06978893

Adaptive Moving TargetIndication in a WindblownClutter Environment

YURIY V. GONCHARENKO, Member, IEEEGORDON FARQUHARSON, Member, IEEEUniversity of WashingtonSeattle, Washington, USA

VOLODIMIR GOROBETSInstitute of Radiophysics and Electronics NAS of UkraineKharkiv, Ukraine

VIKTOR GUTNIKYURIY TSARIN, Member, IEEEInstitute of Radio Astronomy NAS of UkraineKharkiv, Ukraine

The statistical properties of windblown grass and forest clutter,obtained using high-resolution microwave Doppler radar, areanalyzed and shown to be nonstationary. In particular, we show thatthe bandwidth of the Doppler signal depends linearly on the totalpower of the signal for both types of vegetation. Also, variations inthe mean power and spectrum bandwidth of the clutter are similarto the analysis time of existing radars. The model of an adaptivemoving target indication (MTI) notch filter is presented andevaluated. It is shown that adaptive filtering significantly improvesthe efficiency of an MTI system.

Manuscript received August 9, 2013; revised January 22 and April 20,2014; released for publication April 20, 2014.

DOI. No. 10.1109/TAES.2014.130540.

Refereeing of this contribution was handled by R. Narayanan.

Authors addresses: Y. V. Goncharenko, Institute of Radiophysics andElectronics NAS of Ukraine, Proskura st. 12, Kharkiv, 61085, Ukraine,and University of Washington, Applied Physics Lab, 1013 NE 40thStreet, Seattle, WA 98105-6698 United States, E-mail:([email protected]). G. Farquharson, University of Washington,Applied Physics Lab, 1013 NE 40th Street, Seattle, WA 98105-6698United States; V. Gorobets, Institute of Radiophysics and ElectronicsNAS of Ukraine, Proskura st. 12, Kharkiv, 61085, Ukraine; V. Gutnikand Y. Tsarin, Institute of Radio Astronomy NAS of Ukraine 4,Chervonopraporna St., Kharkov, 61002, Ukraine.

0018-9251/14/$26.00 C 2014 IEEE

I. INTRODUCTION

Radars operating in the F (90 to 140 GHz) andD (110 to 170 GHz) microwave bands allow for the use ofsmall antennas and large operating bandwidths to providevery high spatial resolution, and therefore, precisionlocation of objects. Use of these frequency bands alsooffers improved jamming protection and simplifieselectromagnetic compliance [1]. Some drawbacks ofoperating in these bands include relatively low availableoutput power (several Watts), and atmospheric attenuation(0.3 5 dB/km) [2, 3]. These drawbacks considerablyreduce the maximum range over which these radars makea useful measurement. However, despite these drawbacks,the advantages outlined above make design anddevelopment of F- and D-band radars an appealing areaof research.

In this paper, we characterize the statistical signature ofclutter at these microwave frequencies. We define clutterto be the unwanted echo from the ground and objects thatare not the features of interest. Detecting the interestingobjects is easier, when these objects move against a fixedbackground surface of vegetation, which often happens inpractice. This very important radiolocation problem issolved by moving target indication (MTI) algorithms [4].MTI relies on the difference in the Doppler velocities ofthe target and clutter. However, knowledge of thestatistical properties of the clutter is necessary foreffectively implementing MTI techniques.

There have been a great number of experimental andtheoretical investigations of signals scattered from slightlyrough surfaces such as the sea surface [57], and alsoanalysis of the energy spectra of signals reflected byvegetation [8, 9]. This paper adds to this body of workwith measurements of clutter with a D-band radar fordifferent types of vegetation. Furthermore, we do notassume that the clutter at D-band wavelengths ishomogeneous and stationary, as is done in many previousstudies [811]. Finally, we explore the effect of the clutteron the efficiency of MTI algorithms at these wavelengths.

II. EXPERIMENTAL DATA ON THE STATISTICALPROPERTIES OF TERRAIN CLUTTER

The experiments described below were performedusing a coherent Doppler radar with continuous radiation,which uses homodyne detection [12], so we couldmeasure only the magnitude of the target velocity andcouldnt determine the direction of movement. The activeelement of the radar is a backward wave oscillator (BWO)[13]. The radar has a parabolic combined Tx-Rx antennaand was installed on the top of a mobile tower at 5 mheight. The horizontal and vertical direction of the mainpattern of this antenna was adjusted manually. A summaryof the relevant radar parameters is listed in Table I.

