A Review on Low-Cost Microwave Doppler Radar Systems for Structural Health Monitoring

sensors

Review A Review on Low-Cost Microwave Doppler Radar Systems for Structural Health Monitoring

Davi V. Q. Rodrigues * and Changzhi Li

Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA; [email protected] * Correspondence: [email protected]

Abstract: Portable, low-cost, microwave radars have attracted researchers’ attention for being an alternative noncontact solution for structural condition monitoring. In addition, by leveraging their capability of providing the target velocity information, the radar-based remote monitoring of complex rotating structures can also be accomplished. Modern radar systems are compact, able to be easily integrated in sensor networks, and can deliver high accuracy measurements. This paper reviews the recent technical advances in low-cost Doppler radar systems for phase-demodulated displacement measurements and time-Doppler analysis for structural health information, including digital signal processing and emerging applications related to radar sensor networks.

Keywords: Doppler radar; displacement measurement; low-cost radar; micro-Doppler; portable radar; remote sensing; radar network

1. Introduction Citation: Rodrigues, D.V.Q.; Li, C. A Radars have been employed since 1940s as surveillance systems [1]. In the past, Review on Low-Cost Microwave they were mainly used in the military due to high costs and bulky sizes. Thanks to Doppler Radar Systems for Structural the fast and signiﬁcant advancements in the semiconductor industry, radars are being Health Monitoring. Sensors 2021, 21, miniaturized and assembled on printed circuit boards (PCB) or even integrated into a single 2612. https://doi.org/10.3390/ chip with antenna-on-chip/antenna-in-package technologies [2–5]. In the last decades, s21082612 portable short-range radars have been investigated for human and animal vital signs, remote voice recording, gait analysis, fall detection, gesture characterization, occupancy Academic Editor: Jochen Moll sensing, and security applications [6–19]. Biomedical Doppler radars have also been employed in cancer radiotherapy for respiratory gating and tumor tracking for motion- Received: 28 February 2021 adaptive radiotherapy [20–22]. In addition, short-range radars can be used to provide Accepted: 6 April 2021 Published: 8 April 2021 time-frequency analysis of microwave signals backscattered by rotating structures such as wind turbines [23–28].

Publisher’s Note: MDPI stays neutral Structural health monitoring (SHM) is the process of continuous observation of a with regard to jurisdictional claims in structure or mechanical system using one or multiple sensors to provide information published maps and institutional afﬁl- about its true capacity, which is altered by age and/or accidental damage. By sensing low- iations. frequency small-amplitude mechanical vibration or deﬂection of structures, radars may also be successfully employed for the SHM of infrastructures. As the aging of worldwide infrastructures raises concerns and the appearance of new structures forces researchers and engineers to look for alternative solutions for SHM measurements and the improvement of existing ones, private industries and government organizations demand technologies that Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. are able to detect structural damage at the earliest possible time to avoid life-threatening This article is an open access article situations and economical losses. distributed under the terms and Several technologies targeting SHM have been proposed in the past decades. Ac- conditions of the Creative Commons celerometers are commonly used to evaluate the infrastructure health condition and to Attribution (CC BY) license (https:// extract damage-sensitive features because they are relatively cost-effective and can be creativecommons.org/licenses/by/ readily instrumented. Nevertheless, the double integration of the acceleration data makes 4.0/). displacement measurements susceptible to integral drift errors [29]. Strain sensors, laser

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displacement sensors, and vision-based systems are also representatives of nonintrusive solutions for SHM applications. However, these sensors present practical limitations. The measurements retrieved by strain gauges are sensitive to temperature variations, and they may need periodical calibration. Laser displacement sensors are sensitive to the measurement range and the structure’s surface condition. Vision-based systems demand large data storage, high computational load for image recognition and are not robust against ambient light. On the other hand, radars can make use of different types of waveforms for the targeted application. Continuous-wave (CW) radars have simple architecture, allowing for easier integration and lower power consumption, which makes them appealing for portable applications. In a basic CW radar system, the radio frequency (RF) wave is radiated, and an echo returns after being backscattered by a surrounding target. If the target is moving, a shift in the received radar signal due to the Doppler effect is observed. The target’s range can only be assessed by modulated CW radars. Modulated CW radars such as frequency-modulated continuous-wave (FMCW) radars and stepped-frequency continuous-wave (SFCW) radars are popular candidates for applications that require range and/or Doppler information. In contrast, unmodulated CW radars, widely known as Doppler radars, do not have range discrimination capability. Nonetheless, Doppler radars can measure time-varying small-amplitude periodical motion with high accuracy [2]. The most common radars employed on structural condition monitoring are the ground- based interferometric (GBI) radars. Their use for SHM applications has a relatively long history. The inspiration for the utilization of GBI radars on the monitoring of structures such as building or bridges came from the success of spaceborne synthetic aperture radar (SAR) radar systems, which operate at high orbits and detect ground changes based on the phase information of radar images [30,31]. During onsite testing, these sensors are typically mounted on a tripod and pointed towards bridges, landslides, towers, and dams [32–55]. The main difference between GBIR and other portable radar sensors is their relatively large detection range due to the use of bulky, high directivity antennas and waveguide-based components [38]. By transmitting and receiving electromagnetic waves at microwave frequencies, they can remotely detect small displacements of targets using the interferometric technique, and they are also able to distinguish the real displacement of targets of interest from clutter since the vast majority of GBI radar systems employs stepped-frequency continuous-wave (SFCW) or frequency-modulated continuous-wave (FMCW) radar sensors. These systems can operate without any angular resolution or with angular resolution obtained through the rotation of the radar or the movement of the radar along a linear mechanical guide. GBI radars are powerful tools on the estimation of vibration parameters of structures with large areas (bridges, mines, buildings). Bridge monitoring using portable GBI radars dates back to the 2000s [32]. The evaluation of the bridge displacement along the radial direction by an SFCW or FMCW radar systems starts by choosing the range bin associated with different parts of the structure. After the selection of the range bin, the displacement of the target is recovered by demodulating the phase variations of the received signal during the detection time. Another important parameter of modulated wave radar is the range resolution, which is a function of the transmitted bandwidth and is the minimum distance to resolve two or more adjacent targets on the same bearing. Monostatic radars can only detect motion along the radial direction, and the movement of real large targets such as buildings or bridges consists of more than one component. To address the challenge of simultaneously measuring displacements along different directions, the most recent work on SHM based on GBI radars proposed a multi-monostatic 17.2-GHz FMCW radar for the remote monitoring of bridges [55]. They employed two different interferometric radars placed at different positions to measure two components of a bridge’s deck motion. The used radar system was a modiﬁed version of a multiple- input multiple-output (MIMO) GBI radar (IBIS-FM MIMO by IDS Company) that operated with two pairs of transmitting (Tx) and receiving (Rx) antennas (four possible baseband Sensors 2021, 21, 2612 3 of 22

channels). Only two channels were effectively used to retrieve the displacements at two different positions (23 m and 33 m away from the main radar) on the bridge. RF cables were utilized to connect the second pair of Tx/Rx antennas to the main radar system, allowing for the multi-monostatic radar architecture. The radar operated sequentially in a single channel modality, but the time duration between four acquisitions was relatively short (5–12 ms), especially for SHM applications. The cable loss and the time shifts were compensated by low noise amplifiers and by digital signal processing techniques, respectively. The authors chose the 127-m long Varlungo Bridge in Firenze, Italy, to conduct the full-scale experiments. The vehicular traffic provoked the bridge vibration, but a significant change on the displacement was only observed when a truck moved on the bridge. With this strategy, the authors were able to retrieve the displacement vector (y-z plane) and the natural frequencies for the two different radar targets. However, no discussion was made towards modal analysis measurements. In addition, the proposed method relies on choosing an appropriated distance between the main radar and the second pair of Tx/Rx antennas, which must be large enough to allow the evaluation of the vector displacement using the two motion components. The radar had achieved submillimeter accuracy during measurements in a controlled environment with an oscillating corner reflector placed 12.88 m away from the main radar and 7.33 m away from the second pair of antennas. Seismic accelerometer measurements (PCB 393B31 by PCB piezotronics) provided the ground truth. This review mainly focuses on recent advancements on portable, board-level Doppler radar for structural health monitoring. Interested readers on GBI radar-based SHM are encouraged to refer to papers cited in this section for more information on ground-based radar technologies and applications [32–55]. The remainder of this paper is organized as follows. Section2 addresses the theoretical formulation of low-cost Doppler radar, including the arctangent demodulation, the time-frequency analysis technique, and data processing techniques used for field SHM. The recent advancements on vibration based SHM using low-cost Doppler radar are described in Section3. In Section4, SHM based on time-frequency analysis is reviewed. Finally, the outlook for portable microwave radar sensing for SHM is discussed in Section5.

2. Theory of Low-Cost Doppler Radars for Structural Health Monitoring

Doppler radars emit a single-tone microwave signal of frequency ft. The reflected radio-frequency signal is frequency/phase-modulated due to the Doppler effect assuming that the target is moving. The microwave frequency associated with the translational speed v of a point-scatterer target is calculated as fr = ft(1 + v/c)/(1 − v/c), and can be easily retrieved by spectral analysis. If the target consists of several moving parts, the contributions of various point-scatterers produce micro-Doppler signatures, which can be exploited for the extraction of other parameters not related to the main target’s movement [23]. For example, when one does hand gestures in front of a radar sensor, not only will the Doppler frequencies associated with the hand’s movement be captured, but also the frequencies associated with the movements of other parts of the human body such as the arm and the elbow [17,18]. Assuming that a simple homodyne Doppler radar illuminates a structure comprised of rotating blades, as seen is Figure1, the reflected signal is phase-modulated by their movement. The returned echoes are mixed with the same transmitted signal by a quadrature- mixer to generate in-phase (I(t)) and quadrature-phase (Q(t)) baseband responses. The analytic form of the amplitude-normalized baseband signal can be cast as sb(t) = I(t) + jQ(t) K = ∑ exp(−j4πRk(t)/λ), where the time-varying distances between the radar sensor and k=1 each of the K scatterers are Rk(t) and K is the number of scatterers. Taking into account that the blade’s surface backscatters significant radar signals in the perpendicular direction, the distance between each scatterer and the radar makes the total received signals be Sensors 2021, 21, x FOR PEER REVIEW 4 of 23 Sensors 2021, 21, 2612 4 of 22

direction, the distance between each scatterer and the radar makes the total received sig- coherentlynals be coherently added. Therefore,added. Ther itsefore, micro-Doppler its micro-Doppler signatures signat willures have will the have form the of form ﬂashes of in theflashes time-Doppler in the time-D mapsoppler [28]. maps [28].

FigureFigure 1.1. Block diagram for for structural structural he healthalth monitoring monitoring (SHM) (SHM) based based on on low-cost low-cost Doppler Doppler radars radars using time-frequency analysis of micro-Doppler signatures. using time-frequency analysis of micro-Doppler signatures.

