10]).
5.2 Mobilesystems
Mobileanti‐dronesystems(seeFigure12)are
designedtobedeployedquicklyandcanbemovedto
differentlocationsasneeded.Mobilesystemsare
commonlyusedforevents,suchasconcertsand
sportingevents,wherethereisaneedfortemporary
protection.
Figure12.MobileversionofAARTOSdronedetection
system(source:[19]).
5.3 Exemplaryvendors
Therearenumerousvendorsofanti‐dronesystemsin
themarket.Thesevendorsoffervarioussolutionsthat
aretailoredtomeettheneedsofdifferentindustries,
suchasmilitary,lawenforcement,andprivate
organizations.Someofthenotablecompaniesand
theirsystemsinclude:
DJIAeroscope:Thissystemprovidesdrone
detectionandtrackingcapabilitiesforlaw
enforcementagencies,allowingthemtolocate
dronesinrestrictedairspaceandidentifythe
pilotʹslocation.ThesystemiscompatiblewithDJI
drones,makingitanidealsolutionforthose
operatingDJIdrones[27].
Dedrone:Dedroneprovidesanairspacesecurity
platformthatusesmulti‐sensordetectiontodetect
andtrackdrones,providingearlywarningto
users.Thesystemintegrateswithvariousother
securitysystemsandcanbeusedtoprotectcritical
infrastructure,events,andothersensitivelocations
[4].
Raytheon:Raytheonoffersarangeofanti‐drone
solutions,includingtheDroneDefender,a
handhelddevicethatusesradiofrequency
jammingtodisabledrones,andtheCoyotedrone,
460
whichisusedtointerceptanddestroyother
drones[23].
D‐FendSolutions:D‐FendSolutionsprovidesa
radiofrequency‐basedsystemcalledEnforceAir
thatcandetectandmitigateroguedrones.The
systemcanbeusedinurban,rural,andindoor
environments,makingitsuitableforvarious
applications[28].
RheinmetallDefence:RheinmetallDefenceoffers
theSkysweepsystem,whichusesradartodetect
andtrackdrones.Thesystemiscapableof
detectingdronesatarangeofupto10km,making
itanidealsolutionforprotectinglargeareas[29].
Whencomparingthesesolutions,itʹsessentialto
considerfactorssuchasdetectionrange,accuracy,
cost,andeaseofuse.Somesolutions,suchasDJI
AeroscopeandRaytheonʹsDroneDefender,are
designedforspecificapplicationsandmaynotbe
suitableforallscenarios.Othersolutions,suchas
Dedroneʹsplatform,offeramorecomprehensive
approachthatintegratesvariousdetectionsensorsto
provideacompleteairspacesecuritysolution.The
choiceofananti‐dronesystemultimatelydependson
thespecificneedsoftheuser,suchasthesizeofthe
areatobeprotected,thetypeofdronethreat,andthe
budgetavailable.
AsexamplesofPolishvendors,wemayindicate:
HertzSystems:Thiscompanyprovidesarangeof
anti‐dronesystems,includingtheDroneBlocker,
whichusesradiofrequencyjammingtodisable
drones,andtheDroneTracker,whichusesacoustic
andelectromagneticsensorstodetectandtrack
drones.Thecompanyʹssystemsareusedtoprotect
criticalinfrastructure,suchasairportsandpower
plants[17].
APSSystems:Itoffersarangeofanti‐drone
solutions,includingtheSkyWall100,ahandheld
devicethatusesanettocapturedrones,andthe
SkyWall300,afixedinstallationthatcancapture
dronesatlongerranges.Thecompanyʹssystems
aredesignedforuseinurbanenvironmentsand
canbeintegratedwithothersecuritysystems[12].
BothHertzSystemsandAPSSystemsprovide
innovativesolutionstodetectandmitigatedrone
threats.However,thechoiceofasystemultimately
dependsonthespecificneedsoftheuser,suchasthe
sizeoftheareatobeprotected,thetypeofdrone
threat,andthebudgetavailable.Asthethreatof
droneincursionscontinuestogrow,itʹsessentialfor
organizationstoinvestinreliableandeffectiveanti‐
dronesystemstoensurethesafetyofcritical
infrastructureandpersonnel.