Signals scattered from various types of vegetationwere recorded by this radar for wind speeds from 12 to1012 m/s. The output power of the microwave radar wasnot completely stable, so for calibration of the system a

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 50, NO. 4 OCTOBER 2014 2989

TABLE ITechnical Parameters of the Doppler Radar

Signal ParametersRadiated signal ContinuousFrequency 140 GHzPower 0.61WBandwidth range of the Doppler frequency 0.0110 kHzAntenna Parameters

Beamwidth in azimuth plane 6Beamwidth in elevation plane 3

test reflector was used. The test reflector was a 60 mmdiameter metal ball that had a well-known radar crosssection [14, 15]. Test areas with different types ofvegetation were chosen: grass (barley, with height30-35 cm) and wood (willow, birch) at a distance of about60 to 100 m. The area of grass was 200 to 250 m2 and theobserved area of deciduous cover was around 80 to100 m2. The depression angle for observation of the woodwas about 90, and for the grass cover, 3.5. The azimuthangle between the line of sight and wind direction did notexceed 20 to 30 for both series of experiments. Thedetected radar signal and information about wind velocitywere recorded simultaneously with a sampling rate of20 kHz.

Signal processing consisted primarily of obtaining thepower spectrum of Doppler signals for various observationtimes, close to the data refreshing times in real microwaveradars (0.05-0.1 s). The analysis showed a number ofpeculiarities characteristic of spectra, obtained duringcomparatively short time: hundredths to tenths of asecond. The unusual feature of these spectra is theessential time variability of their characteristics, suchas spectrum components level, signal bandwidth, andshape.

Preliminary analysis of the data showed that Dopplerspectra of windblown forest clutter are located mainly inthe frequency range 500 Hz to 10 kHz. In addition, ahigher frequency component appeared at lowerfrequencies due to using a power conversion from 50 Hzac to 400 Hz ac electrical network. Because of that, themeasured energy spectra will be considered, starting fromthe Doppler frequencies of 500 Hz that correspond to thereflectors radial velocities about 0.5 m/s.

Better equipment was used in the series of experimentswith windblown grass clutter. It allowed signal processingin the frequency range 50 Hz to 10 kHz, corresponding toradial velocities from 0.05 to 10 m/s.

In both cases the sampling rate was 22 kHz, the typicalduration of realization was 120 s. The total duration of allrecords was more than 4 h.

III. RESULTS AND DISCUSSION

A typical spectrogram of Doppler shifts of amicrowave signal reflected from a group of birch trees isshown in Fig. 1. The spectrogram was calculated using thealgorithm described in [17], using data recorded during 7

Fig. 1. Doppler shift spectrogram of windblown forest clutter.

Fig. 2. Doppler shift spectrogram of windblown grass clutter.

to 10 m/s gusty wind conditions. Time is shown along theabscissa axis and the spectral component Dopplerfrequencies, along the ordinate axis. Relative power of thespectrum varies from 25 dB (red) to 95 dB (dark blue).The figure shows that local spectral maxima are formed inthe time interval 0.1 to 0.4 s, and the peak magnitude is30 to 35 dB. The frequency of these maxima in thespectrum increases from 600 Hz to 2 kHz in a period of nomore than 0.15 s, remains constant for approximately0.1 s, and then gradually returns back to its initial spectraldistribution in the last 0.3 0.4 s. The time variability ofthe spectral density is clearly seen.

An example of the Doppler spectrogram is shown inFig. 2 for the grass scattered microwave signal under 10 to12 m/s gusty wind conditions. Here, the relative power ofthe spectral components varies from 50dB (red) to120 dB (dark blue). In contrast to scattering by theforest, these Doppler spectra have no clear maxima. Windgusts caused increases of the scattered signal intensity athigher frequencies. It should be also noted that intensitybursts are 3 to 4 times longer in comparison to similarbursts for scattering from a deciduous forest, and theirintensity is 10 to 15 times smaller.

This means that the influence of ground clutter relatedto grass cover on MTI systems is significantly lower incomparison to the influence of ground clutter related to a

2990 IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 50, NO. 4 OCTOBER 2014

Fig. 3. Doppler centroid variations of windblown forest clutter powerspectrum.

deciduous forest. Note the spectral width for grass doesnot exceed 350 to 400 Hz, which differs from forestclutter. This means that the range of scatterer velocitiesdoes not exceed 0.4 m/s for grass.

Variation in time of the position of the Dopplercentroid of the power spectrum (Fig. 3) was obtained fromthe spectrogram shown in Fig. 1. The intensity andposition of the Doppler centroid gives us informationabout the properties of the main part of the spatiallydistributed target [17].