However,However, unique signatures signatures with with sinusoidal sinusoidal forms, forms, called called halos, halos, are arealso alsoobserved observed in inthe the spectrograms spectrograms for rotating for rotating structures structures with translational with translational velocity velocity equals to equals zero (𝑣 to = zero0) ([28].v = 0)The [28 blade]. The tips blade should tips be should considered be considered as point-scatter as point-scatterer targets. Considering targets. that Consider- R0 is the nominal distance between the radar and the rotation center, r is the blade radius, Ω is ing that R0 is the nominal distance between the radar and the rotation center, r is the blade radius,angular and velocity,Ω is the the angular distance velocity, between the the distance blade tips between can be themodeled blade tipsas R cantip = R be0 + modeled rsin(Ωt). as For simplicity, the initial rotation angle on the rotation plane was suppressed. By applying Rtip = R0 + rsin(Ωt). For simplicity, the initial rotation angle on the rotation plane was suppressed.the first Byderivative applying theto firstthe derivativephase history to the of phase the historycomplex of thebaseband complex signal baseband stip(t signal) = exp(−𝑗4π𝑅 (𝑡)/λ) s t −j πR, the(t) time-varyingλ Doppler frequency of the blade tip is expressed as tip( ) = exp 4 tip / , the time-varying Doppler frequency of the blade tip is ex- fD(t) = −2/λ𝑅 (𝑡) = −2r0 Ωcos(Ωt)/λ, which demonstrates that the micro-Doppler signa- pressed as fD(t) = −2R tip(t)/λ = −2rΩcos(Ωt)/λ, which demonstrates that the micro- Dopplertures of the signatures blade tips of are the theoretically blade tipsare sinusoidal. theoretically In fact, sinusoidal. they appear In as fact, quasi-sinusoidal they appear as quasi-sinusoidalsignatures in the signaturestime-Doppler in the maps time-Doppler due to the short-range maps due to detection the short-range [28]. detection [28]. OnOn the other hand, if the the targ targetet presents presents a a periodical periodical line linearar motion, motion, its its displacement displacement alsoalso provokesprovokes the phase-modulation of of the the previously previously transmitted transmitted radar radar signal. signal. Figure Figure 2 2 illustratesillustrates thethe blockblock diagramdiagram forfor thethe remoteremote vibrationvibration monitoringmonitoring basedbased onon aa DopplerDoppler radarra- system.dar system. Again, Again, the backscatteredthe backscattered reflected reflected signal signalR(t) R is(t mixed) is mixed with with the samethe same transmitted trans- signalmittedT signal(t) by T a(t quadrature-mixer) by a quadrature-mixer to generate to generate in-phase in-phase and and quadrature-phase quadrature-phase baseband baseband responses. After the correct condition of the DC offsets, amplitude normalization, responses. After the correct condition of the DC offsets, amplitude normalization, and and circle fitting for the recorded I/Q baseband signals, the detected displacement can be circle fitting for the recorded I/Q baseband signals, the detected displacement can be estimated by nonlinear phase-demodulation algorithms, which combine both the in- estimated by nonlinear phase-demodulation algorithms, which combine both the in-phase phase and quadrature-phase signals to retrieve the changes in the phase angles of consec- and quadrature-phase signals to retrieve the changes in the phase angles of consecutive utive sampling points. Assume that the structure vibrates with a time-varying displace- sampling points. Assume that the structure vibrates with a time-varying displacement x(t) ment x(t) = msin(ω0t), where m is the amplitude and the ω0 is the angular dominant fre- = msin(ω t), where m is the amplitude and the ω is the angular dominant frequency of the quency of0 the periodical motion. The two baseband0 responses can be written as I(t) = periodical motion. The two baseband responses can be written as I(t) = sin(4πx(t)/λ + θ) sin(4πx(t)/λ + θ) and Q(t) = cos(4πx(t)/λ + θ), where θ is the sum of the phase shift due to and Q(t) = cos(4πx(t)/λ + θ), where θ is the sum of the phase shift due to the nominal the nominal distance between the radar and the target, as well as the residual phase noise. distance between the radar and the target, as well as the residual phase noise. By using By using arctangent demodulation, the vibration motion x(t) can be recovered as x(t) = arctangent demodulation, the vibration motion x(t) can be recovered as x(t) = (λ/4π) × (λ/4π) × {arctan[I(t)/Q(t)] − θ} = (λ/4π) × {arctan[sin(4πx(t)/λ + θ)/cos(4πx(t)/λ + θ)] − θ}, {arctan[I(t)/Q(t)] − θ} = (λ/4π) × {arctan[sin(4πx(t)/λ + θ)/cos(4πx(t)/λ + θ)] − θ}, where where λ is the wavelength of the transmitted RF signal [56]. Since the Doppler radars take λ is the wavelength of the transmitted RF signal [56]. Since the Doppler radars take advantage of the range correlation effect, the residual phase noise is negligible [57]. θ is advantage of the range correlation effect, the residual phase noise is negligible [57]. θ only dependent on the constant nominal distance between the target and the radar. There- is only dependent on the constant nominal distance between the target and the radar. fore, it can be removed by subtracting the mean value. Therefore, it can be removed by subtracting the mean value. Other transceiver architectures such as digital-IF receiver or double-sideband radars can also be utilized. Interested readers are encouraged to refer to [58–60] for practical discussions on different Doppler radar architectures.

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FigureFigure 2. 2. BlockBlock diagram diagram for for the the SHM SHM based based on on low-cost low-cost Doppler Doppler radars radars through through remote remote vibration vibration monitoring. monitoring.

3. RecentOther Advancementstransceiver architectures for SHM such Based as ondigi Vibrationtal-IF receiver Analysis or double-sideband radars can alsoPortable be utilized. Doppler/interferometry Interested readers are radar encouraged sensors to are refer attractive to [58–60] for for their practical robustness dis- cussionsagainstambient on different light Doppler and noncontact radar architectures. operation. Benefiting from the ability to provide su- perior accuracy in low-frequency displacement measurements, microwave Doppler radars 3.have Recent emerged Advancements as a potential for SHM solution Based for monitoringon Vibration the Analysis health of civil infrastructures. PortableBoard-level Doppler/interferometry radars face limitations radar due se tonsors the lower are attractive transmitted for powertheir robustness and the use againstof antennas ambient with light lower and directivity.noncontact Theyoperation. have Benefiting shorter detection from the rangeability when to provide compared su- periorwith GBI accuracy radars. in In low-frequency recent years, researchersdisplacement employed measurements, board-level microwave Doppler Doppler radars either ra- darson the have active emerged backscattering as a potential mode solution or on the for passivemonitoring backscattering the health mode.of civil Ininfrastruc- the active tures.backscattering mode, active transponders placed at reference points are utilized to increase the powerBoard-level level ofradars the radar’s face limitations received signal due to and the then lower boost transmitted the signal-to-noise power and ratio the (SNR)use ofof antennas the baseband with signals.lower directivity. Although They the installationhave shorter of detection active transponders range when oncompared the radar withillumination GBI radars. scene In mightrecent beyears, complex, researcher the maximums employed detection board-level range Doppler can be radars increased, either and onit can the beactive an alternative backscattering solution mode for theor on SHM the ofpassive bridges backscattering with higher clearance. mode. In Onthe the active other backscatteringhand, radars operating mode, active in the transponders passive backscattering placed at modereference do notpoints take are advantage utilized ofto active in- creasetransponders the power to improve level of thethe SNR radar’s of the received received signal signal. and Digital then signalboost the processing signal-to-noise strategies ratioare utilized (SNR) of to the essentially baseband denoise signals. the Although baseband the signals installation or to of calibrate active transponders the demodulated on thedisplacement. radar illumination Disadvantages scene might such be as comple shorterx, detection the maximum range detection and less robustnessrange can be against increased,coupling and clutter it can noises be an arealternative apparent solution when the for radarthe SHM operates of bridges in the with passive higher backscattering clearance. Onmode, the sinceother thehand, received radars RFoperating power isin not the boostedpassive bybackscattering corresponding mode active do not transponders. take ad- vantageHowever, of active without transponders the requirement to improve of installing the SNR theof the transponders, received signal. the setupDigital becomes signal processingconsiderably strategies simpler are for utilized practical to deployment.essentially denoise the baseband signals or to calibrate the demodulated displacement. Disadvantages such as shorter detection range and less robustness3.1. Doppler against Radar oncoupling Active Backscatteringclutter noises Modeare apparent when the radar operates in the passiveResearchers backscattering have mode, studied since the the feasibility received of RF using power a board-level is not boosted 2.4-GHz by correspond- DC coupled ingDoppler active radar transponders. to measure However, the dynamic without and the static requirement displacement of installing responses the of transpond- bridges [61]. ers,XBee the radio setup modules becomes were considerably attached to simpler the baseband for practical board deployment. of the radar and to a laptop via a USB cable to enable wireless communication between the sensor and the computer, where 3.1.the Doppler raw data Radar was on demodulated. Active Backscattering Several laboratoryMode experiments investigated the influence of theResearchers target distance, have thestudied motion the amplitude,feasibility of and using the motiona board-level frequency 2.4-GHz on the DC accuracy coupled of Dopplerthe displacement radar to measure measurements. the dynamic Although and stat submillimeteric displacement accuracy responses was achieved of bridges for [61]. mea- XBeesurements radio inmodules electronic were lab attached environment, to the baseba the authorsnd board observed of the radar the necessity and to a of laptop increasing via athe USB SNR cable of the to basebandenable wireless signals communicati to boost theon radar’s between detection the sensor range. and the computer, whereTo the solve raw the data problem was demodulated. of the low SNR Severa of basebandl laboratory responses experiments for longerinvestigated monitoring the influencerange, the of usethe oftarget active distance, transponders the motion as referenceamplitude, points and the was motion considered frequency to track on the the accuracyvibration of measurements the displacement of a bridge measuremen [62,63].ts. In Although [64], full-scale submillimeter tests were accuracy conducted was on a achieved50-m long for bridge measurements at the O’Leno in electronic State Park, lab Florida,environment, USA, asthe is authors revealed observed in Figure the3a. ne- The cessityemployment of increasing of active the backscattering SNR of the baseband configuration signals to allows boost thethe incrementradar’s detection of the receivedrange. backscatteredTo solve the RF problem power or of the the change low SNR on of the baseband frequencies responses of the receivedfor longer signals. monitoring It also range,significantly the use reduced of active the transponders influence of as undesired reference surrounding points was considered clutter. Moreover, to track the the use vi- of brationhigh-directivity, measurements bulky of antennas a bridge became [62,63]. unnecessary, In [64], full-scale and the tests radars were operating conducted in on the a active 50- backscattering mode can also be an alternative for the SHM of bridges with high clearance

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m long bridge at the O’Leno State Park, Florida, USA, as is revealed in Figure 3a. The employment of active backscattering configuration allows the increment of the received backscattered RF power or the change on the frequencies of the received signals. It also significantly reduced the influence of undesired surrounding clutter. Moreover, the use of high-directivity, bulky antennas became unnecessary, and the radars operating in the active backscattering mode can also be an alternative for the SHM of bridges with high Sensorsclearance2021, 21, 2612 or be employed in scenarios with moving targets under the bridge such 6cars, of 22 boats or water flow. After various laboratory experiments demonstrated the improved performance of the active transponder configuration, a full-scale test was conducted to or be employed in scenarios with moving targets under the bridge such cars, boats or validate the proposedwater approach. flow. After variousThe radar laboratory displacements experiments demonstrated were compared the improved with performance the measurements obtained fromof the a active string transponder potentiometer configuration, and aan full-scale accelerometer. test was conducted A transponder to validate was the placed on the groundproposed 2.48 m approach. below Thethe radarrada displacementsr. The vibration were comparedmeasurements with the measurementswere forced obtained from a string potentiometer and an accelerometer. A transponder was placed on by a person jumpingthe close ground to 2.48 the m quarter below the radar.span Thewith vibration a frequency measurements approximately were forced by equal a person to the first natural frequencyjumping close of tothe the bridge. quarter span Although with a frequency the radar approximately operation equal on to the the first passive natural backscattering modefrequency was not of theable bridge. to retrieve Although accurate the radar operation measurement on the passive responses, backscattering the active mode was not able to retrieve accurate measurement responses, the active backscattering mode backscattering modefor for the the SHM SHM based based on Doppler on Do radarppler was radar successfully was successfully demonstrated. demonstrated. However, as the However, as the motionmotion amplitude amplitude of of the the bridge bridge decreased decreased to less to than less 2 mm, than the 2 accuracy mm, the on accuracy the radar on the radar measurementsmeasurements deteriorated, deteriorated, as shownshown in Figurein Figure3b (segment 3b (segment 2). 2).

(a)

(b) Figure 3. SHM of a bridgeFigure based 3. SHM on of Doppler a bridge based radar on on Doppler active radar backscattering on active backscattering configuration: conﬁguration: (a) ex- (a) perimental setup; (b) displacementexperimental setup; measurements (b) displacement retrieved measurements by retrievedall the sensors. by all the Reprinted sensors. Reprinted with with permission from ref. [64]. Copyright 2016 John Wiley and Sons. permission from ref. [64]. Copyright 2016 John Wiley and Sons.

In contrast to the active transponder mode, which might be costly and is more power demanding than the passive backscattering mode, recent advancements on radar technologies leveraging nonlinear tags show alternative solutions for clutter rejection and target

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Sensors 2021, 21, x FOR PEER REVIEW 7 of 23 In contrast to the active transponder mode, which might be costly and is more power demanding than the passive backscattering mode, recent advancements on radar technologies leveraging nonlinear tags show alternative solutions for clutter rejection and discrimination [65–68]. Although further investigations are required to demonstrate the target discrimination [65–68]. Although further investigations are required to demonstrate feasibilitythe of feasibility displacement of displacement reconstruction reconstruction through radar through detection radar aided detection by nonlinear aided by nonlin- tags, promisingear tags, results promising on range results tracking on range and tracking physiological and physiological motion monitoring motion monitoring have have paved thepaved way thefor future way for SHM future applications SHM applications [65]. Recent [65]. works Recent on works passive on nonlinear passive nonlinear tags, tags, which inheritwhich the inherit advantages the advantages of passive of operation, passive operation, low cost, low and cost, simple and hardware, simple hardware, also also demonstrateddemonstrated clutter rejection clutter rejectioncapabilities capabilities [66–68]. [66–68].