6 SUMMARY
Theeventsofrecentyears,especiallyarmedconflicts
andterroristattacks,showthatthenumberof
incidentswithintentionaldestructiveuseofdronesis
increasing.Incidentsofunintentionalharmfuldrone
activityarealsofrequent.Thisresultsinpotential
threatstohumanhealthandlifeandtothecritical
infrastructurefacilitiesofcountries.Thesetrendsare
triggeringthedynamicdevelopmentofC‐UAS
systems,whichareusedtoprotectmilitaryunits,
airports,publicfacilities,orthecountryʹscritical
infrastructure.Thesurveyofanti‐dronesolutionsis
showninthispaper.Wewanttocontinueresearchin
designingandimplementingC‐UASsforthe
protectionofmilitaryunits,stateadministration,and
criticalinfrastructurefacilities.Wealsoplanto
considerinourfutureworksaspectsofcommandand
controlofC‐UAS.
ACKNOWLEDGMENT
ThisworkwasfinancedbytheMilitaryUniversityof
Technologyunderprojectno.UGB/22‐863/2023/WATon
‘Moderntechnologiesofwirelesscommunicationand
emitterlocalizationinvarioussystemapplications’.
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... The identification component of the anti-drone system is responsible for determining the specific characteristics and capabilities of the detected drones. This subsystem uses a combination of signal analysis, visual identification, behavioral analysis, acoustic identification, and electro-optical identification techniques to accurately identify drone models, their operators, and their intended purposes [113]. Table 11 shows the various identification methods, their working principles, capabilities, limitations, and ranges. ...
... By integrating and synthesizing data from various sources, the system can generate a more accurate and reliable picture of the airspace, improving the overall effectiveness of the anti-drone measures. By effectively managing and processing data, anti-drone systems can provide accurate and timely information, enabling operators to make informed decisions and respond effectively to potential threats [113]. The implementation of robust data handling protocols is crucial for the successful operation and deployment of anti-drone systems. ...
Nation’s Defense: A Comprehensive Review of Anti-Drone Systems and Strategies
Article
Full-text available
  • Jan 2025
The rapid advancement of drone technology has revolutionized both the civilian and military sectors, enabling a wide range of applications, from commercial deliveries to battlefield reconnaissance. However, the misuse of drones has introduced significant security threats, necessitating the development of robust anti-drone systems. This paper provides a comprehensive review of anti-drone technologies and defense strategies. It explores the rise of drones, their dual-use potential, and the growing need for countermeasures. The study examines the evolution of anti-drone technologies, classifies existing systems, and analyzes case studies of drone-related incidents. It also highlights the correlation between drone threats and the use of anti-drone solutions. Furthermore, this review differentiates between military and civilian defense frameworks, emphasizing the impact of drone misuse on national security and public safety. This study discusses current defense strategies, technological challenges, and complex regulatory landscapes. Finally, it outlines future directions for developing and deploying anti-drone technologies, aiming to raise awareness among policymakers and technologists about the critical role that these systems play in national defense.
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... • Passive optical imaging technology [17]: ...
... Remote Sens. 2025,17, 165 ...
Research on Cam–Kalm Automatic Tracking Technology of Low, Slow, and Small Target Based on Gm-APD LiDAR
Article
Full-text available
  • Jan 2025
With the wide application of UAVs in modern intelligent warfare as well as in civil fields, the demand for C-UAS technology is increasingly urgent. Traditional detection methods have many limitations in dealing with “low, slow, and small” targets. This paper presents a pure laser automatic tracking system based on Geiger-mode avalanche photodiode (Gm-APD). Combining the target motion state prediction of the Kalman filter and the adaptive target tracking of Camshift, a Cam–Kalm algorithm is proposed to achieve high-precision and stable tracking of moving targets. The proposed system also introduces two-dimensional Gaussian fitting and edge detection algorithms to automatically determine the target’s center position and the tracking rectangular box, thereby improving the automation of target tracking. Experimental results show that the system designed in this paper can effectively track UAVs in a 70 m laboratory environment and a 3.07 km to 3.32 km long-distance scene while achieving low center positioning error and MSE. This technology provides a new solution for real-time tracking and ranging of long-distance UAVs, shows the potential of pure laser approaches in long-distancelow, slow, and small target tracking, and provides essential technical support for C-UAS technology.
