Microwave Interferometric and Doppler Radar Measurements of a UAV

Jeffrey A. Nanzer1 and Victor C. Chen21ECE, Michigan State University

2Ancortek Inc.

Abstract—The first dual-mode measurements of the time-varying radial and angular velocity signatures of a UAV quad-copter are presented. Measured with a compact 24 GHz interfer-ometric radar, the signatures are measured at various observationangles relative to the UAV. It is shown that the signatures from theUAV at high grazing angles, when the radial velocity of the rotorblades relative to the radar is low, provide features related to therotation rate of the rotor blades in certain instances. Using bothDoppler and interferometric radar velocity measurements maytherefore provide a method of detecting and classifying UAVs.

I. INTRODUCTION

Unmanned aerial systems (UASs), commonly called un-manned aerial vehicles (UAVs), are becoming increasinglyubiquitous. While enabling new and useful remote sensing andmonitoring capabilities at low cost, no established trackingsystem is in place to monitor the movements of UAVs likethere exists for manned aircraft. Particularly in congestedenvironments like cities, it is important that methods exist toquickly detect and classify UAVs for safety reasons and fordeveloping tracking protocols.

Microwave radar provides a unique and useful method fordetecting and classifying platforms such as UAVs. With theability of microwave signals to propagate through clouds,dust, smoke, and other obscurants with no attenuation [1],radar enables all-weather detection and tracking capabilitiesand the ability to operate in conditions where optical andIR systems fail due to obscurants. In addition to the tradi-tional radar measurements of target range and angle, Dopplerradar processing enables unique classification abilities. Non-rigid objects, such as walking humans or UAVs with rotorblades, generate time-varying Doppler shifts on the returnsignal, which are directly linked to the physical motion ofthe target [2], [3]. These unique features can be used todetect and classify UAVs through the signatures generatedby the rotors, thereby providing a robust method of detectingUAVs. Microwave radar systems are furthermore becomingmore affordable as microwave technology improves, and asradars are increasingly developed and used for commercialpurposes such as automotive radar. As an additional benefit,radar signals can in some cases be processed in non-line-of-sight situations by leveraging multipath signal channels,thereby affording detection capabilities buildings.

Micro-Doppler signatures of non-rigid objects have beenshown to be particularly useful for the discrimination ofmoving objects and for classifying objects and activities.Discrimination of humans and non-humans and the classifi-cation of human activity has been shown to be feasible usingvarious forms of micro-Doppler signature classification [4]–[7]. One significant drawback of micro-Doppler measurements

stems from the inability of the radar to measure signatureswhen the radial velocity is low. Beyond an angle relativeto the radar of approximately 60 degrees, prior efforts havedemonstrated that the micro-Doppler signature of movingpeople is reduced in frequency significantly enough to preventaccurate classification [8]–[10]. In cases where the object ismoving tangentially to the radar, the motion relative to theradar is primarily angular, and thus a measurement of theangular velocity signature is necessary to enable classificationregardless of the trajectory of the object.

Direct measurement of the angular velocity of moving ob-jects can be accomplished using a two-element interferometricradar and cross-correlating the two signal returns [11]. Theinterferometer consists of two receiving antennas that arewidely separated. As an object passes through the interferencepattern of the interferometer, a frequency shift is generatedon the cross-correlated output signal that is directly propor-tional to the angular velocity of the object. Simultaneously,the output of one or both individual radar receivers can beprocessed to generate a traditional Doppler frequency signa-ture. Because the Doppler and interferometric modes respondto complementary velocity vectors of the object (radial forDoppler and angular for interferometric), a direct measurementof the motion of the object can be obtained regardless of themotion relative to the radar [12]. Furthermore, with sufficientdistortion mitigation, the measured signatures can be processedusing nearly identical classification algorithms as are used inmicro-Doppler classification [13].