3.2. Doppler3.2. Radar Doppler Network Radar on Network Active onBackscattering Active Backscattering Mode Mode The compactnessThe compactness of board-level of board-level portable portableradars makes radars it makesalso appealing it also appealing to establish to establish radar sensorradar networks sensor networks to capture to structural capture structural deflections deﬂections at multiple at multiplelocations. locations. The simul- The simultaneous measurementtaneous measurement of displacements of displacements at different at differentparts of a parts structure of a structure are of paramount are of paramount importanceimportance when structural when structural modal analysis modal is analysis needed. is Researchers needed. Researchers have proposed have proposed a wire- a wireless smartless Doppler smart Doppler radar network radar network to measure to measure the dynamic the dynamic and near and static near displacement static displacement of of a 31.6 am 31.6 long m pedestrian long pedestrian steel girder steel girder bridge bridge with a with wood a wood deck deckat Sweet at Sweet Park Parkin Gaines- in Gainesville, ville, Florida,Florida, USA, USA, as asshown shown in inFigure Figure 44 [69].[ 69]. The The proposed proposed smart smart Doppler Doppler radar sensorsensor network network operatedoperated inin thethe activeactive transpondertransponder mode.mode. To To address time-synchronization er- errors that rors thatmay may occuroccur whenwhen aa sensorsensor networknetwork isis deployed,deployed, andand toto accountaccount forfor thethe lacklack ofofonboard onboard datadata processing capabi capabilitylity of of stand-alone stand-alone Doppler Doppler radars, radars, the the authors authors had had to toem- employ a ploy a low-powerlow-power wireless wireless smart smart sensor sensor platform, platform, which which was was attached attached to to each each radar radar sen- sensor node. sor node.Again, Again, XBee XBee radios radios were were used used as as the the wireless wireless communication communication device device for for each each radar and radar anda centrala central base base station, station, which which controlled controlled thethe sensorsensor networknetwork operation by send- sending com- ing commandsmands to to each each sensor sensor node. node. No No communication communication was was allowed allowed among among the the radar radar sensor sensor nodesnodes to to avoid avoid interferences. interferences. Essentia Essentially,lly, each each node node was was intended intended to to conduct conduct the the remote remote sensingsensing during during a apredetermined predetermined duty duty cycle cycle after after receiving receiving the the command command from from the the base base stationstation to preserve to preserve power. power. Then, Then, the raw the baseband raw baseband signals signalswere compressed were compressed and sent and sent to the baseto thestation, base where station, the where analog the radar analog signals radar were signals then were automatically then automatically converted converted to to displacementdisplacement responses. responses.

(a)

Figure 4. Cont.

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(b)

Figure 4. ExperimentalFigure 4. Experimental setup for the setup SHM for of the a SHMbridge of done a bridge by a doneDoppler by aradar Doppler sensor radar network: sensor ( network:a) (a) Doppler radarDoppler mounted radar on mounted plastic onenclosure; plastic enclosure; (b) Doppler (b) radar Doppler network radar deployed. network deployed. Reprinted Reprinted with with permissionpermission from ref. [69]. from Copyright ref. [69]. Copyright 2018 American 2018 American Society of SocietyCivil Engineers of Civil Engineers..

To deployTo a deploysmart wireless a smart wirelesssensor network, sensor network, it is also itnecessary is also necessary to evaluate to evaluatethe power the power consumptionconsumption of each node, of each since node, they since rely they on batteries. rely on batteries. It was observed It was observed that the average that the average current drawncurrent for drawn 1-h duty for 1-hcycle duty was cycle 52.9 wasmA 52.9for each mA forradar each node radar during node 60 during s of remote 60 s of remote detection.detection. In addition, In addition,the robustness the robustness of network of networkoperations operations depends dependson reliable on wireless reliable wireless communicationcommunication between between the nodes the and nodes the and base the station. base station. Since the Since XBee the XBeeradios radios were wereuti- utilized lized by byeach each node node of ofthe the network, network, the the wireless wireless transmission transmission range for thethe XBeeXBeetransceiver trans- was ceiver wasinvestigated. investigated. The The authors authors carried carrie outd out transmission-range transmission-range experiments experiments with with two smart two smartDoppler Doppler radar radar enclosures enclosures mounted mounted on on a a tripod tripod at at 11 mm aboveabove thethe ground.ground. The The success success raterate of of the the transmission transmission was was defined defined as as the number of sent packetspackets thatthat werewere receivedre- for ceived fora a line-of-sight line-of-sight (LOS) (LOS) link link between between two two sensor sensor nodes.nodes. The distance between the the sensors sensors continuouslycontinuously increased increased from from 0 0 to to 100 100 m m in in intervals intervals of of 10 10 m. m. After After the the mark mark of of 100 100 m, the m, the teststests were were done done at atintervals intervals of of5 m. 5 m. The The transmission transmission success success rate rate of ofthe the XBee XBee until until 120 m 120 m waswas 100%. 100%. The The success success rate rate was was higher higher than than 90% 90% for for distances distances between between 120 120 m m and and 150 m, 150 m, andand it iteventually eventually dropped dropped quickly quickly for for distances distances greater greater than than 150 150 m. m. To validateTo validatethe proposed the proposed smart wireless smart wirelessnetwork, network, three waterproof three waterproof enclosures enclosures were were assembledassembled and then andattached then to attached the bottom to the of bottomthe bridge of thewith bridge the antennas with the directed antennas to- directed towards the ground, which was approximated 1.7 m below the radars. One radar was wards the ground, which was approximated 1.7 m below the radars. One radar was in- installed at the midspan and the other two were placed close to the quarter-span. Three stalled at the midspan and the other two were placed close to the quarter-span. Three corresponding active transponders were installed directly below the radars on the ground. corresponding active transponders were installed directly below the radars on the The base station was placed at approximately 15 m away from the closest radar. To verify ground. The base station was placed at approximately 15 m away from the closest radar. the radar measurements at the midspan, a corresponding string potentiometer was set up. To verify the radar measurements at the midspan, a corresponding string potentiometer Initially, dynamically forced vibration was provoked at the midspan with an excitation was set up. Initially, dynamically forced vibration was provoked at the midspan with an frequency of 1.5 Hz. The natural frequency of the first vibration mode for the bridge is excitation frequency of 1.5 Hz. The natural frequency of the first vibration mode for the 4.7 Hz. Figure5a exhibits the displacement measurement recorded at the midspan for the bridge is 4.7 Hz. Figure 5a exhibits the displacement measurement recorded at the mid- dynamic experiments and the corresponding ground truth (string potentiometer responses). span for the dynamic experiments and the corresponding ground truth (string potentiom- The measurement errors were less than 0.1 mm for most of the experiments. However, eter responses). The measurement errors were less than 0.1 mm for most of the experi- for very small displacements (maximum amplitude <0.2 mm), the radar was not capable ments. However,of providing for very accurate small deflections.displacements Furthermore, (maximum theamplitude first mode <0.2 shape mm), ofthe the radar bridge was was not capableevaluated of usingproviding the measurements accurate deflecti fromons. three Furthermore, deployed the radars. first Amode finite-element shape of model the bridge(FEM) was ofevaluated the bridge using provided the measuremen the reference,ts from and thethree three-point deployed moderadars. shape A finite- estimated by element themodel radar (FEM) responses of the werebridge found provided to be inthe good reference, agreement and the with three-point simulation-based mode results

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Sensors 2021, 21, 2612 9 of 22 shape estimated by the radar responses were found to be in good agreement with simulation-based results as seen in Figure 5b. A near static deflection was excited by a 10-mph moving truck. Byas analyzing seen in Figure the deflections5b. A near staticat the deﬂectionmidspan and was at excited the quarter-span by a 10-mph after, moving truck. By the travel time betweenanalyzing those the deﬂectionstwo points atwas the estimated midspan as and 1.01 at s. the Since quarter-span the distance after, be- the travel time tween them wasbetween 4.52 m, the those approximate two points velo wascity estimated of 9.99 mph as 1.01 was s. calculated Since the distancefor the mov- between them was ing vehicle. 4.52 m, the approximate velocity of 9.99 mph was calculated for the moving vehicle.

(a)

(b)

Figure 5. ExperimentalFigure results 5. Experimental for the Doppler results radar for the sensor Doppler network radar operating sensor network in the active operating mode: in the active mode: (a) displacement measured(a) displacement at the midspan; measured (b at) shape the midspan; of the first (b) mode. shape ofReprinted the ﬁrst mode.with permission Reprinted with permission . from ref. [69]. Copyrightfrom ref. 2018 [69]. Amer Copyrightican Society 2018 American of Civil Engineers Society of Civil Engineers.

Although wireless sensorAlthough networks wireless contribute sensor to minimize networks the contribute implementation to minimize costs of SHM the implementation sys- costs tems, significant time-synchronizationof SHM systems, significant errors may time-synchronizationoccur, which reduce the e errorsffectiveness may of occur, the use which reduce the of multiple sensorseffectiveness for the simultaneous of the use measur of multipleement recording sensors on for differe the simultaneousnt locations. Further- measurement recording more, there was no consideration on the influence of environmental vibrations on the previously on different locations. Furthermore, there was no consideration on the influence of environ- studied works. The motion analysis was always based on forced vibrations. Even with the pres- mental vibrations on the previously studied works. The motion analysis was always based ence of active transponders, which makes the scenario more complex and power demanding, the detection range ofon the forced proposed vibrations. radar configuration Even with (active the presence backscattering of active mode) transponders, was limited to which makes the up to 3 m to providescenario reasonably more accurate complex displacements. and power demanding, the detection range of the proposed radar configuration (active backscattering mode) was limited to up to 3 m to provide reasonably accurate displacements.

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3.3.3.3. Doppler Doppler Radar Radar on on Passive Passive Backscattering Backscattering Mode Mode RadarsRadars on on passive passive backscattering backscattering mode mode can can be be easily easily set set up up and and put put in in operation. operation. LikeLike radars radars on on active active backscattering backscattering mode, mode, they they can can also be programmed for continuous monitoringmonitoring or forfor thethe remoteremote sensing sensing from from time time to to time time to keepto keep the the power power consumption consumption low. low.To overcome To overcome the practical the practical limitations limitations of deploying of deploying active active transponders, transponders, a feasibility a feasibility study studyon the on SHM the of SHM traffic of lighttraffic structure light structure based on based Doppler on radarDoppler operating radar operating in passive in mode passive was modeproposed was [proposed70]. The block [70]. diagramThe block for diagram the field for SHM the field of a trafficSHM lightof a traffic structure light is structure revealed isin revealed Figure6. in For Figure the first 6. time,For the it wasfirst showntime, it the was presence shown ofthe sudden presence jumps of sudden introduced jumps in introducedthe oscillatory in displacementthe oscillatory obtained displacement from the obtained 5.8-GHz from Doppler the 5.8-GHz radar phase Doppler history. radar The phasemajor history. cause of The this major issue wascause the of insufficientthis issue wa SNRs the of insufficient the received SNR signal of the at times, received caused signal by atmultipath times, caused effects by and multipath the long distanceeffects and between the long the mastdistance and thebetween ground. the Since mast the and radar- the ground.based displacement Since the radar-based is estimated displacement by calculating is estimated the phase by differences calculating between the phase successive differ- encessampling between points, successive in moments sampling of high points, noise in levels, moments an abnormal of high noise point levels, in the constellationan abnormal pointgraph in may the greatlyconstellation deviate graph from may the unitgreatly circle deviate during from the the application unit circle of during the conventional the appli- cationarctangent of the demodulation conventional algorithm arctangent [56 demodula]. To addresstion this algorithm issue, the [55]. non-adaptive To address joint this signalissue, theprocessing non-adaptive algorithm joint (JSPA), signal processing which relies algo onrithm the application (JSPA), which of a median relies on filter the followedapplication by ofa revisea median circle filter fitting, followed was proposed. by a revise circle fitting, was proposed.

FigureFigure 6. 6. BlockBlock diagram diagram for for field ﬁeld structural structural health health mo monitoringnitoring based based on on Doppler Doppler radar. radar. Reprinted Reprinted with permission from ref. [70]. Copyright 2020 IEEE. with permission from ref. [70]. Copyright 2020 IEEE.

