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Key Technologies and System Trade-Offs forDetection and Localization of Amateur Drones

Mohammad Mahdi Azari, Hazem Sallouha, Alessandro Chiumento,Sreeraj Rajendran, Evgenii Vinogradov, and Sofie Pollin

Email: mahdi.azari@kuleuven.be

Abstract—The use of amateur drones (ADrs) is expectedto significantly increase over the upcoming years. However,regulations do not allow such drones to fly over all areas, inaddition to typical altitude limitations. As a result, there is anurgent need for ADrs surveillance solutions. These solutionsshould include means of accurate detection, classification, andlocalization of the unwanted drones in a no-fly zone. Inthis paper, we give an overview of promising techniques formodulation classification and signal strength based localizationof ADrs by using surveillance drones (SDrs). By introducinga generic altitude dependent propagation model, we showhow detection and localization performance depend on thealtitude of SDrs. Particularly, our simulation results show a25 dB reduction in the minimum detectable power or 10 timescoverage enhancement of an SDr by flying at the optimumaltitude. Moreover, for a target no-fly zone, the locationestimation error of an ADr can be remarkably reduced byoptimizing the positions of the SDrs. Finally, we conclude thepaper with a general discussion about the future work andpossible challenges of the aerial surveillance systems.

I. INTRODUCTION

Detecting the presence of amateur drones (ADrs) in a no-fly zone is an arduous task as the ADrs are usually smallobjects flying at low altitudes. These ADrs can pose greatsafety and security problems in critical locations such aspower plants, military zones, densely populated areas andprivate residences [1]. Such a variety of no-fly zone locationsrequires a flexible surveillance solution. The research com-munity is very active in developing techniques to determinethe location and subsequently track unidentified drones [2]–[5].

Current solutions can be divided into active methods,in which a ground infrastructure actively scans the no-flyzone for intruders using video or radar techniques [2,4], orpassive methods in which an ADr is detected by its RFtransmission or audio signature [3,5]. Active Radar-basedmethods require large mono- or multi- static RF nodes usedto scan the no-fly zone. Frequency sweeping is used to scanfor the presence of drones and classification techniques areemployed to determine the nature of the detected drone andto track it. Such solutions are very powerful but require thepurchase of large devices with fixed coverage radius and thesmall size of the ADrs poses great detection limitations [4].Solutions based on video detection require the presence ofeither distributed camera equipment or 360◦ video recordingdevices [2]. Video processing is then applied on the recorded

image to identify whether a drone has entered the protectedspace, the drone is then identified and tracked.

Passive methods, on the other hand, listen to either theADr emitted audio or to the transmitted RF signal (i.e.control or downlink to the ADr’s base station). Sound-based solutions are able to identify the presence and modelof commercial ADrs by listening for specific motor soundsignatures but require distributed microphone arrays to trackthe drone [3]. Passive RF solutions listen instead for theADrs downlink transmission of, for example, a video streamand are able to localize the drone by determining the sourcepoint of the transmission [5]. The main advantages anddisadvantages of such methods are listed in Table I.

The ADrs detection and tracking solutions describedabove assume the presence of a ground infrastructure. Hence,in densely built-up areas, the number of deployed sensornodes must be dramatically increased to maintain the re-quired sensor system performance in challenging propaga-tion environments. However, an alternative solution is todetect and localize the ADrs by using surveillance drones(SDrs) with passive RF sensing ability. Using SDrs flying athigher altitudes than the ADrs allows for a flexible solutionable to be deployed quickly, it can be used to cover a widerange of ground surfaces and subsequently, is able to detectand localize the ADrs with high accuracy due to betterpropagation conditions at high altitudes (i.e. higher signal-to-noise ratio and line-of-sight probability).

Received signal strength (RSS) localization has been usedsuccessfully for the positioning of ground nodes [6]. Agood link between the receiving sensor and the node to belocalized is necessary to allow RSS-based methods. In fact,it has been shown that the presence of an aerial based sensor,together with an appropriate channel model, can greatlybenefit the connection to and thus the localization of targetnodes [7,8]. The usage of aerial RF monitoring devices canthen make use of better channels between ADrs flying atlow altitudes and surveillance drones (SDrs) for detectionand localization to improve current passive RF solutions.