In this paper, the first measurements of the time-frequencysignatures of the radial and angular velocity of a small UAVquadcopter are presented. Time-varying Doppler signatures ofUAVs have been measured previously [14], [15]; in this work,the first dual-mode measurements of the time-varying radialand angular velocity signatures of a small UAV is presented.The angular velocity measurement technique is first reviewedin the next section. Following this, the experimental setup us-ing a compact 24 GHz interferometric radar is described. Themeasured time-frequency signatures of a small UAV are shownfor the two Doppler modes as well as for an interferometricmode with antenna separation of 5.6λ for various observationangles. Two cases are presented: measurements where onlyone rotor blade is on the UAV, and measurements where allfour rotor blades are present. With a single rotor in the UAV,it is shown that when the radar is observing the UAV at ahigh grazing angle, where the relative velocity of the rotorblades is low, the angular velocity signature provides distinctfeatures that may be used for detection and classification.The multiple rotor blade measurements show that the time-

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Fig. 1. Geometry of the interferometric radar setup, showing the two receivingchannels.

frequency signature in all modes becomes more complex,and distinct features are less prominent. The interferometricmode, using a nonlinear correlation detection process, isparticularly impacted in the four rotor case by the increasein intermodulaton distortion.

II. RADAR ANGULAR VELOCITY MEASUREMENT

Measurement of the angular velocity of a moving object canbe accomplished directly using an interferometric radar withtwo receiving elements separated by a baseline D (see Fig.1). The received signals from the two receiving elements areprocessed by cross-correlation, which yields a signal responseof the form [3]

r(t) = |A|2 exp{j2πDλsin(ωt)} (1)

where A is the amplitude of the received signal (assumed tobe identical in both channels), λ is the wavelength of theradiated signal, and ω is the angular velocity of the object.The instantaneous frequency of the interferometer response,given by

fs =ωD

λcosθ, (2)

where θ = ωt, yields a quantity that is directly proportionalto the angular velocity of the moving object. Near broadside(θ � 1), the interferometric radar frequency shift can beapproximated by fs = ωD/λ. The response can then be givenby

r(t) = |A|2ej2πfst. (3)

This form is nearly identical to the Doppler frequency shiftproduced by the radial velocity of an object, however theinterferometric frequency shift measures a complementaryform of movement. Thus, combining the radial and angularvelocity measurements, the interferometric radar can providemeasurements of the velocity of an object regardless of thetrajectory relative to the radar. Furthermore, the two receivedsignals can be independently digitized, enabling simultaneousprocessing of the Doppler returns on both channels as well asthe interferometer return obtained by cross-correlating the twosignals.

Fig. 2. Experimental setup of the 24 GHz interferometric radar monitoringa small UAV quadcopter.

III. RADAR MEASUREMENTS OF A UAV QUADCOPTER

A. Experimental Setup

The UAV measurement setup is shown in Fig. 2. AnAncortek compact 24 GHz radar (www.ancortek.com) withtwo receiving channels was used for the measurement. The tworeceiving antennas were separated by a distance of 5.6λ. Thetransmitting antenna was located between the two receiversand transmitted a continuous-wave (CW) 24 GHz signal. 10dBi standard gain horns were used on the transmitter and thereceivers. The signals from the two receiving channels weredigitized separately so that both could be processed in Dopplermode individually, and so that they could be cross-correlatedand processed in interferometric mode as well. Processing wasaccomplished offline using Matlab.

The UAV used in the experiments was a SYMA X5SWQuadcopter Drone, with rotor blades that moved with ap-proximately 30-40 rotations per second. The UAV was heldstationary by securing it on a small platform, and the radarwas placed on a tripod and directed towards the UAV. Theorientation of the UAV was varied between three depressionangles: 0◦, or end-on, viewing the UAV from the side; 45◦; and90◦, viewing the UAV from a top-down point-of-view. Thisenabled the measurement of various relative velocity profilesdue to the rotor blades: end-on the radial velocity is at itshighest, while when viewed top-down the radial velocity is atits lowest. In the latter case in particular it is of interest todetermine the angular velocity signature of the UAV.