SinceSince the the manual manual adjustment adjustment of ofthe the parameters parameters of the of thenon-adaptive non-adaptive JSPA JSPA would would im- pedeimpede its itsreal-time real-time implementation, implementation, two two novel novel strategies strategies were were suggested suggested to to eliminate jumpsjumps in phase-demodulatedphase-demodulated DopplerDoppler radar radar data: data: the the adaptive adaptive JSPA JSPA (AJSPA), (AJSPA), which which is the is theautomated automated version version of the of the JSPA, JSPA, and and the adaptivethe adaptive lowpass lowpass filtering filtering algorithm algorithm (ALFA) (ALFA) [71]. [71].The flowchartsThe flowcharts of the of proposed the proposed adaptive adaptive displacement displacement techniques techniques are shownare shown in Figure in Fig-7. ureThe 7. AJSPA The AJSPA and the and ALFA the ALFA only requiresonly requires as input as input a segment a segment of the of basebandthe baseband signals, signals, the thesampling sampling frequency, frequency, the radarthe radar operating operating frequency, frequency, and the and window the window size of size the of circle the fittingcircle fittingsubroutine. subroutine. The AJSPA The firstAJSPA applies first theapplies median the filter median whose filter window whose size windown depends size onn de- the pendsused sampling on the used rate. sampling It contributes rate. It to contributes reduce excessive to reduce noise excessive levels and noise has levels an advantage and has anof beingadvantage more of robust being againstmore ro abust single against corrupted a single point corrupted than the point mean than filter. the Sincemean jumpsfilter. Sincemight jumps be omitted might bybe theomitted median by the filter, median a revised filter, circle a revised fitting circle subroutine fitting issubroutine considered is consideredafter the conventional after the conventional circle fitting circle processing fitting processing to ensure to that ensure remaining that remaining deviations devi- are ationsaccounted are accounted for. On the for. other On hand, the other the ALFA hand, relies the ALFA on a lowpass relies on filtering a lowpass approach, filtering which approach,automatically which changesautomatically its relevant changes parameters its relevant according parameters to the according examined to segment the examined of raw segmentradar data. of raw Since radar the numberdata. Since of sinusoidalthe number components of sinusoidal on components the I/Q baseband on the signalsI/Q base- is banda function signals of is the a function instantaneous of the instantaneou motion amplitude,s motion the amplitude, cut-off frequency the cut-off of frequency the adaptive of thelowpass adaptive filter lowpass embedded filer in embedded ALFA is chosen in ALFA to be is slightly chosen higher to be thanslightly the frequencyhigher than of the the frequencyweakest significant of the weakest harmonic significant component. harmonic As a consequence, component. onlyAs a theconsequence, necessary harmoniconly the necessarycomponents harmonic are preserved components for the are following preserved phase-demodulation for the following phase-demodulation subroutine and high digitalfrequency processing interferences and high are attenuated.frequency interferences are attenuated.

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(a) (b) Figure 7. Flowchart for the propose adaptive displacement calibration strategies: (a) adaptive joint signal processing algorithm; (b) Figure 7. Flowchart for the propose adaptive displacement calibration strategies: (a) adaptive joint signal processing adaptive lowpass filtering algorithm. Reprinted with permission from ref. [71]. Copyright 2020 IEEE. algorithm; (b) adaptive lowpass ﬁltering algorithm. Reprinted with permission from ref. [71]. Copyright 2020 IEEE.

TheThe effectiveness effectiveness of of both both approaches approaches was was validated validated by by computer-generated computer-generated and and ex- ex- perimentalperimental results. results. Again, Again, thethe mastmast armarm waswas manually excited by by pulling pulling a a rope rope until until its itsmotion motion reached reached large large amplitudes. Then, the vibrationvibration decayeddecayed freely.freely. The The radar radar was was mountedmounted near near the the midspan midspan and and the the ground ground truth truth was was simultaneously simultaneously detected detected by a conven-by a con- tionalventional tri-axial tri-axial DC-response DC-response accelerometer accelerometer of ±4 g of in ±4 measurement g in measurement range that range was that installed was in- tostalled the same to the axial same position axial position as the radar. as the The radar.I/Q channelsThe I/Q channels of the radar of the and radar the and channels the chan- of thenels accelerometer of the accelerometer were digitized were bydigitized a National by a InstrumentNational Instrument NI-9239 voltage NI-9239 input voltage module input onmodule a CompactRIO on a CompactRIO platform, usedplatform, as the used data as acquisition the data acquisition system (DAQ). system (DAQ). FigureFigure8 depicts 8 depicts the the experimental experimental setup setup and an thed the displacement displacement obtained obtained after after the the use use ofof the the conventional conventional arctangent arctangent demodulation demodulation algorithm algorithm on on one one of of the the recorded recorded data data sets. sets. JumpsJumps are are clearly clearly seen seen around around instants instants 240 240 s ands and 320 320 s. s. The The manual manual versions versions of of the the AJSPA AJSPA andand the the ALFA ALFA were were also also implemented implemented in in MATLAB MATLAB and and applied applied to to the the recorded recorded data data set set shownshown in Figurein Figure8b. Figure8b. Figure9a,b illustrate 9a,b illustra the displacementte the displacement measurement measurement obtained through obtained thethrough non-adaptive the non-adaptive JSPA and the JSPA non-adaptive and the non-adaptive lowpass filtering lowpass technique, filtering respectively.technique, respec- The accelerometertively. The accelerometer measurements measurements are compared are with compared the radar with responses. the radar Discontinuities responses. Discon- and jumpstinuities are observedand jumps in are both observed demodulated in both data, demodulated which demonstrates data, which that demonstrates the non-adaptive that the strategiesnon-adaptive are not strategies robust forare not practical robust SHM for practical applications. SHM applications. The reason theThe non-adaptivereason the non- JSPAadaptive fails inJSPA providing fails in accurateproviding displacements accurate displacements is because is it because keeps the it keeps window the sizewindow of thesize median of the filtermedian constant, filter constant, which causes which severe it causes attenuation severe attenuation on high-frequency on high-frequency compo- nentscomponents particularly particularly when the when amplitude the amplitude motion mo is considerablytion is considerably large. large. The non-adaptive The non-adap- lowpasstive lowpass filtering filtering strategy strategy is ineffective is ineffective because, because, as the motionas the motion amplitude amplitude continuously continu- changes,ously changes, the number the number of significant of significant harmonics harmonics also changes, also changes, while the while cutoff the frequency cutoff fre- ofquency the lowpass of the filterlowpass is fixed. filter Thus,is fixed. it doesThus, not it does account not account for amplitude for amplitude motion motion variations. vari- Inations. contrast, In Figurecontrast,9c,d Figure reveal 9c,d the reveal radar displacementsthe radar displacements after applying after theapplying AJSPA the and AJSPA the

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and the ALFA,ALFA, respectively. respectively. The sudden The sudden bounces bounces are completely are completely removed removed from from the de- the demodulated modulated DopplerDoppler radar radar data data since since both both appr approachesoaches modify modify its key its key parameters parameters as the as the amplitude amplitude motionmotion changes. changes. However, However, althou althoughgh the the radar radar measurements measurements werewere inin good good agreement with agreement withthe the ground ground truth, truth, when when the the SNR SNR deteriorates, deteriorates, the the AJSPA AJSPA produced produced distortions dis- as seen in tortions as seenFigure in Figure9c. Likewise, 9c. Likewise, discontinuities discontinuities caused caused by the by process the process of cutting of cutting apart displacement apart displacementwith with abrupt abrupt jumps jumps are highlightedare highlighted in Figure in Figure9d. The9d. The RMS RMS error error for for the the AJSPA response AJSPA responsewas was 0.445 0.445 cm cm and and the the RMS RMS error error between between the the ALFA ALFA measurement measurement and and the reference was the ground was0.465 0.465 cm. cm. The The authors authors also also analyzed analyzed the theproposed proposed algorithmsalgorithms regardingregarding their accuracy their accuracy and computationalcomputational demanddemand forfor other other seven seven recorded recorded data data sets. sets. Both Both calibration cali- strategies bration strategiesprovided provided sub sub centimeter centimeter measurement measurement accuracy. accuracy. During During the the computational computa- complexity tional complexityanalysis, analysis, ALFA ALFA performances performances presented presented higher higher latencies latencies than the than AJSPA the performances AJSPA performancessince the since latter the has latter an has embedded an embedded spectral spectral analysis analysis subroutine. subroutine. The proposed The adaptive proposed adaptivecalibration calibration strategies strategies are are capable capable to to handle handle the the issue issue of of introduced introduced suddensud- jumps and den jumps andcan can be be used used for for the the data data demodulation demodulation of of long-term long-term baseband baseband signals. signals. However, with- However, withoutout the presence presence of of active active tran transponderssponders or ornonlinear nonlinear tags, tags, clutter clutter and andmov- moving targets ing targets remainedremained a challenge a challenge for accurate for accurate SHM SHM based based on Doppler on Doppler radar radaroperating operating in in passive passive backscatteringbackscattering mode. mode.

(a)

(b)

Figure 8. ExperimentalFigure results 8. Experimental for the field results SHM for of thea traffic ﬁeld light SHM structure: of a trafﬁc (a light) experimental structure: setup (a) experimental setup with the radar enclosurewith the shown radar enclosure in the inset; shown (b) demodulated in the inset;( bdisplacement) demodulated using displacement only arctangent using only arctangent demodulation. Reprinteddemodulation. with Permission Reprinted withfrompermission ref. [71]. Copyright from ref. 2020 [71]. IEEE. Copyright 2020 IEEE.

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(a) (b)

FigureFigure 9. Experimental 9. Experimental results: results: (a) demodulated (a) demodulated displacement displacement using using the non-adaptive the non-adaptive JSPA; JSPA; (b) demodulated (b) demodulated displacement displacement using the non-adaptiveusing the non-adaptive ALFA; (c) demodulated ALFA; (c) demodulated displacement displacement using the AJSPA; using the(d) demodulated AJSPA; (d) demodulated displacement displacement using the ALFA. using Disconti- the nuities are highlighted in (b–d). Reprinted with permission from ref. [71]. Copyright 2020 IEEE. ALFA. Discontinuities are highlighted in (b–d). Reprinted with permission from ref. [71]. Copyright 2020 IEEE. 3.4. Doppler Radar Array on Passive Backscattering Mode 3.4. Doppler Radar Array on Passive Backscattering Mode As already mentioned, the structural modal analysis is obtained from the synchro- As already mentioned, the structural modal analysis is obtained from the synchronous nous multipoint vibration measurements. In this paper, cabled radar networks, which do multipoint vibration measurements. In this paper, cabled radar networks, which do not rely not rely on batteries as DC power sources, are referred to as radar sensor arrays. By stud- on batteries as DC power sources, are referred to as radar sensor arrays. By studying the ying the changes on the vibration mode shape, another important parameter for the struc- changes on the vibration mode shape, another important parameter for the structural health tural health diagnosis can be evaluated. Recently, researchers proposed a low-cost Dop- diagnosis can be evaluated. Recently, researchers proposed a low-cost Doppler radar array pler radar array operating in the passive backscattering mode for the SHM of a traffic light operating in the passive backscattering mode for the SHM of a traffic light as illustrated in as illustrated in Figure 10 [72]. In the full-scale tests, forced vibrations demonstrated that Figure 10[ 72]. In the full-scale tests, forced vibrations demonstrated that the displacements the displacements at the tip and at the midspan could be simultaneously recovered with at the tip and at the midspan could be simultaneously recovered with sub centimeter sub centimeter accuracy. The references were displacements estimated by twice numerical accuracy. The references were displacements estimated by twice numerical integration of integration of the corresponding accelerometer measurements. A high-pass filter with a the corresponding accelerometer measurements. A high-pass filter with a cut-off frequency cut-off frequency of 0.5 Hz was applied to the processed data after each integration step. of 0.5 Hz was applied to the processed data after each integration step. In contrast to most In contrast to most of the existing SHM works based on Doppler radars, structural re- of the existing SHM works based on Doppler radars, structural response to environmental sponse to environmental loads was also considered. The arrays were prepared to uninter- loads was also considered. The arrays were prepared to uninterruptedly monitor the ruptedly monitor the traffic light for several days. Only the segments of raw data that traffic light for several days. Only the segments of raw data that produced displacements produced displacements larger than ~1 cm at the tip and larger than ~0.5 cm at the mid- larger than ~1 cm at the tip and larger than ~0.5 cm at the midspan were successfully span were successfully phase-demodulated. The amplitude motion significantly affects phase-demodulated. The amplitude motion significantly affects the SNR of the received the SNR of the received RF signals [73]. Since jumps were seen at the arctangent demod- RF signals [73]. Since jumps were seen at the arctangent demodulated data retrieved ulated data retrieved from the tip of the mast, the previously mentioned AJSPA was nec- from the tip of the mast, the previously mentioned AJSPA was necessary to calibrate essary to calibrate the radar measurements. Figure 11a,b show the wind-induced vibration the radar measurements. Figure 11a,b show the wind-induced vibration measurements measurements obtained at the tip and at the midspan, respectively. The RMS error for the obtained at the tip and at the midspan, respectively. The RMS error for the displacement displacement recovered at the tip was calculated as 0.282 cm. The error for the displace- recovered at the tip was calculated as 0.282 cm. The error for the displacement measured ment measured at the midspan was 0.117 cm, which was smaller due to the introduced at the midspan was 0.117 cm, which was smaller due to the introduced distortions by the distortions by the filtering approaches embedded in the AJSPA. Fourier-based analysis filtering approaches embedded in the AJSPA. Fourier-based analysis and structural modal and structural modal evaluations obtained through proper orthogonal decomposition al- evaluations obtained through proper orthogonal decomposition algorithms are not able gorithms are not able to capture the non-stationary fluctuations caused by environmental to capture the non-stationary fluctuations caused by environmental vibrations because vibrations because these methods only deliver the average spectral decomposition of the these methods only deliver the average spectral decomposition of the signal [74,75]. On signal [74,75]. On the other hand, multi-resolution strategies that rely on complex Morlet the other hand, multi-resolution strategies that rely on complex Morlet wavelet transforms