In this paper, we present a framework for the detection andlocalization of ADrs using SDrs. The proposed frameworkis based on the received signal by at least three SDrs,see Figure 1. As the SDrs are assumed to be flying athigher altitudes than the ADrs, this gives them a betterview of the no-fly zone and places them much further

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2TABLE I. Drone Detection Technologies

Technology Advantages Disadvantages

Video [2] Mature technologyAccessible equipment

Distributed high resolution camera network or3D cameras neededSubject to poor visibility problemsFixed installation

Audio [3]Cheap sensorsLimited processing necessaryAccessible equipment

Sensitive to ambient noiseLimited rangeDistributed microphone array necessaryLarge datasets necessary for trainingFixed installation

Radar [4] Easily installable

Either mono-static with limited resolution orMulti-static and distributedSmall drone cross-section can be challenging to detectExpensive

RF [5] Easily installableCheap sensors

Requires good SNR to perform detectionSusceptible to interferenceRange is limited by link quality

away from buildings and people. Moreover, they could alsobe tethered to guarantee increased safety and operated bycertified pilots or authorities. The intercepted drone needsto be first identified as such, then by employing a realisticpropagation model, the approximate location of the ADr isobtained by multilateration based on the RSS at the SDrs.

The main contributions of this work can be summarizedas follows:

• An overview of the passive RF scanning for detec-tion solutions, relying on recent innovations in deeplearning, as a possible solution for the detection andlocalization of an ADr entering the no-fly zone,

• a characterization of the propagation channel seen by anSDr in urban environments, necessary to determine thedensity of the surveillance sensors for meeting detectionand localization constraints,

• an investigation on the impact of SDr altitude on thecoverage area for ADr detection and the range of itsdetectable transmit power,

• a study of the optimal SDrs positioning for bettersensing and higher localization accuracy over a targetzone based on a range of possible ADr’s transmitpower,

• and an overview of the future research directions andchallenges.

II. PASSIVE RF SENSING, TECHNOLOGY DETECTIONAND LOCALIZATION

In this section, an overview of passive RF sensing andlocalization techniques is presented. For each task, weillustrate a promising solution: deep learning for detectionand RSS based distance estimation for localization. Analysesshow that detection and localization performance stronglydepends on the received signal strength.

A. Passive RF sensing and detection

A first step in the surveillance of ADrs is detectingtheir presence in the no-fly zone. When detection of smallADrs with low transmit powers is of interest, it is needed

to rely on a very dense infrastructure of RF sensors thatare constantly monitoring the radio spectrum. To facilitatedetection, we foresee passive RF sensing as a simple yetrobust monitoring technique to protect the target no-fly zone.Continuous passive radio spectrum monitoring should beenabled in SDrs to allow effective detection of ADrs. Forsimplifying the analysis, ADrs are assumed to have omni-directional antennas enabling a LoS component to the SDrsas shown in Figure 1. Accordingly, the ADr’s antenna gainis identical for any elevation angle θ.

A few recent approaches try to tackle the RF spectrummonitoring problem in a crowd-sourced fashion [9]. Wirelessspectrum sensing at high altitude is less challenging as moreLoS channels are available when compared to sensing atground stations. Even with a perfect RF spectrum scanningarchitecture in place, huge effort is involved in analyzing,detecting and locating transmissions or anomalies in thesensed RF spectrum. Automated systems should be in placeto detect authorized or unauthorized transmissions. The de-tected signals should be then classified to understand the typeof transmission. Subsequently, this technology classificationcan further aid signal power estimation and RSS basedlocalization algorithms.

Accurate technology classification can be achieved tosome extent using state-of-the-art (SoA) machine learningclassifier models [10,11]. To validate technology classifi-cation, a few deep learning based time domain modelsemploying convolutional neural networks (CNN) and longshort term memory (LSTM) units are tested. The deeplearning models take IQ samples as input giving out theprobability of the data belonging to a particular technologyclass. Analyses show that the proposed models [10,11]yields an average classification accuracy close to 90% for11 different technologies, at varying signal-to-noise ratio(SNR) conditions ranging from 0 dB to 20 dB, independentof channel characteristics. It was also noticed that mostof the technology classification algorithms including deeplearning solutions require an SNR above 0 dB for accurateclassification which is used as a required SNR threshold inour simulation.