B. Single-Rotor Measurements

Measurements with only one rotor blade were first con-ducted. The measured results from the three observation anglesare shown in Figs. 3, 4, and 5. The signals from eachchannel were processed individually to generate the time-varying Doppler frequency shifts, and the two channels wereco-processed by cross-correlation to generate the time-varyinginterferometric frequency shift. Each signal was analyzedin the time-frequency domain using the spectrogram, whichis the magnitude of the short-time Fourier transform. The

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Doppler Channel 1

(a)Doppler Channel 2

(b)Interferometer

(c)

Fig. 3. Measured time-frequency signatures of a single rotor of the UAV at adepression angle of 0◦ (viewed end-on). (a) Doppler frequency signature ofChannel 1. (b) Doppler frequency signature of Channel 2. (c) Interferometricfrequency signature.

spectrogram shows the amplitude of the various frequencycomponents of the signal as a function of both time andfrequency.

It can be seen from the figures that the radial velocity signa-tures and the angular velocity signatures both provide usefulinformation related to the motion of the UAV rotor blades.Viewing at 0◦ (Fig. 3) the Doppler signatures show a strongpositive and negative bulk frequency shift at approximately±250 Hz; this is due to the simultaneously approaching and

Doppler Channel 1

(a)Doppler Channel 2

(b)Interferometer

(c)

Fig. 4. Measured time-frequency signatures of a single rotor of the UAVat a depression angle of 45◦. (a) Doppler frequency signature of Channel 1.(b) Doppler frequency signature of Channel 2. (c) Interferometric frequencysignature.

receding motion of the rotor blades. Periodic structures arealso seen that are related to the rotation rate of the blades:when the blade is oriented such that the incident radar signalis specular, a larger radar return is produced, creating ”flashes”in the signature.

Viewing the UAV at 45◦ shows similar signatures for bothradial and angular velocity modes. The signatures are prevalentat ±250 Hz are show a longer periodicity that when viewedend-on; this is likely due to the physical rotation of the rotor

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Doppler Channel 1

(a)Doppler Channel 2

(b)Interferometer

(c)

Fig. 5. Measured time-frequency signatures of a single rotor of the UAVat a depression angle of 90◦ (viewed top-down). (a) Doppler frequencysignature of Channel 1. (b) Doppler frequency signature of Channel 2. (c)Interferometric frequency signature.

blades producing a larger specular return in only half the cases- when the approaching blade is largely specular on the flatpart of the blade, the receding blade presents a narrow physicalsurface, significantly reducing the return.

When viewed at 90◦ (top-down), the radial signature showsa largely asymmetric signature, possibly due to the asymmetricshape of the rotor blades. The movement of the rotor bladesare largely angular, and the signature of the interferometricmeasurement mode reflects this with clear periodic features

that are particularly visible at 500 Hz. These features areproportional to the rotation rate of the blades.

C. Multiple-Rotor Measurements

Time-frequency measurements of the UAV when all fourrotor blades are used presents a more complex picture. Insteadof receiving radar signals scattered from one rotating object,the superposition of scattered signals from multiple rotatingobjects is received by the radar. While large-scale motionof the rotor blades is the same from one rotor to the next,there are differences at small scales that impact the radarmeasurement. The initial rotation phase of each rotor israndomly distributed, and the rotation rate of the rotors arenot phase-locked; therefore the superposition of the scatteredsignals from multiple rotor blades tends to add incoherently,degrading the strong signatures seen in the single rotor case.