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can overcome these limitations [76–79]. Therefore, the authors estimated the evolution ofwavelet the dominant transforms vibration can overcome frequencies these by limitat identifyingions [76–79]. the frequencies Therefore, associated the authors with esti- the waveletmated transformsthe evolution can of overcome the dominant these vibrationlimitations frequencies [76–79]. Therefore, by identifying the authors the frequencies esti- maximummated the evolution magnitude of the of dominant the wavelet vibration scalograms frequencies of by the identifying measurements the frequencies obtained from associated with the maximum magnitude of the wavelet scalograms of the measurements Dopplerassociated radar with the and maximum accelerometer magnitude displacement of the wavelet data scalograms at every instant. of the measurements Figure 11c,d exhibit obtained from Doppler radar and accelerometer displacement data at every instant. Fig- theobtained plots from for the Doppler instantaneous radar and naturalacceleromete frequenciesr displacement retrieved data atat theevery tip instant. and the Fig- midspan, ure 11c,d exhibit the plots for the instantaneous natural frequencies retrieved at the tip respectively.ure 11c,d exhibit The the variance plots for onthe theinstantaneous dominant natural vibration frequencies frequencies retrieved is relativelyat the tip higher and the midspan, respectively. The variance on the dominant vibration frequencies is rel- forand the the smallermidspan, amplitude respectively. motion, The variance and iton turns the dominant lower whenvibration the frequencies amplitude is ofrel- the mast atively higher for the smaller amplitude motion, and it turns lower when the amplitude movementatively higher increases. for the smalle Ther amplitude RMS errors motion, for the and dominant it turns lower frequencies when the amplitude estimated by the of the mast movement increases. The RMS errors for the dominant frequencies estimated Dopplerof the mast radar movement array wereincreases. 0.0006 The Hz RMS for errors the measurements for the dominant retrieved frequencies at the estimated tip of the mast by the Doppler radar array were 0.0006 Hz for the measurements retrieved at the tip of andby the 0.0003 Doppler Hz forradar the array measurements were 0.0006 recovered Hz for the at measurements the midspan. retrieved Again, the at the use tip of of the AJSPA thethe mast mast and and 0.0003 0.0003 Hz Hz for forthe themeasurements measurements recovered recovered at the at midspan. the midspan. Again, Again, the use the of use of to calibrate the demodulated displacement at the tip affected the accuracy for the calculated thethe AJSPA AJSPA to tocalibrate calibrate the the demodulated demodulated displacement displacement at the at tip the affected tip affected the accuracy the accuracy for for dominantthethe calculated calculated frequencies. dominant dominant frequencies. frequencies.

FigureFigure 10. ExperimentalExperimental setup. setup. The Thelow-cost low-cost Doppler Doppler radar array radar and array the data and acquisition the data acquisition instru- instru- mentationFigure 10. are Experimental highlighted [72]. setup. The low-cost Doppler radar array and the data acquisition instru- mentation are highlighted [72]. mentation are highlighted [72].

(a) (b)

Figure 11. Cont.

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(e)

Figure 11. Experimental results:Figure (a) 11. demodulated Experimental displacementresults: (a) demodulated using the displacement AJSPA retrieved using the at AJSPA the tip retrieved of the mast;at the (b) tip of the mast; (b) demodulated displacement recovered at the midspan; (c) instantaneous domi- demodulated displacement recoverednant frequencies at the midspan; at the tip; ( c(d)) instantaneous instantaneous dominant dominant freq frequenciesuencies at the at the midspan; tip; (d )(e instantaneous) first in- dominant frequencies at the midspan;plane mode (e) ﬁrstshape in-plane of the traffic mode light shape structure of the ev trafﬁcaluated light by the structure Doppler evaluatedradar array by and the an Doppleraccel- radar array and an accelerometererometer attached attached to the to pole-arm the pole-arm connection connection [72 [72].].

ByBy including including thethe vibrationvibrations measurements retrieved retrieved by byan accelerometer an accelerometer placed placed at at thethe pole-arm pole-arm connection connection of the traffic traffic light, light, the the first first in-plane in-plane structural structural mode mode was also was also calculated.calculated. A A time-domain time-domain POD POD method method was was applied applied to the to themodal modal responses responses simultane- simultane- ouslyously recorded recorded at at three three different different locations locations ofof thethe mastmast (tip, midspan, mast-pole mast-pole connec- connection). In summary,tion). For the the first, high the sensitivity high sensitivity of a Dopplerof a Doppler radar radar array array to to shifts shifts on on thethe instantaneousinstanta- neous natural frequencies was demonstrated. In addition, the estimation of the shape of natural frequencies was demonstrated. In addition, the estimation of the shape of the the first in-plane mode for a traffic light structure was obtained through measurements firstconducted in-plane by mode radars for operating a traffic in light the structurepassive backscattering was obtained mode. through Although measurements the use of con- ductedsignal by processing radars operating techniques in handled the passive the issue backscattering of the low SNR mode. at times Although without the the use need of signal processingof active transponders, techniques handledthe proposed the work issue on ofly the reported lowSNR sub centimeter at times withoutaccuracy results the need of activeand transponders,the radar measurement the proposed errors workfor very only smal reportedl vibrations sub (less centimeter than 0.5 accuracycm) were resultsnota- and thebly radar significant. measurement Furthermore, errors a cabled for very appa smallratus vibrations was deployed, (less which than might 0.5 cm) be werea costly notably significant.and non-practical Furthermore, solution a for cabled certain apparatus SHM applications. was deployed, which might be a costly and non-practical solution for certain SHM applications. 4. Recent Advancements for SHM Based on the Analysis of Time-Doppler Signatures 4. RecentAmong Advancements all the available for renewable SHM Based energy on thesources, Analysis wind ofenergy Time-Doppler is the one with Signatures the widestAmong adoption all the in availablethe United renewableStates and many energy other sources, countries wind in the energy world is[80]. the With one the with the widestincreasing adoption production in the of United wind energy States andfrom manylarge turbines other countries and larger in wind the worldfarms, [real-80]. With thetime increasing monitoring production of operating of windwind energyturbines fromis of critical large turbinesimportance and to largerminimize wind thefarms, maintenance cost, reduce potential costs due to early damage, and avoid human and ani- real-time monitoring of operating wind turbines is of critical importance to minimize mal life-threating events. the maintenanceDue to the cost,capability reduce of also potential providing costs ti dueme-Doppler to early signatures, damage, andradars avoid have human been and animalinvestigated life-threating for SHM events. of wind turbines [25–28,81–93]. The long-term, long-range, and compactnessDue to the of capability low-cost radar of also sensors providing make them time-Doppler strong candidates signatures, to the remote radars detec- have been investigatedtion of the blade’s for SHM motion. of wind The turbines theoretical [25 st–udy28,81 of– 93Doppler]. The radar long-term, signatures long-range, acquired andby com- pactnessshort-range of low-cost Doppler radar radars sensors and a parabolic make them model strong for blade candidates curvature to thewere remote proposed detection in of the[28]. blade’s Through motion. mathematical The theoretical simulations, study the of Dopplerdifferent aspects radar signatures of the time-Doppler acquired byre- short- rangesponses Doppler with respect radars to and contra a parabolicsting blade model forms and for radar blade illumination curvature angles were proposed(0° and 90°) in [28]. Throughwere analyzed. mathematical The authors simulations, concluded the that different the curved aspects flashes ofobtained the time-Doppler after the detection responses withof respectmoving tocurved contrasting blades are blade the formsresult of and the radar coherent illumination sum of signal angles contributions (0◦ and 90 associ-◦) were analyzed.ated Thewith authors blade scatterers, concluded i.e., that the the flashes curved exist flashes due to obtained the constructive after the detectioninterference of of moving equal-phase scatterers not located at the blade tip, and the halos are related to the move- curved blades are the result of the coherent sum of signal contributions associated with ment of the blade tip. To experimentally validate the theoretical results, radar measure- bladements scatterers, were carried i.e., out the for flashes a 50-m-height exist due (660-kW to the Vestas constructive V47) and interference a 12-m-height of (1.9-kW equal-phase scatterersSkystream not 3.7) located wind at turbine the blade in the tip, American and the Wind halos Power are related Center, to theLubbock, movement TX, USA, of the as blade tip.illustrated To experimentally in Figure 12a. validate Two Doppler the theoretical radars operating results, in radar the C-band measurements (transmitting were fre- carried outquency for a 50-m-height at 5.8-GHz) and (660-kW the K-band Vestas (transmitting V47) and a frequency 12-m-height at 24-GHz) (1.9-kW frequencies Skystream were 3.7) wind turbine in the American Wind Power Center, Lubbock, TX, USA, as illustrated in Figure 12a. Two Doppler radars operating in the C-band (transmitting frequency at 5.8-GHz) and the K-band (transmitting frequency at 24-GHz) frequencies were deployed. By observing the positive and negative Doppler flashes, one can estimate the blade rotation period, and then its angular velocity, which is not only a function of the wind speed but is also roughly Sensors 2021, 21, x FOR PEER REVIEW 16 of 23 Sensors 2021, 21, 2612 16 of 22

deployed. By observing the positive and negative Doppler flashes, one can estimate the bladecorrelated rotation withperiod, the and amount then its of angular power thatvelocity, will which be converted is not only into a electricity.function of Thethe main winddifference speed but between is also roughly the two correlated Doppler with radars the employed amount of power in this that study will are be theconverted transmitting intoRF electricity. frequency The and main the antennadifference beamwidth. between the Fortwo the Doppler 24-GHz radars radar, employed the detected in this turbine studyblades are the were transmitting not completely RF frequency contained and withinthe antenna the antenna beamwidth. beam. For However,the 24-GHz the ra- higher dar,operation the detected frequency turbine enabled blades were the construction not completely of highercontained resolution within the spectrograms. antenna beam. To better However,benefit fromthe higher the radial operation speed frequency detection enab of Dopplerled the radarsconstruction and mitigate of higher spurious resolution curvature spectrograms.effects, illumination To better angles benefit near from to the zero ra aredial preferred. speed detection In addition, of Doppler the proposed radars and parabolic mitigatemodel spurious for curved curvature blades effects, was verified illumination by simulations angles near and to zero confirmed are preferred. by the experimentsIn addition,conduct the proposed with the Skystreamparabolic model 3.7 wind for curv turbine.ed blades Figure was 12 verifiedb shows by the simulations spectrogram and for the confirmedVestas V47 by the illuminated experiments by aconduct 24-GHz with Doppler the Skystream radar. The 3.7 positivewind turbine. and negative-Doppler Figure 12b showsflashes, the whichspectrogram are associated for the Vestas with theV47 different illuminated contributions by a 24-GHz of theDoppler blade radar. segments The when positive and negative-Doppler flashes, which are associated with the different contribu- their instantaneous position is perpendicular to the transmitting radar signal, are con- tions of the blade segments when their instantaneous position is perpendicular to the firmed. It should be noted that the presence of mirrored flashes is attributed to the I/Q transmitting radar signal, are confirmed. It should be noted that the presence of mirrored mismatches of the in-house mixer used in the 24-GHz radar prototype. Figure 12c exhibits flashes is attributed to the I/Q mismatches of the in-house mixer used in the 24-GHz radar the spectrogram for the Skystream 3.7 when illuminated by a 24-GHz Doppler radar sensor. prototype. Figure 12c exhibits the spectrogram for the Skystream 3.7 when illuminated by The hook shape of the flashes validates the proposed parabolic model for curved blades. a 24-GHz Doppler radar sensor. The hook shape of the flashes validates the proposed An estimation of the blade curvature can be provided through the analysis of the radar parabolic model for curved blades. An estimation of the blade curvature can be provided throughsignatures. the analysis Finally, of the the authors radar signatures. did not report Finally, any the approaches authors did or methodsnot report for any the ap- eventual proachesSHM of or the methods blades. for Only the theoreticaleventual SHM findings of the were blades. demonstrated Only theoretical by computational findings were results demonstratedand realistic by experiments. computational results and realistic experiments.