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Fig. 1. Due to the high LoS probability PLoS at high altitudes, the use of SDrs guarantees higher SNR for better detection and lessshadowing for accurate localization compared to terrestrial surveillance. As the ADrs’ transmit power is unknown the estimated locationof the ADr is represented by a sphere of radius δ.

B. Localization challenges

The multilateration process is the most prominent methodfor accurately determining the position of a transmittingsource. It is basically a process that uses the estimateddistances from such source to at least three different re-ceivers in order to perform localization. Time-based or RSS-based techniques are used to estimate the distance betweena transmitter and a receiver [6]. Time-based techniquesestimate the distance by multiplying the time of flight bythe speed of light. However, defining the time of flight isthe bottleneck of these techniques as a very accurate timesynchronization is required between the transmitter and thereceivers. Certainly, such technique is not feasible for theSDrs since the time of flight cannot be accurately definedas there is no cooperation with the ADr.

In contrast to time-based techniques, RSS-based solutionsare known for their computational simplicity and for notrequiring any time synchronization. RSS-based techniquesestimate the distance by using a deterministic function thatrepresent RSS as a function of distance. In fact, assuming aknown transmit power, the path loss model is a well knownrepresentation for the RSS-distance relation. Generally, themajor issue limiting the accuracy of RSS-based techniquesis the presence of shadowing between the transmitter and thereceiver which causes either over- or under-estimation of thedistance. However, this drawback can be overcome by usingSDrs combined with their altitude optimization which isthoroughly discussed in the next sections. Another challengethat SDrs have to tackle is the unknown transmit powerof the ADrs (PTx) due to the use of different standard ofcommunication (LTE, WiFi, ...) or power control in the ADr.The unknown transmit power leads to an estimated location

with uncertainty. As shown in Figure 1 the uncertainty canbe modeled as a sphere of radius δ. This uncertainty canbe minimized by classifying the technology being used bythe ADrs due to the fact that known technologies havea standard range of transmit powers (e.g., wifi limited to20 dBm, LTE limited to 24 dBm in uplink and 43 dBm–48 dBm in downlink, etc). A thorough discussion of SDraltitude’s effect on the channel characteristics including pathloss and shadowing effects is presented in the followingsection.

III. WHY FLY HIGHER: A BETTER LOS EXPERIENCE

To analyze the deployment of SDrs over urban environ-ments, as our main focus, and to characterize the receivedSNR, a comprehensive understanding of the communicationlinks’ channel characteristics is needed. Although air-to-air(A2A) links are dominated by Line-of-Sight (LoS) propaga-tion, the impact of multipath fading due to ground/buildingsreflections cannot be ignored. In [12], the Rician modelwith an altitude-dependent K-factor was used to model A2Achannels. Naturally, the influence of LoS grows with theincreasing ADr’s altitude as well as the Doppler frequencydue to the higher relative velocity.

In general, a ground-to-air (G2A) link encounters obstruc-tions between the terrestrial and aerial nodes which limitsthe LoS and lowers the quality of the channel. Therefore,a G2A link represents a worst case scenario which occurswhen the ADrs fly in very low altitudes.

A G2A channel is observed to be significantly differentthan A2A and ground-to-ground (G2G) communication dueto the high impact of altitude on the channel parametersincluding path loss exponent, small-scale fading and shad-owing effect. To clarify this fact, we consider two extreme

4cases for a given ground terminal that aims to communicatewith a drone seen by an elevation angle of θ:

1) θ → 0: In this case, which is equivalent to h→ 0 (forr 6= 0), the channel behavior follows G2G modelswhere the presence of many obstacles results in adramatic drop for the received power. This significantpower decay is reflected onto the channel model byproposing a large path loss exponent α and severeshadowing and small-scale fading effects. It is worthnoting that for this case the channel between a trans-mitter and receiver is roughly always non-line-of-sight(NLoS) as the probability of line-of-sight (LoS) PLoSconverges to zero1. In fact the LoS probability PLoScan be obtained as follows [7]

PLoS(θ) =1

1 + a0 exp (−b0θ), (1)

where a0 and b0 are environment dependent constants.2) θ → 90o: In this case, which is equivalent to h→∞

(for r 6= 0), the probability of LoS PLoS converges toone and the channel adopts roughly free space char-acteristics. Accordingly, a lower path loss exponentand a lighter small-scale fading and shadowing effectsare experienced since the environment between thetransmitter and receiver becomes less obstructed [7].