Fig. 6 shows the Doppler and interferometric signatures inthe four-rotor case when viewing the UAV from a depressionangle of 0◦. Comparing this to Fig. 3, it can be seen thatthe prominently structured signature of the single-rotor caseis degraded. For the Doppler modes, the signal strength fallsroughly within the same frequency band as seen in the single-rotor case, but periodic time-frequency signature content isminimal. The interferometric mode shows a marked reductionis periodic time-frequency signature content; the primary rea-son for this degradation is the non-linear correlation processinherent in the interferometric mode. The multiple frequenciespresent in each four-rotor Doppler signal create a multitudeof intermodulation frequencies in the multiplication processthat spread over the entire signal bandwidth. Because of theincoherence of the signal returns from the multiple rotorblades, the resulting signals are further degraded.

Measurements of the four-rotor UAV at a 45◦ depressionangle, shown in Fig. 7, result in similar signature degradation.Comparing to Fig. 4, some of the prominent periodic signa-tures in the Doppler measurements are still partially visible,due to the blade flashes at specular angles, however these arenot as periodic due to the incoherence of the multiple rotorblades. The interferometric measurements shows a similardegradation as the 0◦ measurement.

When viewed at 90◦ (top-down), the measured signatures inthe four-rotor case (Fig. 8) show similar large-scale features asin the single-rotor case (Fig. 5), particularly the asymmetricfrequency content in the Doppler mode measurements. Theinterferometric signature is similarly degraded as in the othercases.

D. Potential for Classification

When viewing a UAV with a single rotor blade, the time-frequency signatures are distinct for both Doppler and inter-ferometric detection modes, and using both modes a usablesignature can be detected regardless of the angle of observa-tion. These features may potentially be used in detection andclassification algorithms to aid in the discrimination of UAVsand other airborne objects, such as birds, or possible in thediscrimination of different types of UAVs.

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Doppler Channel 1

(a)Doppler Channel 2

(b)Interferometer

(c)

Fig. 6. Measured time-frequency signatures of all four rotors of the UAV ata depression angle of 0◦ (viewed end-on). (a) Doppler frequency signature ofChannel 1. (b) Doppler frequency signature of Channel 2. (c) Interferometricfrequency signature.

When multiple rotor blades are in action, the incoherenceof the scattered signals degrades the periodic time-frequencysignatures in both modes. The interferometric processingnon-linearities are known to create challenges in multiple-frequency scattered signals, and various measures can be usedto mitigate the intermodulation distortion [13]. Of these tech-niques, resolving the individual rotors in range may be possibleat millimeter-wave frequencies, where waveform bandwidthsof hundreds of MHz can be implemented. The Doppler and

Doppler Channel 1

(a)Doppler Channel 2

(b)Interferometer

(c)

Fig. 7. Measured time-frequency signatures of all four rotors of the UAV at adepression angle of 45◦ (viewed end-on). (a) Doppler frequency signature ofChannel 1. (b) Doppler frequency signature of Channel 2. (c) Interferometricfrequency signature.

interferometric time-frequency signatures from each individualrotor can then be processed individually, potentially recoveringthe types of signatures shown in Section IIIB.

Despite the degraded signatures in the multiple rotor case,detection and classification may still be possible in someinstances; for example, in discriminating between single andmultiple-rotor UAVs the relative differences in the Dopplerand interferometric modes may provide a level of classifi-cation capability, even if a periodic signature is not visibly

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Doppler Channel 1

(a)Doppler Channel 2

(b)Interferometer

(c)

Fig. 8. Measured time-frequency signatures of all four rotors of the UAV at adepression angle of 90◦ (viewed end-on). (a) Doppler frequency signature ofChannel 1. (b) Doppler frequency signature of Channel 2. (c) Interferometricfrequency signature.

present. Simple features such as the bandwidth of the signatureover time may also provide a level of classification ability.Furthermore, the development of detection and classificationalgorithms using advanced feature extraction approaches canalso provide capabilities based on features that are not readilyapparent to the human eye.