It is clear from Fig. 3 that at the moment t = 0 theDoppler centroid is at the frequency 600 Hz. This meansthat at that moment in time the backscattered signal isdetermined by a group of reflectors moving with averagevelocity close to 0.6 m/s. In the time interval t = (0 to0.25) s the reflector velocity increases from 0.6 to 1.9 m/sand in the interval t = (0.25 to 0.4) s it returns to the initialvalue of 0.6 m/s. In the example considered there was agroup of oscillating scatterers in the reflecting volume.Such a spectrum is typical for small patterns of deciduousforest (tens of square meters) under a pronounced gustywind. Other examples of spectra recorded show that undera sustained wind, the spectrum has no maximum, but theintensities of spectral components and bandwidth vary intime.

Let us determine the instantaneous power and spectralthreshold frequency of terrain clutters caused by reflectionfrom the deciduous forest, by considering their behaviorwithin short time intervals. Similarly to the instantspectrum, we consider the instantaneous power as thepower of the signal, which has a finite duration. For thisanalysis, this duration is equal to the analysis time Ta =0.093 s. The spectrum threshold frequency values Ft arecomputed from instantaneous spectra obtained by theperiodogram approach of [18].

These frequency values are obtained using thecondition that the instantaneous power for the frequenciesgreater than Ft is no more than triple the radar receiverintrinsic noise:

Fig. 4. Doppler ECS (gray) and threshold frequency (black) ofwindblown forest clutter time variations.

Fig. 5. Doppler ECS (gray) and threshold frequency (black) ofwindblown grass clutter time variations.

3Pnoise

Ft

Pinst (f )df (1)

where Pnoise is the receiver intrinsic noise, and Pinst is theinstantaneous power of signal.

The scattered signal instantaneous power completelycharacterizes the scattering effective cross section ofobserved objects (ECS). It may be determined exactly,since responses from a calibrating ball were also recordedin the experiments. The ECS of forest clutter wasdetermined in the Doppler frequency band from 500 Hzto Ft, and the ECS of grass clutter in the band from 50 Hzto Ft.

Generally, the investigated parameters characteristic ofground clutter and their time variability [ECS (t) andthreshold frequency Ft(t)] are random values. They wereobtained from records of signals scattered by deciduousforest and grass covers with total lengths of more than240 min. Typical examples of time variations of ECS andthreshold frequency of ground clutters are presented inFigs. 4 and 5.

The records of time dependencies shown on Figs. 4and 5 indicate the nonstationarity characteristics of theclutter signal due to time variation of its power andDoppler frequency bandwidth. It is clear that both (t) andFt(t) mean values are different for, e. g., timeslots denotedby letters A and B on both charts. A different characterof oscillations on the dependencies on both time intervalsmight also be noted, which reports different values ofdispersion of random values (t) and Ft(t) at differentperiod timeslots.

GONCHARENKO ET AL.: ADAPTIVE MOVING TARGET INDICATION IN A WINDBLOWN CLUTTER ENVIRONMENT 2991

Fig. 6. Threshold frequency distribution of forest clutter.

Fig. 7. Power distribution of forest clutter.

Fig. 8. Threshold frequency distribution of grass clutter.

Probability distributions of (t) and Ft(t) are shown inFigs. 6 and 7. These distributions are computed over 200 stime interval (2000 of samples of F and ) for thewindblown forest and grass clutter. The probabilitydistributions for the windblown grass clutter are shown inFigs. 8 and 9. The threshold frequency distribution inFig. 6 (forest clutter) is well approximated by a normaldistribution law. The power distribution of Fig. 7 (forestclutter) is well approximated by the Weibull distribution,which is in good agreement with well-known resultsdiscussed in the papers [1921], where the statisticalproperties were determined at hundreds-of-seconds timeintervals. Fitting parameters for obtained distributions arepresented in Table V.

Fig. 9. Power distribution of grass clutter.

Statistical parameter estimate obtained within varioustime durations are presented in Tables II and III. It can beseen from the tables that the cross-correlation coefficientP (linking the ECS of ground clutter with the thresholdfrequency) is almost independent of the time duration. Itvaries in the intervals 0.39 to 0.49 for windblown forestclutter and 0.56 to 0.65 for grass clutter.

The most probable threshold frequency for the signalscattered by grass cover is 570 Hz, which is more than4 times lower than the analogous frequency for forestclutter. This shows that the range of radial scatterervelocities is significantly narrower than the similar rangefor forest scatterers and is limited to between 0 and0.57 m/s, whereas speed of the scatterers responsible forthe reflections from the forest signal, may achieve 2.9 m/s.