The above-mentioned intuition encourages to model thedrone communication channel dependent on the elevationangle as this, easily observable variable, presents a strongcorrelation with the link quality. To this end, the authorsin [13] studied a statistical propagation model by con-sidering two major groups of received power and theirprobability of occurrence, namely LoS and dominant non-LoS (NLoS) components. This model captures differenturban environment properties and proposes a θ-dependentpath loss and shadowing prediction of the communicationchannel between a terrestrial and an aerial node.

To extend this G2A model we refer to the work pre-sented in [7,8] where we include the small-scale fading andelevation angle dependent path loss exponent. This modelunifies a widely used G2G channel model with that ofG2A that enables us to study the co-existence of droneswith the existing terrestrial networks. In the following, webriefly discuss the dependency of the main components tothe elevation angle:

• Path Loss: path loss exponent is linked to the LoSprobability PLoS in [7] by proposing a negative lineardependency as follows

α(θ) = −a1PLoS(θ) + b1, (2)

where a1 and b1 are environment dependent parameters.Such dependency, illustrated in Figure 2a for Urbanenvironment, is motivated by the fact that the path loss

1Please note that for a short distance between a transmitter and receiver,the LoS probability PLoS exponentially decreases as the link length in-creases. This impact, however, is approximately neglected for long G2Acommunication links.

exponent is proportional to the number of obstaclesbetween a transmitter and receiver. Accordingly, forlarger elevation angle the path loss exponent is smallerdue to the presence of less obstacles between a groundtransmitter and an aerial receiver. The reduction of pathloss in Figure 2b is due to a decrease in α(θ) and theincrease is because of an increase in the link length dwhile the altitude increases.

• Small-Scale Fading: a G2A link is likely to experienceLoS condition and hence Rician fading is an adequatechoice for such channel that reflects the combinationof LoS and multipath scatters [14]. In this model, thefading power is determined by the Rician factor Kcharacterized as the ratio between the power of LoSand multipath components. In fact, the Rician factorrepresents the severity of fading such that a smaller Kcorresponds to a more severe fading. Due to a higherLoS probability and the presence of fewer obstaclesand scatters at higher altitudes, the average Ricianfactor could be characterized as a function of θ, i.e.K = K(θ) [8]. Investigating a functional form for k(θ)at different urban environment is an open question.

• Shadowing: the shadowing effect is studied in [13]where a log-normal distribution is considered separatelyfor each LoS and NLoS component. The standarddeviation of each group σLoS(θ) and σNLoS(θ) is char-acterized using a negative exponential dependency withthe elevation angle in which a lower elevation angleand hence altitude leads to a larger variation around theaverage path loss. Following [13], the overall averageshadowing effect in the links can be represented by thestandard deviation written as

σ2(θ) = P2LoS(θ) · σ2

LoS(θ) + [1− PLoS(θ)]2 · σ2

NLoS(θ),(3)

which is illustrated in Figure 2b. From the figure, asthe drone goes higher the shadowing effect graduallydiminishes due to the presence of fewer obstaclesbetween the transmitter and receiver.

By relying on this altitude-dependent shadowing model,it becomes possible to determine the SDr detection coverageand localization accuracy as function of SDr height. Theseresults include an optimization of SDrs network in orderto provide larger coverage, to detect low power ADrs andsubsequently maximize the localization accuracy. Finally,please note that the above-mentioned channel characteristicsare environment dependent [13], however in the sequel wehave examined an Urban environment and focus on thedetailed analysis of the performance for this scenario only,rather than quantifying the values for different environments.

IV. SUPERIOR SURVEILLANCE DRONES

We deploy an SDr system for detection and localization,and study its performance as function of altitude. Thiswill give insights into the possibility and benefits of usingSDrs for ADrs surveillance. In this section we discuss theefficient positioning of the SDrs to optimally localize an

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ADr considering the scenario in which the ADr is veryclose to the ground. In the following, by considering anUrban environment, we link the altitude of the flying SDr toits coverage area, to the ADr’s transmit power, and finallyto the localization accuracy as the ultimate goal. Note thatthe detection of ADrs can be done using one SDr whereaslocalizing them in the no-fly zone requires the cooperationof all three SDrs.