(a)

(b) (c)

Figure 12. SHM of industrial wind turbines: (a) experimental scenario; (b) spectrogram for the Vestas V47 illuminated by a 24-GHz Doppler radar; (c) spectrogram for the Skystream 3.7 illuminated by a 24-GHz Doppler radar sensor. Reprinted with permission from ref. [28]. Copyright 2016 IEEE.

Sensors 2021, 21, x FOR PEER REVIEW 17 of 23

Figure 12. SHM of industrial wind turbines: (a) experimental scenario; (b) spectrogram for the Sensors 2021, 21, 2612 Vestas V47 illuminated by a 24-GHz Doppler radar; (c) spectrogram for the Skystream 3.7 illumi-17 of 22 nated by a 24-GHz Doppler radar sensor. Reprinted with permission from ref. [28]. Copyright 2016 IEEE.

A novel strategy for the structural conditioncondition monitoring of a horizontal axis wind turbineturbine was proposed in [94]. [94]. The The solution solution reli relieded on the extraction extraction of three key parameters (rotational speed, downtime, and duration of yaw) from the time-Doppler map associated with the turbineturbine blades’blades’ movement. movement. Figure Figure 13 13a,ba,b reveal reveal the the spectrograms spectrograms when when the the turbine tur- bineis fully is fully operational operational and whenand when it is gradually it is gradually stopping. stopping. The rotation The rotation speed speed is related is related to the toamount the amount of generated of generated power aspower already as alread mentioned.y mentioned. The monitoring The monitoring of downtime, of downtime, which is whichthe time is ofthe reduced time of operationreduced operation or inactivity, or inac is alsotivity, associated is also associated with energy with generation. energy gener- The ation.yaw monitoring The yaw monitoring provides information provides information about the blades’about the condition. blades’ Thecondition. structural The healthstruc- turaldiagnosis health of diagnosis the wind turbineof the wind is assessed turbine by is analyzing assessed theby analyzing spectral energy, the spectral the maximum energy, thedetected maximum Doppler detected frequency, Doppler the frequency, time interval the betweentime interval flashes between after theflashes separation after the of sep- the arationDC level, of thethe halos,DC level, and the halos, flash signatures and the flash from signatures the spectrogram. from the The spectrogram. proposed algorithm The pro- posedcalculates algorithm the downtime calculates from the the downtime overall estimated from the spectraloverall energyestimated at thespectral DC level. energy If the at theenergy DC level. at the If zero-Doppler the energy at suppresses the zero-Doppl a givener suppresses threshold for a given more threshold than 5 s, the for counter more than for 5the s, downtimethe counter is for triggered. the downtime The yaw is triggered. time evaluation The yaw is based time evaluation on the maximum is based Doppler on the maximumspeed. Again, Doppler if the speed. blades Again, are not if the perpendicularly blades are not oriented perpendicularly towards oriented the radar towards due to thechanges radaron due the to directionchanges on of the direction wind, the of maximum the wind, detectedthe maximum frequency detected will frequency vary, and willthe durationvary, and of the this duration event canof this be event measured. can be The measured. blade rupture The blade can rupture easily becan identified easily be identifiedby observing by observing the maximum the maximum Doppler frequencyDoppler frequency for each blade.for each Blade blade. surface Blade damage surface damagecan also can be detectedalso be detected by the analysisby the analysis of the processedof the processed time-Doppler time-Doppler signatures. signatures. Since Sincethe inner the inner constituents constituents of the of blade the blade have differenthave different backscattering backscattering properties, properties, the reflected the re- flectedechoes echoes (flashes (flashes and halos) and ofha bladeslos) of blades with surface with surface damages damages would would be less be powerful. less powerful. Angle Anglemismatches mismatches are observed are observed when the when inclined the inclined angle between angle between successive successive blades changes, blades changes,which indicates which indicates that the that blade the is blade deviating is deviating from its from original its original position. position. The interval The interval time timebetween between the corresponding the corresponding flashes flashes can also can be also used be to used estimate to estimate the rotational the rotational speed. Figurespeed. Figure13c illustrates 13c illustrates the proposed the proposed procedure procedure for extracting for extracting SHM features SHM features from the from corresponding the corre- spondingtime-frequency time-frequency plots. plots.

(a) (b)

(c)

Figure 13. SHM ofof aa onshoreonshore windwind turbine: turbine: ( a()a) spectrogram spectrogram when when the the turbine turbine is fullyis fully operational; operational; (b) ( spectrogramb) spectrogram when when the bladethe blade rotation rotation is gradually is gradually stopping; stopping; (c) ﬂowchart (c) flowchart of the of proposed the proposed strategy strategy for the for SHM the SHM of a wind of a wind turbine. turbine. Reprinted Reprinted with permissionwith permission from from ref. [94 ref.]. Copyright[94]. Copyright 2021 IEEE.2021 IEEE.

Simulations and experimental results obtained through the radar monitoring of real turbines from a wind farm were used to demonstrate the proposed strategy. The blade length for each turbine is 55.5 m. A 5.8-GHz Doppler radar was chosen to illuminate the targets. Five wind turbines were monitored for the rotational speed, which was compared Sensors 2021, 21, 2612 18 of 22

with a reference extracted from simultaneously recorded videos. The measurement error after two trials for each turbine remained below 0.4 rotations per minute. They also calculated the downtime for a gradually stopping turbine. The radar-based measurement was 21.31 s and the reference was given as 19.4 s. Finally, a faulty diagnosis was performed. After the calculation of the maximum Doppler frequencies for the flashes associated with the monitored blades, it was observed that one of them had a rupture since its speed greatly deviated from the other two. Figure 14 shows an extract of the spectrogram for the three- blade faulty wind turbine. The Doppler frequencies on points A and B are, respectively, −3230 Hz and −3208 Hz. However, the frequency on point C is −2864 Hz, which flagged a damaged blade. Since the features’ extraction depends on the acquired spectrograms, the alignment of the radar with the rotation plane of the blades plays a critical role. For example, if the radar’s field of view is not identical for receding and approaching blades, Sensors 2021, 21, x FOR PEER REVIEW 19 of 23 the algorithm will yield false positive results. The summary list of the research works reviewed in this paper is given in Table1.

Figure 14. Spectrogram for a faulty three-blade wind turbine obtained from 5.8-GHz Doppler radar Figure 14. Spectrogram for a faulty three-blade wind turbine obtained from 5.8-GHz Doppler ra- measurements. Reprinted with the permission from ref. [94]. Copyright 2021 IEEE. dar measurements. Reprinted with the permission from ref. [94]. Copyright 2021 IEEE.

Table 1. Summary list of the research works reviewed in this paper. 5. Conclusions Publication Type of Reported Meas. Radar System/ArrangementPowered Mode by the advancements of semiconductorTestbed technologies, Doppler radar can be miniaturized, which led to vastYear practical implementationsVibration in the civilianRange world. The recentAccuracy Multi-monostatic GBI Bridge technicalPassive backscattering advancements on digital 2021 signal processing pushedForced the tec 23hnology m/33 m further <1into mm radar [55] smart integration with embedded systems.monitoring In addition, the potential costs of Doppler ra- Aluminum 2.4-GHz Doppler radar [61]dar Passive sensors backscattering after CMOS integration 2015 and mass productionForced would possibly 1.25 make m them even <1 mm more appealing than other noncontact approabeamches for SHM such as camera systems and Pedestrian 2.4-GHz Doppler radar [64]laser Active vibrometers. backscattering Beamforming 2016 technology can also beForced exploited to address 2.48 m the current <1 mm issue of measuring only the displacementbridge on the radial direction. Furthermore, by lever- 2.4-GHz Doppler radars aging the compactness of low-cost DopplerPedestrian/car radars, radar sensor networks can be de- arranged as a wireless Active backscattering 2019 Forced 4.52 m <1 mm bridge network [69] ployed. Although the adoption of low-cost, compact radars still lags behind other technologies, SHM researchers and engineers can be optimistic about the promising possibil- Passive backscattering Traffic light 5.8-GHz Doppler radar [70] ities(non-adaptive for low-cost displ. radars with 2020the rapid dissemination of Forcedchipset-based sensors 6 m and migra- <1 cm structure tioncalibration to mm-wave algorithm) bands. Passive backscattering Traffic light 5.8-GHz Doppler radar [71] (adaptiveAuthor Contributions: displ. calibration D.V.Q.R.2020 carried out the literature reviewForced and wrote the 6 m manuscript. <1C.L. cm structure revisedalgorithms) the paper and provided valuable recommendations. All authors have read and agreed to the published version of the manuscript. Forced & 5.8-GHz Doppler radars Traffic light Passive backscattering 2021 ambient 6 m <1 cm arranged as an array [72] Funding: The authors would like to acknowledgestructure funding support from National Science Founda- vibration tion (NSF) under grants ECCS-1808613 and ECCS-2030094. 5.8-GHz/24-GHz Doppler Rotor diameter: Passive backscattering 2016 Wind turbine Forced - radar [28] Conflicts of Interest: The authors declare no conflict of interest. 47 m/3.7 m Rotor diameter: 5.8-GHz Doppler radar [94] Passive backscattering 2021 Wind turbine Forced - References 111 m 1. James, R.J. A history of radar. IEE Rev. 1989, 35, 343–349. 2. Peng, Z.; Muñoz-Ferreras, J.M.; Tang, Y.; Liu, C.; Gómez-García, R.; Ran, L.; Li, C. A Portable FMCW Interferometry Radar with Programmable Low-IF Architecture for Localization, ISAR Imaging, and Vital Sign Tracking. IEEE Trans. Microw. Theory Tech. 2017, 65, 1334–1344. 3. Peng, Z.; Ran, L.; Li, C. A K -Band Portable FMCW Radar with Beamforming Array for Short-Range Localization and Vital- Doppler Targets Discrimination. IEEE Trans. Microw. Theory Tech. 2017, 65, 3443–3452. 4. Peng, Z.; Li, C. A Portable K -Band 3-D MIMO Radar with Nonuniformly Spaced Array for Short-Range Localization. IEEE Trans. Microw. Theory Tech. 2018, 68, 5075–5086. 5. Nasr, I.; Jungmaier, R.; Baheti, A.; Noppeney, D.; Bal, J.S.; Wojnowski, M.; Karagozler, E.; Raja, H.; Lien, J.; Poupyrev, I.; et al. A Highly Integrated 60 GHz 6-Channel Transceiver with Antenna in Package for Smart Sensing and Short-Range Communica- tions. IEEE J. Solid-State Circuits 2016, 51, 2066–2076. 6. Li, C.; Xiao, Y.; Lin, J. Experiment and Spectral Analysis of a Low-Power Ka-Band Heartbeat Detector Measuring from Four Sides of a Human Body. IEEE Trans. Microw. Theory Tech. 2006, 54, 4464–4471.

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5. Conclusions Powered by the advancements of semiconductor technologies, Doppler radar can be miniaturized, which led to vast practical implementations in the civilian world. The recent technical advancements on digital signal processing pushed the technology further into smart integration with embedded systems. In addition, the potential costs of Doppler radar sensors after CMOS integration and mass production would possibly make them even more appealing than other noncontact approaches for SHM such as camera systems and laser vibrometers. Beamforming technology can also be exploited to address the current issue of measuring only the displacement on the radial direction. Furthermore, by leveraging the compactness of low-cost Doppler radars, radar sensor networks can be deployed. Although the adoption of low-cost, compact radars still lags behind other technologies, SHM researchers and engineers can be optimistic about the promising possibilities for low-cost radars with the rapid dissemination of chipset-based sensors and migration to mm-wave bands.

Author Contributions: D.V.Q.R. carried out the literature review and wrote the manuscript. C.L. revised the paper and provided valuable recommendations. All authors have read and agreed to the published version of the manuscript. Funding: The authors would like to acknowledge funding support from National Science Foundation (NSF) under grants ECCS-1808613 and ECCS-2030094. Conﬂicts of Interest: The authors declare no conﬂict of interest.