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... UAVs have been rapidly developed and been adopted across sectors such as photography, surveying, and logistics [1]. Despite European regulations [2], non-compliance remains common, leading to numerous incidents. ...
Jamming and Spoofing Techniques for Drone Neutralization: An Experimental Study
Article
Full-text available
  • Dec 2024
This study explores the use of electronic countermeasures to disrupt communications systems in Unmanned Aerial Vehicles (UAVs), focusing on the protection of airspaces and critical infrastructures such as airports and power stations. The research aims to develop a low-cost, adaptable jamming device using Software Defined Radio (SDR) technology, targeting key UAV communication links, including geolocation, radio control, and video transmission. It applies jamming techniques that successfully disrupt UAV communications. GPS spoofing techniques were also implemented, with both static and dynamic spoofing tested to mislead the drones’ navigation systems. Dynamic spoofing, combined with no-fly zone enforcement, proved to be particularly effective in forcing drones to land or exhibit erratic behavior. The conclusions of this study highlight the effectiveness of these techniques in neutralizing unauthorized UAVs, while also identifying the need for future research in countering drones that operate on alternative frequencies, such as 4G/5G, to enhance the system’s robustness in evolving drone environments.
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... Effective traditional UAV detection approaches include radar systems [20,21], visual surveillance [22,23], radio frequency (RF) signals [24][25][26], and acoustic sensors [27]. According to [28], radio waves are used by radar systems to identify objects in the airspace. Visual detection uses image processing systems to detect and track UAVs based on their airframe features and overall appearance. ...
UAV Detection Using Reinforcement Learning
Article
Full-text available
Unmanned Aerial Vehicles (UAVs) have gained significant popularity in both military and civilian applications due to their cost-effectiveness and flexibility. However, the increased utilization of UAVs raises concerns about the risk of illegal data gathering and potential criminal use. As a result, the accurate detection and identification of intruding UAVs has emerged as a critical research concern. Many algorithms have shown their effectiveness in detecting different objects through different approaches, including radio frequency (RF), computer vision (visual), and sound-based detection. This article proposes a novel approach for detecting and identifying intruding UAVs based on their RF signals by using a hierarchical reinforcement learning technique. We train a UAV agent hierarchically with multiple policies using the REINFORCE algorithm with entropy regularization term to improve the overall accuracy. The research focuses on utilizing extracted features from RF signals to detect intruding UAVs, which contributes to the field of reinforcement learning by investigating a less-explored UAV detection approach. Through extensive evaluation, our findings show the remarkable results of the proposed approach in achieving accurate RF-based detection and identification, with an outstanding detection accuracy of 99.7%. Additionally, our approach demonstrates improved cumulative return performance and reduced loss. The obtained results highlight the effectiveness of the proposed solution in enhancing UAV security and surveillance while advancing the field of UAV detection.
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Bio-Inspired Electrostatic Detection Method for Threat Perception in Unmanned Platforms
Article
  • Jan 2025
Unmanned platforms have been widely adopted in both civilian and military contexts, achieving notable success. However, their energy resources, computational power, payload capacity, and cost are constrained and interdependent. These limitations prevent the integration of advanced threat detection systems, such as high-speed cameras and radar. Consequently, they are unable to detect high-velocity projectiles, significantly undermining their survivability in hostile environments. Developing low-power, cost-effective, and computationally efficient threat detection methods is of critical importance. This paper proposes a method for perceiving the flight trajectories of charged objects based on the principle of electrostatic induction. A physical model for electrostatic detection is established, and the effects of flight speed and trajectory on induction signals are analyzed. Inspired by sharks, a four-element array testing system with specific orientation and spatial distribution characteristics is designed. An indoor experimental setup is developed to simulate and test the flight of charged objects, generating a comprehensive dataset by varying parameters such as flight speed, incident angle, and testing distance. This dataset is then used to train a symbolic regression machine learning model, resulting in a mathematical model capable of predicting the incoming direction of charged objects with an error margin of less than 10 degrees. The model's generalization ability and the impact of the number of sensors are also discussed.