A. A Target Control Zone

Considering a target control zone for an SDr flying ataltitude h, the fact that the ADr’s transmit power is unknownmeans that several flight levels have to be defined based onthe range of possible transmit powers rather than a singleoptimal altitude. In any case, in order to detect the drone,it should be within the coverage of the SDr. Figure 3aillustrates a lower bound of the range of ADr’s transmitpower that is detectable by an SDr at each altitude. Thislower bound is obtained by comparing the received SNRand a certain threshold. For instance, if the SDr flies at200 m above a target region of radius 500 m, only an ADrtransmitting higher than 0 dBm can be sensed within thewhole target zone. This figure also proposes an optimumaltitude at which the lowest possible transmit power isdetectable. As a matter of fact, if the SDr goes higher theLoS probability increases resulting in better channel qualityas explained in the previous section, however the link lengthalso increases deteriorating the channel quality due to pathloss. These opposite effects are balanced at the optimumaltitude shown in the figure. From this figure it can beseen that, as the target region becomes larger the minimumrequired transmit power of ADrs and the optimum altitude

of SDrs increases. Please note that if the ADr flies higherover the ground, then the link to the SDr will become LoSand hence the channel quality will increase resulting in alower detectable power and a higher classification accuracyof the technology used in the ADr.

B. Coverage Extension

In this subsection we present an efficient deployment of anSDr to maximize the covered region assuming a minimumADr PTx. Therefore, the result can be used to find the optimalnumber of surveillance drones in order to cover a largertarget region. Figure 3b illustrates the impact of altitudefor different minimum transmit powers. The figure showsthat as the drone goes higher the coverage increases suchthat at an optimum altitude the coverage is maximized. Forinstance, an SDr can fly at an altitude of 900 m to maximizethe region of control assuming a minimum ADr’s transmitpower of -10 dBm. More technical discussion for the optimaldeployment of the drones and the trade-offs can be found in[7,8,15].

C. Localizing Amateur Drones

Once the ADr has been detected and its RSS has beenmeasured, we can proceed to estimate its location. In oursimulation we assume 3 SDrs positioned as vertices ofan equilateral triangle with sides of length l and equaladjustable altitude h. Each SDr has its own coverage radiusand is able to detect any ADr separately. The intersection ofthe three coverage areas produces the no-fly zone associatedwith the three SDrs combined.

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Fig. 3. (a) For a target zone indicated by its radius, the range of sensing powers are influenced significantly by the altitude of flying SDrs.(b) For an assumed minimum ADr’s transmit power an SDr can fly at an optimum altitude to extend its monitoring region compared toground surveillance.

Unlike the G2G RSS-based localization scenarios whichsuffer from shadowing as the main source of error, intro-ducing the altitude in G2A and A2A scenarios as a thirddimension promises to overcome the shadowing due to thehigh PLoS and hence minimize the localization error. In thissubsection, we present the localization of ADrs by usingSDrs as shown in Figure 1. As illustrated in the figure, wetarget localizing any ADr that would fly in the defined no-fly zone by means of the RSS-based distance estimation. Inorder to define the position of the ADr, distances to threedifferent SDrs need to be estimated using RSS. However, asthe transmit power from the ADr is unknown, one can definea constant P(min)

Tx ≤ C ≤ P(max)Tx that represents the possible

transmit power. Accordingly, the estimated distance betweenthe ADr and the SDr is equal to d+ δ where δ is a constantthat represents the uncertainty due to the unknown transmitpower. Subsequently, after estimating the distance betweenthe ADr and three SDrs, we will end up with a sphere ofradius δ in which the ADr is located. A representation ofδ is shown in Figure 4a which can cause under- or over-estimation of the distance.