IV. CONCLUSION

This paper presented the first experimental results of bothmicrowave and Doppler measurements of a small UAV cap-tured simultaneously in single and multiple rotor blade cases.When the signal from a single rotor blade can be measured,either on a single-rotor UAV or by separating the signals fromeach rotor in range, the interferometric measurement providesthe ability to detect a time-frequency signature at angles wherethe Doppler signature may be reduced, thereby creating the po-tential for detection and classification regardless of the viewingangle of the UAV. The ability to measure such signatures inadverse conditions may enable future detection and monitoringof UAVs for improved safety in congested environments asthe number of UAVs continues to increase. Future effortswill focus on classification algorithm development and signalprocessing approaches to mitigate the non-linear distortionproduced by the interferometric detection processing.

REFERENCES

[1] N. C. Currie and C. E. Brown, Principles and Applications of Millimeter-Wave Radar. Artech House, 1987.

[2] V. C. Chen, The Micro-Doppler Effect in Radar. Artech House, 2011.[3] J. A. Nanzer, Microwave and Millimeter-Wave Remote Sensing for

Security Applications. Artech House, 2012.[4] P. van Dorp and F. Groen, “Human walking estimation with radar,”

Radar, Sonar and Navigation, IEE Proceedings -, vol. 150, no. 5, pp.356–365, Oct 2003.

[5] F. Fioranelli, M. Ritchie, and H. Griffiths, “Classification of un-armed/armed personnel using the netrad multistatic radar for micro-doppler and singular value decomposition features,” IEEE Geoscienceand Remote Sensing Letters, vol. 12, no. 9, pp. 1933–1937, Sept 2015.

[6] B. alyan and S. Z. Grbz, “Micro-doppler-based human activity classi-fication using the mote-scale bumblebee radar,” IEEE Geoscience andRemote Sensing Letters, vol. 12, no. 10, pp. 2135–2139, Oct 2015.

[7] Y. Kim, S. Ha, and J. Kwon, “Human detection using doppler radarbased on physical characteristics of targets,” IEEE Geoscience andRemote Sensing Letters, vol. 12, no. 2, pp. 289–293, Feb 2015.

[8] D. Tahmoush and J. Silvious, “Angle, elevation, prf, and illuminationin radar microdoppler for security applications,” in Antennas and Prop-agation Society International Symposium, 2009. APSURSI ’09. IEEE,June 2009, pp. 1–4.

[9] M. G. Anderson, “Design of multiple frequency continuous waveradar hardware and micro-doppler based detection and classificationalgorithms,” Ph.D. dissertation, University of Texas at Austin, 2008.

[10] Y. Kim and H. Ling, “Human activity classification based on micro-doppler signatures using a support vector machine,” Geoscience andRemote Sensing, IEEE Transactions on, vol. 47, no. 5, pp. 1328–1337,May 2009.

[11] J. A. Nanzer, “Millimeter-wave interferometric angular velocity detec-tion,” Microwave Theory and Techniques, IEEE Transactions on, vol. 58,no. 12, pp. 4128–4136, Dec 2010.

[12] J. A. Nanzer and K. S. Zilevu, “Dual interferometric-doppler measure-ments of the radial and angular velocity of humans,” Antennas andPropagation, IEEE Transactions on, vol. 62, no. 3, pp. 1513–1517,March 2014.

[13] J. A. Nanzer, “Micro-motion signatures in radar angular velocity mea-surements,” in 2016 IEEE Radar Conference (RadarConf), May 2016,pp. 1–4.

[14] P. Molchanov, K. Egiazarian, J. Astola, R. I. A. Harmanny, and J. J. M.de Wit, “Classification of small uavs and birds by micro-dopplersignatures,” in 2013 European Radar Conference, Oct 2013, pp. 172–175.

[15] F. Fioranelli, M. Ritchie, H. Griffiths, and H. Borrion, “Classificationof loaded/unloaded micro-drones using multistatic radar,” ElectronicsLetters, vol. 51, no. 22, pp. 1813–1815, 2015.

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