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IDENTYFIKACJA I NEUTRALIZACJA BEZZAŁOGOWYCH STATKÓW POWIETRZNYCH
Article
Full-text available
  • Dec 2023
Bezzałogowe aparaty latające już od XIX wieku zrewolucjonizowały prowadzenie walk i wojen. Kolejne lata przyniosły bardzo dynamiczny rozwój bezzałogowych statków powietrznych nie tylko w działaniach wojsk, ale także w sektorze komercyjnej i cywilnej. Zastosowanie nowoczesnych technologii kojarzone jest głównie z wprowadzeniem nowych możliwości do życia codziennego. Jednakże wiele z tych zalet może być przez człowieka wykorzystana w sposób nieodpowiedni, prowadząc tym samym do ujawnienia niedoskonałości maszyn. Łatwy dostęp oraz luki w prawie przyczyniły się do wykorzystywania bezzałogowych statków powietrznych w działalnościach przestępczych, a nawet terrorystycznych.
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Optimization of Anti-Drone Defense: Analyzing Non-Kinetic Gun Selection Using DIBR II-Grey MARCOS Methodology
Article
Full-text available
  • Aug 2024
The selection of appropriate anti-drone systems is critical for enhancing a military's defensive capabilities. With a range of non-kinetic anti-drone guns available, it is essential to identify the optimal system that meets specific military requirements. This study presents a comprehensive approach, combining Multiple Criteria Decision Making (MCDM) techniques to facilitate this selection process. The Defining Interrelationships Between Ranked Criteria II (DIBR II) method has been employed to determine and calculate the criteria weighting coefficients, while the Grey Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method, modified to utilize interval grey numbers, has been applied to rank the alternatives. The criteria weighting coefficients, defined by expert input, are aggregated using the Bonferroni mean. The proposed DIBR II-Grey MARCOS model is then subjected to a sensitivity analysis, which further validates the robustness of the selection process. A comparative analysis of results, based on the applied MCDM methods, underscores the efficacy of the proposed model. The findings demonstrate that this integrated model not only provides a reliable framework for selecting anti-drone guns but also offers a versatile tool for resolving other MCDM challenges across various domains. The study highlights the potential of this model for broader application in diverse operational environments, where complex decision-making is required. The combination of MCDM techniques and sensitivity analysis offers valuable insights into optimizing resource allocation, thereby enhancing strategic decision-making processes. The proposed model's adaptability and effectiveness suggest its significant potential for adoption beyond the military sector.
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Testing GNSS receiver robustness for jamming
Article
Full-text available
  • Dec 2023
Global Navigation Satellite Systems (GNSSs) providing positioning, navigation and synchronization, has become an important element of modern systems and devices that have a crucial impact on many economy branches and the life of an common person. Literature analysis and reports from recent armed conflicts show that the use of techniques for jamming and spoofing GNSS signals is becoming increasingly. This reduces the level of safety in transport and increases the risk of improper operation of GNSS-based systems, like cellular telephony or bank sector. This paper focuses on the methodology for testing the GNSS receiver robustness for jamming. For this purpose, a broadband jamming device was developed. ==================================================== Globalne systemy nawigacji satelitarnej (GNSS) zapewniając pozycjonowanie, nawigację i synchronizację, stały się istotnym elementem współczesnych systemów i urządzeń, które mają kluczowy wpływ na wiele gałęzi gospodarki i życie zwykłego człowieka. Analiza literatury oraz doniesienia z ostatnich konfliktów zbrojnych pokazują, że wykorzystanie technik zagłuszania i fałszowania sygnałów GNSS staje się coraz bardziej powszechne. To powoduje zmniejszenie poziomu bezpieczeństwa w transporcie oraz wzrost ryzyka niewłaściwego działania systemów opartych na GNSS. Ten artykuł poświęcony jest metodyce testowania odporności odbiornika GNSS na zagłuszanie. W tym celu opracowane zostało szerokopasmowe urządzenie zagłuszające.