As shown in previous sections, the altitude of the droneshas a significant influence on the model representing thereceived power and hence, the accuracy of the estimatedlocation of the ADr. In fact, for any SDr, the variationsof both the the path loss exponent and shadowing standarddeviation with the altitude are modeled based on statisticalrepresentations given in equations (2) and (3), respectively.The shadowing effect at low values of h will be relativelyhigh causing large localization errors. As h increases, the

shadowing effect will decrease, concurrently, the resolution2

will also decrease. In the case of low resolution, any smallvariation will bring a large localization error. Therefore,based on the behavior of the path loss model, the existenceof an optimal altitude is investigated as shown in Figure 4.Our results show that for a targeted no-fly zone, an optimumaltitude h minimizing the localization error is present. Thisoptimal altitude is shown in Figure 4a. As one can see,an optimal altitude for minimizing the estimated locationerror exists at h = 800 m for a coverage radius of 1000 m.Moreover, it can be seen that when the SDrs are at the lowaltitude of < 800m, the RSS is exposed to high shadowingresulting a relatively high estimation error assuming thesame coverage. On the other hand, high altitude meanslow resolution in the RSS-distance curve where any lowshadowing causes a relativity high estimation error. It isworth noting that, the coverage radius here can be connectedto the PTx, since the lower the PTx of ADr, the smaller thecoverage radius of the SDr at which ADrs are detectable.

In addition to the altitude dependence, the localizationaccuracy is affected by the distance l between the SDrs.Figure 4b illustrates the location estimation error againstthe distances l under an assumption of the equidistant SDrspositioning. As shown in the figure, when l is relativelysmall, the intersection zone between the estimated distancesof the three SDrs will be large leading to a relativelyhigh localization error. Moreover, increasing l improvesthe localization accuracy where an accuracy of 100 m isachieved at l = 300 m for the same h of 1000 m. Eventually,

2The resolution is the ability to distinguish two different distances fromtwo different RSS measurements

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Fig. 4. Localization of ADrs is affected by the altitude h and the distance l between SDrs: (a) Localization error as a function of theSDrs altitude with the uncertainty constant δ. (b) Localization error as function of distance l between SDrs.

as l keeps increasing, the distance will become too large togive an acceptable resolution for distance estimate, makinga low localization error impossible.

V. RESEARCH DIRECTIONS AND FUTURE WORK

Gradually, drones are gaining a lot of momentum asan ingrained part of future wireless technologies makingsurveillance of amateur drones very important. In this sectionwe address some open problems as future work towardsauxiliary reliable surveillance of ADrs.

A. Experimental channel models validationAlthough, some interesting works have been carried out to

model the G2A channel characteristic using measurementsor simulations, they only considered either non-urban en-vironments or relatively high altitudes. In fact, a genericchannel model that reflects characteristics of both A2Aand G2A channels and dependency on the elevation angleincluding the shadowing effect is required. To this end, ameasurement campaign in order to validate the differentproposed simulation-based models and define a generic oneis still one of the future plans.

B. Tracking of ADrsIn this work we consider localizing the ADrs at a given

time, however, as the ADrs are mobile in nature, localiza-tion must be considered over time in order to track theADrs’ movement. To this end, the processing time betweendetecting the ADr, defined a valid RSS measurement andestimating its position need to be minimized. In fact, thistime has to be lower than the time needed for the ADr tomove a certain distance that defined the required localizationaccuracy.

C. RF fingerprinting for ADr identification

Even though we have explained various state-of-the-arttechniques for ADr detection and technology classification,identifying a drone with passive RF monitoring is quite chal-lenging. For detecting ADrs, temporal and spatial wirelesstransmission statistics should be derived from the receiveddetected signal which should be further associated with aparticular drone. Detailed studies should be done to enableand improve RF fingerprinting for drone surveillance. Webelieve drone identification with low false identificationrates can be achieved by combining RF localization andfingerprinting.

D. Mobility aided surveillance

In order to decrease the number of surveillance drones ina given region, a mobile drone can be deployed. A mobiledrone will not only increase the number of accessible groundstation but it can also localize other drones by collectingmeasurements at different locations. However, the cost to bepayed is the delay although the speed and the trajectory ofmovement can be optimized for the minimum delay.