References 1. James, R.J. A history of radar. IEE Rev. 1989, 35, 343–349. [CrossRef] 2. Peng, Z.; Muñoz-Ferreras, J.M.; Tang, Y.; Liu, C.; Gómez-García, R.; Ran, L.; Li, C. A Portable FMCW Interferometry Radar with Programmable Low-IF Architecture for Localization, ISAR Imaging, and Vital Sign Tracking. IEEE Trans. Microw. Theory Tech. 2017, 65, 1334–1344. [CrossRef] 3. Peng, Z.; Ran, L.; Li, C. A K -Band Portable FMCW Radar with Beamforming Array for Short-Range Localization and Vital- Doppler Targets Discrimination. IEEE Trans. Microw. Theory Tech. 2017, 65, 3443–3452. [CrossRef] 4. Peng, Z.; Li, C. A Portable K -Band 3-D MIMO Radar with Nonuniformly Spaced Array for Short-Range Localization. IEEE Trans. Microw. Theory Tech. 2018, 68, 5075–5086. [CrossRef] 5. Nasr, I.; Jungmaier, R.; Baheti, A.; Noppeney, D.; Bal, J.S.; Wojnowski, M.; Karagozler, E.; Raja, H.; Lien, J.; Poupyrev, I.; et al. A Highly Integrated 60 GHz 6-Channel Transceiver with Antenna in Package for Smart Sensing and Short-Range Communications. IEEE J. Solid-State Circuits 2016, 51, 2066–2076. [CrossRef] 6. Li, C.; Xiao, Y.; Lin, J. Experiment and Spectral Analysis of a Low-Power Ka-Band Heartbeat Detector Measuring from Four Sides of a Human Body. IEEE Trans. Microw. Theory Tech. 2006, 54, 4464–4471. [CrossRef] 7. Li, C.; Lin, J. Random Body Movement Cancellation in Doppler Radar Vital Sign Detection. IEEE Trans. Microw. Theory Tech. 2008, 56, 3143–3152. 8. Xiong, J.; Hong, H.; Zhang, H.; Wang, N.; Chu, H.; Zhu, X. Multitarget Respiration Detection with Adaptive Digital Beamforming Technique Based on SIMO Radar. IEEE Trans. Microw. Theory Tech. 2020, 68, 4814–4824. [CrossRef] 9. Wang, P.; Ma, Y.; Liang, F.; Zhang, Y.; Yu, X.; Li, Z.; An, Q.; Lv, H.; Wang, J. Non-Contact Vital Signs Monitoring of Dog and Cat Using a UWB Radar. Animals 2020, 10, 205. [CrossRef] 10. Rodriguez, D.; Li, C. Sensitivity and Distortion Analysis of a 125-GHz Interferometry Radar for Submicrometer Motion Sensing Applications. IEEE Trans. Microw. Theory Tech. 2019, 67, 5384–5395. [CrossRef] 11. Wang, Y.; Ling, H.; Chen, V.C. ISAR motion compensation via adaptive joint time-frequency technique. IEEE Trans. Aerosp. Electron. Syst. 1998, 34, 670–677. [CrossRef] 12. Ding, C.; Hong, H.; Zou, Y.; Chu, H.; Zhu, X.; Fioranelli, F.; Le Kernec, J.; Li, C. Continuous Human Motion Recognition with a Dynamic Range-Doppler Trajectory Method Based on FMCW Radar. IEEE Trans. Geosci. Remote Sens. 2019, 57, 6821–6831. [CrossRef] 13. Ram, S.S.; Ling, H. Through-wall tracking of human movers using joint Doppler and array processing. IEEE Trans. Geosci. Remote Sens. 2008, 5, 537–541. [CrossRef] 14. Kim, Y.; Ling, H. Human activity classification based on micro-Doppler signatures using an artificial neural network. In Proceedings of the IEEE Antennas and Propagation Society International Symposium, San Diego, CA, USA, 5–11 July 2008; pp. 1–4. 15. Kim, Y.; Ling, H. Human activity classification based on micro-Doppler signatures using a support vector machine. IEEE Trans. Geosci. Remote Sens. 2009, 47, 1328–1337. Sensors 2021, 21, 2612 20 of 22

16. Ram, S.S.; Christianson, C.; Kim, Y.; Ling, H. Simulation and analysis of human micro-Dopplers in through-wall environments. IEEE Trans. Geosci. Remote Sens. 2010, 48, 2015–2023. [CrossRef] 17. Rodrigues, D.V.Q.; Li, C. Hand gesture recognition using FMCW radar in multi-person scenario. In Proceedings of the IEEE MTT-S Radio and Wireless Symposium (RWS), Virtual Conference, 17–20 January 2021. 18. Rodrigues, D.V.Q.; Li, C. Noncontact exercise monitoring in multi-person scenario with frequency-modulated continuous- wave radar. In Proceedings of the IEEE MTT-S International Microwave Biomedical Conference (IMBioC), Virtual Conference, 14–17 December 2020. 19. Li, Y.; Peng, Z.; Pal, R.; Li, C. Potential Active Shooter Detection Based on Radar Micro-Doppler and Range-Doppler Analysis Using Artificial Neural Network. IEEE Sens. J. 2019, 19, 1052–1063. [CrossRef] 20. Gu, C.; Salmani, Z.; Zhang, H.; Li, C. Antenna array technology for radar respiration measurement in motion-adaptive lung cancer radiotherapy. In Proceedings of the IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems (BioWireleSS), Santa Clara, CA, USA, 15–18 January 2012; pp. 21–24. 21. Gu, C.; Li, R.; Zhang, H.; Fung, A.Y.C.; Torres, C.; Jiang, S.B.; Li, C. Accurate respiration measurement using DC-coupled continuous-wave radar sensor for motion-adaptive cancer radiotherapy. IEEE Trans. Biomed. Eng. 2012, 59, 3117–3123. [PubMed] 22. Klemm, M.; Craddock, I.J.; Leendertz, J.A.; Preece, A.; Benjamin, R. Radar-Based Breast Cancer Detection Using a Hemispherical Antenna Array—Experimental Results. IEEE Trans. Antennas Propag. 2009, 57, 1692–1704. [CrossRef] 23. Chen, V.C.; Ling, H. Time-Frequency Transforms for Radar Imaging and Signal Analysis; Artech House: Norwood, MA, USA, 2002. 24. Li, J.; Ling, H. Application of adaptive chirplet representation for ISAR feature extraction from targets with rotating parts. IEE Proc. Radar Sonar Navig. 2003, 150, 284–291. [CrossRef] 25. Naqvi, A.; Yang, S.-T.; Ling, H. Investigation of Doppler features from wind turbine scattering. IEEE Antennas Wireless Propag. Lett. 2010, 9, 485–488. [CrossRef] 26. Li, C.J.; Ling, H. On simulating the high-resolution radar image of a wind turbine. In Proceedings of the 10th European Conference on Antennas and Propagation (EuCAP), Davos, Switzerland, 10–15 April 2016; pp. 1–3. 27. Whitelonis, N.; Yang, S.-T.; Ling, H. Application of nearfield to far-field transformation to Doppler features from wind turbine scattering. IEEE Trans. Antennas Propag. 2012, 60, 1660–1665. [CrossRef] 28. Muñoz-Ferreras, J.-M.; Peng, Z.; Tang, Y.; Gómez-García, R.; Liang, D.; Li, C. Short-range Doppler-radar signatures from industrial wind turbines: Theory, simulations, and measurements. IEEE Trans. Instrum. Meas. 2016, 65, 2108–2119. [CrossRef] 29. Thenozhi, S.; Yu, W.; Garrido, R. A novel numerical integrator for structural health monitoring. In Proceedings of the 5th International Symposium on Resilient Control Systems, Salt Lake City, UT, USA, 14–16 August 2012; pp. 92–97. 30. Srivastava, S.K.; Lukowski, T.I.; Gray, R.B.; Shepherd, N.W.; Hawkins, R.K. RADARSAT: Image quality management and performance results. In Proceedings of the Canadian Conference on Electrical and Computer Engineering, Calgary, AB, Canada, 26–29 May 1996; Volume 1, pp. 21–23. 31. Farrar, C.R.; Darling, T.W.; MigliorI, A.; Baker, W.E.; Adams, M. Microwave interferometers for non-contact vibration measurements on large structures. Mech. Syst. Signal Process. 1999, 13, 241–253. [CrossRef] 32. Pieraccini, M.; Tarchi, D.; Rudolf, H.; Leva, D.; Luzi, G.; Bartoli, G.; Atzeni, C. Structural static testing by interferometric synthetic radar. NDT E Int. 2000, 33, 565–570. [CrossRef] 33. Tarchi, D.; Casagli, N.; Fanti, R.; Leva, D.D.; Luzi, G.; Pasuto, A.; Pieraccini, M.; Silvano, S. Landslide monitoring by using ground-based SAR interferometry: An example of application to the Tessina landslide in Italy. Eng. Geol. 2003, 68, 15–30. [CrossRef] 34. Atzeni, C.; Bicci, A.; Dei, D.; Fratini, M.; Pieraccini, M. Remote survey of the Leaning Tower of Pisa by Interferometric Sensing. IEEE Geosci. Remote Sens. Lett. 2010, 7, 185–189. [CrossRef] 35. Pieraccini, M.; Papi, F.; Donati, N. I-Q imbalance correction of microwave displacement sensors. Electron. Lett. 2015, 51, 1021–1023. [CrossRef] 36. Luzi, G.; Crosetto, M.; Fernández, E. Radar interferometry for monitoring the vibration characteristics of buildings and civil structures: Recent case studies in Spain. Sensors 2017, 17, 669. [CrossRef] 37. Di Pasquale, A.; Nico, G.; Pitullo, A.; Prezioso, G. Monitoring Strategies of Earth Dams by Ground-Based Radar Interferometry: How to Extract Useful Information for Seismic Risk Assessment. Sensors 2018, 18, 244. [CrossRef] 38. Hu, J.; Guo, J.; Xu, Y.; Zhou, L.; Zhang, S.; Fan, K. Differential Ground-Based Radar Interferometry for Slope and Civil Structures Monitoring: Two Case Studies of Landslide and Bridge. Remote Sens. 2019, 11, 2887. [CrossRef] 39. Moll, J.; Bechtel, K.; Hils, B.; Krozer, V. Mechanical Vibration Sensing for Structural Health Monitoring Using a Millimeter-Wave Doppler Radar Sensor. In Proceedings of the 7th European Workshop on Structural Health Monitoring (EWSHM), Nantes, France, 8–11 July 2014; pp. 1802–1808. 40. Diaferio, M.; Fraddosio, A.; Daniele Piccioni, M.; Castellano, A.; Mangialardi, L.; Soria, L. Some issues in the structural health monitoring of a railway viaduct by ground-based radar interferometry. In Proceedings of the IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS), Milan, Italy, 24–25 July 2017; pp. 1–6. 41. Pieraccini, M.; Betti, M.; Forcellini, D.; Dei, D.; Papi, F.; Bartoli, G.; Facchini, L.; Corazzi, R.; Cerisano Kovacevic, V. Radar detection of pedestrian-induced vibrations on Michelangelo’s David. PLoS ONE 2017, 12, e0174480. [CrossRef] 42. Luzi, G.; Crosetto, M.; Angelats, E.; Fernández, E. An interferometric radar sensor for monitoring the vibrations of structures at short. In MATEC Web of Conferences; EDP Sciences: Les Ulis, France, 2018; Volume 148, p. 01005. Sensors 2021, 21, 2612 21 of 22