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Survey on Anti-Drone Systems: Components, Designs, and Challenges
Article
Full-text available
  • Mar 2021
This paper presents a comprehensive survey on anti-drone systems. After drones were released for non-military usages, drone incidents in the unarmed population are gradually increasing. However, it is unaffordable to construct a military grade anti-drone system for every private or public facility due to installation and operation costs, and regulatory restrictions. We focus on analyzing anti-drone system that does not use military weapons, investigating a wide range of anti-drone technologies, and deriving proper system models for reliable drone defense. We categorized anti-drone technologies into detection, identification, and neutralization, and reviewed numerous studies on each. Then, we propose a hypothetical anti-drone system that presents the guidelines for adaptable and effective drone defense operations. Further, we discuss drone-side safety and security schemes that could nullify current anti-drone methods, and propose future solutions to resolve these challenges.
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Anti-Drone System with Multiple Surveillance Technologies: Architecture, Implementation, and Challenges
Article
Full-text available
  • Apr 2018
In recent years, drones have undergone tremendous development. Due to the low price and ease of use, drones have been widely utilized in many application scenarios, which potentially pose great threats to public security and personal privacy. To mitigate these threats, it is necessary to deploy anti-drone systems in sensitive areas to detect, localize, and defend against the intruding drones. In this article, we provide a comprehensive overview of the technologies utilized for drone surveillance and the existing anti-drone systems. Then we develop an anti-drone system at Zhejiang University, named ADS-ZJU, which combines multiple passive surveillance technologies to realize drone detection, localization, and radio frequency jamming. Furthermore, we discuss the challenges and open research issues in such a system.
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Laser Weapon Systems,” Lockheed Martin
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Lockheed Martin, "Laser Weapon Systems," Lockheed Martin, Apr. 19, 2022. https://www.lockheedmartin.com/enus/capabilities/directed-energy/laser-weaponsystems.html
Ranger R8SS-3D Air and Ground Drone Surveillance Panel Radar
  • Flir Teledyne
Teledyne FLIR, "Ranger R8SS-3D Air and Ground Drone Surveillance Panel Radar," Teledyne FLIR. https://www.flir.eu/products/ranger-r8ss-3d?vertical=surveillance+general&segment=surveillance (accessed Mar. 15, 2023).
Aaronia-AARTOS Drone Detection System
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Aaronia AG, "Aaronia-AARTOS Drone Detection System," 2022. [Online]. Available: https://downloads.aaronia.com/datasheets/solutions/dro ne_detection/Aaronia_AARTOS_Drone_Detection_Syste m.pdf
Sky-Net-Longbow+. Datasheet
  • Kirintec
Kirintec, "Sky-Net-Longbow+. Datasheet." [Online]. Available: https://www.kirintec.com/wpcontent/uploads/2021/01/Sky-Net-Longbow-Datasheet_A4.pdf
Best antidrone solution uses multilayer approach for complete solution
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Dedrone, "Best antidrone solution uses multilayer approach for complete solution." https://www.dedrone.com/products/counter-dronetechnology (accessed Mar. 12, 2023).
View All Counterdrone (C-UAS) Products,” AI-enabled Multi-Mission Solutions
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DroneShield, "View All Counterdrone (C-UAS) Products," AI-enabled Multi-Mission Solutions.
Counter Drone Technology
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D Fend Solutions, "Counter Drone Technology," D-Fend Solutions. https://d-fendsolutions.com/ (accessed May 02, 2023).
Military Radar Solutions: Hemispheric Coverage
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DRS RADA Technologies, "Military Radar Solutions: Hemispheric Coverage," DRS RADA Technologies. https://www.drsrada.com/radars (accessed Mar. 15, 2023).