E. Network lifetime

In order to increase network lifetime we need to optimizethe power consumption. Since mechanical power is the mainsource of the energy cost, power consumption can be im-proved by optimizing the flying trajectory. Considering thetrajectory of movement, the duration of communication, thepayload weight and the battery size, the lifetime of a droneis limited to less than one hour. Therefore, an alternativesolutions such as solar cells (e.g., Facebook Aquila Drone)

8for providing the required mechanical energy is of highimportance. Nevertheless, adding solar cells also increasesthe rate of energy consumption as more weight has to becarried on the drone.

VI. CONCLUSION

In this article we have considered a network of SDrsaiming to sense the presence of ADrs and localize themover a no-fly zone by means of the passive RF sensing.An overview of the state-of-the-art RF passive sensing anddetection showed that an SNR above 0 dB is required foraccurate detection of ADrs. However, using aerial basedsensors improves the received SNR due to better channelsbetween ADrs flying at low altitudes and SDrs. Therefore,the characteristics of the channel between SDrs and ADrshave been thoroughly studied considering the worst casescenario at which the ADrs are 2 m above the ground. Ourresults show that a tenfold increase of the coverage radiusand a 25 dB reduction of the minimum detectable powercan be achieved by flying the SDrs at the optimal altitude.Furthermore, it has been shown that 4 times better localiza-tion accuracy is gained by careful optimizing the altitude ofthe SDrs for a given no-fly zone. We expect that academicand industrial research and development activities can usethe proposed framework to address the drone surveillancechallenges introduced in the paper.

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Mohammad Mahdi Azari(mmahdi.azari@gmail.com) has received theB.Sc. and M.Sc. degrees in electrical andcommunication engineering from Universityof Tehran, Tehran, Iran. Currently he is aPh.D. candidate at the Department of ElectricalEngineering, KU Leuven, Belgium. His mainresearch interests include unmanned aerialvehicle (UAV) communication and networking,modeling and analysis of cellular networks, andmmWave communication. He has also received

Iran’s National Elites Foundation (INEF) Award.

Hazem Sallouha (hazem.sallouha@kuleuven.be)received the B.Sc. degree in electrical engineeringfrom Islamic University of Gaza, Palestine, in2011, the M.Sc. degree in electrical engineeringmajoring in wireless communications from JordanUniversity of Science and Technology, Jordanin 2013. Currently he is a Ph.D. candidate atthe Department of Electrical Engineering, KULeuven, Belgium. His main research interestsinclude localization techniques, communicationswith unmanned aerial vehicles, Internet of things

networks and machine learning algorithms for localization.

Alessandro Chiumento(alessandro.chiumento@esat.kuleuven.be)received his Ph.D. degree in cellular networkmanagement from Imec, Leuven, Belgium, in2015. He is currently with the Department ofElectrical Engineering, Katholieke UniversiteitLeuven. His research interests include massivemachine-to-machine communication, channelprediction, very dense networks, and theapplication of machine learning to theoreticalproblems in telecommunication and information

management.

9Sreeraj Rajendran received his Masters degreein communication and signal processing fromthe Indian Institute of Technology, Bombay, in2013. He is currently pursuing the Ph.D. degreein the Department of Electrical Engineering, KULeuven. Before joining KU Leuven, he workedas a senior design engineer in the baseband teamof Cadence and ASIC verifidetecation engineer inWipro Technologies. His main research interestsinclude machine learning algorithms for wirelessand low power wireless sensor networks.

Evgenii Vinogradov received the Dipl. Engineerdegree in Radio Engineering and Telecommunica-tions from Saint-Petersburg Electrotechnical Uni-versity (Russia), in 2009. After several years ofworking in the field of mobile communications, hejoined UCL (Belgium) in 2013, where he obtainedhis Ph.D. degree in 2017. His doctoral researchinterests focused on radio propagation channelmodeling. In 2017, Evgenii joined the electricalengineering department at KU Leuven (Belgium)where he is working on wireless communications

with UAVs.

Sofie Pollin Sofie Pollin obtained her PhD atKU Leuven in 2006. She continued her researchon wireless communication at UC Berkeley. InNovember 2008 she returned to imec to becomea principal scientist in the green radio team.Since 2012, she is tenure track assistant professorat the electrical engineering department at KULeuven. Her research centers around NetworkedSystems that require networks that are ever moredense, heterogeneous, battery powered and spec-trum constrained.