43. Viviani, F.; Michelini, A.; Mayer, L.; Conni, F. IBIS-ArcSAR: An Innovative Ground-Based SAR System for Slope Monitoring. In Proceedings of the IGARSS—IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018; pp. 1348–1351. 44. Liu, X.; Li, S.; Tong, X. Two-Level W-ESMD Denoising for Dynamic Deflection Measurement of Railway Bridges by Microwave Interferometry. IEEE J. Sel. Top. Signal Process. 2018, 11, 4874–4883. [CrossRef] 45. Miccinesi, L.; Pieraccini, M. Monostatic/Bistatic Interferometric Radar for Monitoring Slander Structures. In Proceedings of the IEEE Conference on Antenna Measurements & Applications (CAMA), Kuta, Bali, Indonesia, 23–25 October 2019; pp. 1–4. 46. Castagnetti, C.; Bassoli, E.; Vincenzi, L.; Mancini, F. Dynamic Assessment of Masonry Towers Based on Terrestrial Radar Interferometer and Accelerometers. Sensors 2019, 19, 1319. [CrossRef][PubMed] 47. Pieraccini, M.; Miccinesi, L. Ground-Based Radar Interferometry: A Bibliographic Review. Remote Sens. 2019, 11, 1029. [CrossRef] 48. Pieraccini, M.; Miccinesi, L. An Interferometric MIMO Radar for Bridge Monitoring. IEEE Geosci. Remote Sens. Lett. 2019, 16, 1383–1387. [CrossRef] 49. Pieraccini, M.; Miccinesi, L.; Abdorazzagh Nejad, A.; Naderi Nejad Fard, A. Experimental Dynamic Impact Factor Assessment of Railway Bridges through a Radar Interferometer. Remote Sens. 2019, 11, 2207. [CrossRef] 50. Wang, Y.; Dai, K.; Xu, Y.; Zhu, W.; Lu, W.; Shi, Y.; Mei, Z.; Xue, S.; Faulkner, K. Field Testing of Wind Turbine Towers with Contact and Noncontact Vibration Measurement Methods. J. Perform. Constr. Facil. 2020, 34, 04019094. [CrossRef] 51. Nico, G.; Prezioso, G.; Masci, O.; Artese, S. Dynamic Modal Identification of Telecommunication Towers Using Ground Based Radar Interferometry. Remote Sens. 2020, 12, 1211. [CrossRef] 52. Huang, Q.; Wang, Y.; Luzi, G.; Crosetto, M.; Monserrat, O.; Jiang, J.; Zhao, H.; Ding, Y. Ground-Based Radar Interferometry for Monitoring the Dynamic Performance of a Multitrack Steel Truss High-Speed Railway Bridge. Remote Sens. 2020, 12, 2594. [CrossRef] 53. Artese, S.; Nico, G. TLS and GB-RAR Measurements of Vibration Frequencies and Oscillation Amplitudes of Tall Structures: An Application to Wind Towers. Appl. Sci. 2020, 10, 2237. [CrossRef] 54. Miccinesi, L.; Michelini, A.; Pieraccini, M. Blurring/Clutter Mitigation in Quarry Monitoring by Ground-Based Synthetic Aperture Radar. IEEE Trans. Geosci. Remote Sens. 2021.[CrossRef] 55. Miccinesi, L.; Beni, A.; Pieraccini, M. Multi-Monostatic Interferometric Radar for Bridge Monitoring. Electronics 2021, 10, 247. [CrossRef] 56. Park, B.; Boric-Lubecke, O.; Lubecke, V. Arctangent demodulation with DC offset compensation in quadrature Doppler radar receiver systems. IEEE Trans. Microw. Theory Tech. 2007, 55, 10731079. [CrossRef] 57. Droitcour, A.D.; Boric-Lubecke, O.; Lubecke, V.M.; Lin, J.; Kovac, G.T. Range correlation and I/Q performance benefits in single-chip silicon Doppler radars for noncontact cardiopulmonary monitoring. IEEE Trans. Microw. Theory Tech. 2004, 52, 838–848. [CrossRef] 58. Wei, C.; Lin, J. Digitally assisted low IF architecture for noncontact vital sign detection. In Proceedings of the IEEE MTT-S International Microwave Symposium, Phoenix, AZ, USA, 17–22 May 2015; pp. 1–4. 59. Gu, C. Short-Range Noncontact Sensors for Healthcare and Other Emerging Applications: A Review. Sensors 2016, 16, 1169. [CrossRef] 60. Ma, X.; Li, L.; You, X.; Lin, J. Envelope detection for a double-sideband Low IF CW radar. In Proceedings of the IEEE/MTT-S International Microwave Symposium—IMS, Philadelphia, PA, USA, 10–15 June 2018; pp. 240–243. 61. Guan, S.; Rice, J.A.; Li, C.; Li, Y.; Wang, G. Dynamic and static structural displacement measurement using backscattering DC coupled radar. Smart Struct. Syst. 2015, 16, 521–535. [CrossRef] 62. Gu, C.; Wang, G.; Rice, J.A.; Li, C. Interferometric Radar Sensor with Active Transponders for Signal Boosting and Clutter Rejection in Structural Health Monitoring. In Proceedings of the IEEE/MTT-S International Microwave Symposium Digest, Montreal, QC, Canada, 17–22 June 2012; pp. 1–3. 63. Rice, J.A.; Gu, C.; Li, C.; Guan, S. A radar-based sensor network for bridge displacement measurements. In Proceedings of the SPIE Sensor Smart Structures Technologies for Civil Mechanical, and Aerospace Systems, San Diego, CA, USA, 3 April 2012; p. 83450I. 64. Guan, S.; Rice, J.A.; Li, C.; Li, Y.; Wang, G. Structural displacement measurements using DC coupled radar with active transponder. Struct. Control Health Monitor. 2016, 24, 1909. [CrossRef] 65. Wang, J.; Rodriguez, D.; Mishra, A.; Nallabolu, P.R.; Karp, T.; Li, C. 24-GHz Impedance-Modulated BPSK Tags for Range Tracking and Vital Signs Sensing of Multiple Targets Using an FSK Radar. IEEE Trans. Microw. Theory Tech. 2021, 69, 1817–1828. 66. Tsai, Z.; Jau, P.-H.; Kuo, N.-C.; Kao, J.-C.; Lin, K.-Y.; Chang, F.-R.; Yang, E.-C.; Wang, H. A High-Range-Accuracy and High- Sensitivity Harmonic Radar Using Pulse Pseudorandom Code for Bee Searching. IEEE Trans. Microw. Theory Tech. 2013, 61, 666–675. [CrossRef] 67. Mishra, A.; Li, C. A Low Power 5.8-GHz ISM-Band Intermodulation Radar System for Target Motion Discrimination. IEEE Sens. J. 2019, 19, 9206–9214. [CrossRef] 68. Mishra, A.; McDonnell, W.; Wang, J.; Rodriguez, D.; Li, C. Intermodulation-Based Nonlinear Smart Health Sensing of Human Vital Signs and Location. IEEE Access 2019, 7, 158284–158295. [CrossRef] 69. Guan, S.; Rice, J.A.; Li, C.; DeMello, N.J. Smart radar sensor network for bridge displacement monitoring. J. Bridge Eng. 2019, 24, 04018102. [CrossRef] Sensors 2021, 21, 2612 22 of 22

70. Rodrigues, D.V.Q.; Tang, Z.; Wang, J.; Zuo, D.; Li, C. Structural Health Monitoring of a Traffic Signal Support Structure Based on 5.8-GHz Doppler Radar with Median Filter and Revised Circle Fitting. In Proceedings of the IEEE Radio and Wireless Symposium (RWS), San Antonio, TX, USA, 26–29 January 2020; pp. 187–190. 71. Rodrigues, D.V.Q.; Zuo, D.; Tang, Z.; Wang, J.; Gu, C.; Li, C. Adaptive Displacement Calibration Strategies for Field Structural Health Monitoring Based on Doppler Radars. IEEE Trans. Instrum. Meas. 2020, 69, 7813–7824. [CrossRef] 72. Rodrigues, D.V.Q.; Zuo, D.; Li, C. Wind-Induced Displacement Analysis for a Traffic Light Structure Based on a Low-Cost Doppler Radar Array. IEEE Trans. Instrum. Meas. (under review). 73. Guan, S.; Rice, J.A.; Li, C.; Gu, C. Automated DC offset calibration strategy for structural health monitoring based on portable CW radar sensor. IEEE Trans. Instrum. Meas. 2014, 63, 3111–3118. [CrossRef] 74. Han, S.; Fenny, B. Application of proper orthogonal decomposition to structural vibration analysis. Mech. Syst. Signal Process. 2003, 17, 989–1001. [CrossRef] 75. Azam, S.E.; Rageh, S.A.; Linzell, D. Damage detection in structural systems utilizing artificial neural networks and proper orthogonal decomposition. Struct. Control Health Monit. 2019, 26, e2288. [CrossRef] 76. Percival, D.B.; Walden, A.T. Wavelet Methods for Time Series Analysis; Cambridge Series in Statistical and Probabilistic Mathematics; Cambridge University Press: New York, NY, USA, 2000. 77. Addison, P.S.; Watson, J.N.; Feng, T. Low-oscillation complex wavelets. J. Sound Vib. 2002, 254, 733–762. [CrossRef] 78. Zuo, D.; Wu, L.; Smith, D.A.; Morse, S.M. Experimental and analytical study of galloping of a slender tower. Eng. Struct. 2017, 132, 44–60. [CrossRef] 79. Wu, L.; Zuo, D. Numerical evaluation of coupled galloping of slender towers in boundary-layer winds based on a nonlinear analytical model. J. Fluids Struct. 2018, 83, 358–371. [CrossRef] 80. U.S. Primary Energy Consumption by Energy Source. 2019. Available online: https://www.eia.gov/energyexplained/renewable- sources/ (accessed on 25 February 2021). 81. Ciang, C.C.; Lee, J.-R.; Bang, H.-J. Structural health monitoring for a wind turbine system: A review of damage detection methods. Meas. Sci. Technol. 2008, 19, 1–20. [CrossRef] 82. Li, C.J.; Bhalla, R.; Ling, H.A.; Yang, S.-T.; Ling, H. Investigation of dynamic radar signatures of a vertical-axis wind turbine. IEEE Antennas Wireless Propag. Lett. 2015, 14, 763–766. [CrossRef] 83. Karabayir, O.; Yucedag, S.M.; Coskun, A.F.; Yucedag, O.M.; Serim, H.A.; Kent, S. Wind turbine signal modelling approach for pulse Doppler radars and applications. IET Radar Sonar Navigat. 2015, 9, 276–284. [CrossRef] 84. Nikoubin, T.; Muñoz-Ferreras, J.-M.; Gómez-García, R.; Liang, D.; Li, C. Structural health monitoring of wind turbines using a low-cost portable K-band radar: An ab-initio field investigation. In Proceedings of the IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet), San Diego, CA, USA, 25–28 January 2015; pp. 69–71. 85. Muñoz-Ferreras, J.-M.; Peng, Z.; Tao, Y.; Gómez-García, R.; Liang, D.; Li, C. A step forward towards radar sensor networks for structural health monitoring of wind turbines. In Proceedings of the IEEE Radio and Wireless Symposium (RWS), Austin, TX, USA, 24–27 January 2016; pp. 1–3. 86. Scholz, M.; Rediske, S.; Nuber, A.; Friedmann, H.; Moll, J.; Arnold, P.; Krozer, V.; Kraemer, P.; Salman, R.; Pozdniakov, D. Radar-based Non-Destructive Testing of Wind Turbine Blades: First Experiments from a Laboratory Study. In Proceedings of the 8th European Workshop on Structural Health Monitoring, Bilbao, Spain, 5–8 July 2016. 87. Moll, J.; Krozer, V.; Arnold, P.; Dürr, M.; Zimmermann, R.; Salman, R.; Hübsch, D.; Pozdniakov, D.; Friedmann, H.; Nuber, A.; et al. Radar-based structural health monitoring of wind turbine blades. In Proceedings of the 19th World Conference on Non-Destructive Testing, Munich, Germany, 13–17 June 2016. 88. Crespo-Ballesteros, M.; Antoniou, M.; Cherniakov, M. Wind turbine blade radar signatures in the near field: Modeling and experimental confirmation. IEEE Trans. Aerosp. Electron. Syst. 2017, 53, 1916–1931. [CrossRef] 89. Moll, J.; Simon, J.; Malzer, M.; Krozer, V.; Pozdniakov, D.; Salman, R.; Durr, M.; Feulner, M.; Nuber, A.; Friedmann, H. Radar Imaging System for In-Service Wind Turbine Blades Inspections: Initial Results from a Field Installation at a 2MW Wind Turbine. Prog. Electromagn. Res. 2018, 162, 51–60. [CrossRef] 90. Moll, J.; Arnold, P.; Mälzer, M.; Krozer, V.; Pozdniakov, D.; Salman, R.; Rediske, S.; Scholz, M.; Friedmann, H.; Nuber, A. Radar-based structural health monitoring of wind turbine blades: The case of damage detection. Struct. Health Monitor. 2018, 17, 815–822. [CrossRef] 91. Moll, J.; Mälzer, M.; Simon, J.; Zadeh, A.T.; Krozer, V.; Dürr, M.; Kramer, C.; Friedmann, H.; Nuber, A.; Salman, R.; et al. Monitoring of Wind Turbine Blades using Radar Technology: Results from a Field Study at a 2MW Wind Turbine. Struct. Health Monitor. 2019.[CrossRef] 92. Fioranelli, F.; Patel, J.; Horne, C.; Palamà, R.; Griffiths, H.; Danoon, L.; Brown, A. Experimental measurements of radar signatures of large wind turbine. J. Eng. 2019, 7165–7169. [CrossRef] 93. Moll, J.; Zadeh, A.T.; Malzer, M.; Simon, J.; Krozer, V.; Kramer, C.; Friedmann, H.; Nuber, A.; Durr, M.; Pozdniakov, D.; et al. Radar-based Detection of Birds at Wind Turbine Installations: Results from a Field Study. In Proceedings of the 23rd International Microwave and Radar Conference (MIKON), Warsaw, Poland, 5–8 October 2020; pp. 285–288. 94. Zhao, H.; Chen, G.; Hong, H.; Zhu, X. Remote Structural Health Monitoring for Industrial Wind Turbines Using Short-Range Doppler Radar. IEEE Trans. Instrum. Meas. 2021, 70, 1–9.