It may be supposed that the observed difference in thescatterer Doppler velocities is connected to the differencein their mean mass and geometric sizes. The mass of abirch leaf (which is the highest speed scatterer) is10-20 times less than the mass of a barley ear, and itswindage is essentially higher. Therefore, due to lowerinertia and higher windage, a birch leaf may achieve thehighest vibration speed significantly quicker than barleyear. Probabilities of the signal exceeding its mean valuesP , PF 0.4. . .0.45 and large values of the meandeviation of ECS and threshold frequency prove the pulsecharacter of the process we considered, where fast andshort signal bursts prevail.

First-order regression lines were constructed on thebasis of the data and are shown in Figs. 10 and 11. Thesehave the following form:

FT = F0 + B (2)where FT and are random values (F0 free term, regression coefficient).

The interdependence of the parameters of signalsscattered by a deciduous forest and by grass may be seenfrom Figs. 10 and 11. Power and threshold frequency maybe satisfactorily approximated by the expression (1). Thisshows the tendency for most cases: the more reflectedsignal power, the more Doppler frequency bandwidth thesignal occupies.


TABLE IIStatistical Characteristics of Windblown Forest Clutter

Tr, s.Correlation

coefficient, RMean ECS (t),m2

MeanthresholdfrequencyFt (t), kHz

Deviation ofECS , kHz

Deviation ofthreshold frequency

F, kHz P PF

15 0,39 1,25 2,39 0,58 0,88 0,46 0,4250 0,41 1,15 2,87 0,42 0,91 0,46 0,48100 0,49 1,13 2,90 0,43 0,93 0,47 0,50

TABLE IIIStatistical Characteristics of Windblown Grass Clutter

Tr, s.Correlation

coefficient, RMean ECS (t),m2

MeanthresholdfrequencyFt (t), kHz

Deviation ofECS , kHz

Deviation ofthreshold frequency

F, kHz P PF

15 0.56 0.19 497 0.09 171 0.40 0.4150 0.57 0.22 540 0.10 188 0.41 0.42100 0.65 0.23 574 0.11 203 0.44 0.39

Fig. 10. Scatter plot for windblown forest clutter.

Fig. 11. Scatter plot for windblown grass clutter.

IV. NUMERICAL SIMULATION OF TERRAIN CLUTTERINFLUENCE ON DIFFERENT MTI SYSTEMS

The main goal of MTI radars is the detection ofmoving targets in a background of stationary groundclutter. The constant false alarm rate (CFAR) and

probabilities of correct target detection are the main radarcharacteristics and depend on the distribution of clutter,static characteristics of the target, signal-to-noise ratio(SNR), and detection algorithm [14, 22]. Development ofan optimal detection algorithm for moving target detectionin time-variable noise is a challenging task.

In this section we evaluate MTI detection dependenceon SNR, which has the essential impact on the overallefficiency of radar. For this evaluation we use theexperimental data and regression functions obtained inprevious paragraphs. The main unit of each MTI system isa notch filter. It should attenuate Doppler signals inwaveband 0 to Fn max and pass signals in the frequencyrange Fn max, to FD max,, where Fn max is maximum Dopplerfrequency of clutter, and FD max is maximum expectedDoppler frequency of the useful signal. So, a notch filter isa high-pass filter (HPF) with fixed or variable cut-offfrequency. A few coefficients describe the efficiency of anMTI system: rejection ratio Kp and improvement factorKimp [14].

Kp = Pin/Pout (3)where Pin and Pout are the power of the clutter at the inputand output of the notch filter, respectively. This coefficientshows the noise suppression but doesnt show whathappens with useful signal. The improvement factorquantifies the increase in SNR:

Kimp = (Ps/Pn)out / (Ps/Pn)in (4)where (Ps/Pn)in and (Ps/Pn)out are the SNR at input andoutput of filter.

The improvement factor Kimp can represent fully theefficiency of the MTI system because it gives us not onlyinformation about noise suppression, but also informationconcerning useful signal. The simulation of the influence


Fig. 12. Results of simulation for fixed-bandwidth HPF-1 (left panel), and for filter with varying bandwidth (right panel). (a), (b) Input SN.(c), (d) Output SNR. (e), (f) Improvement factor.

TABLE IVCoefficient of Regression Function

Clutter types: F0, kHz , kHz/m2

Windblown forest clutter: 0.65 2.35Windblown grass clutter: 0 0.35

TABLE VFitting Parameters for ECS and Threshold Frequency Distributions

Type ofVegetation

Fitting Parameters forDistribution of EffectiveDoppler Cross Section

Fitting Parameters forDistribution of Threshold

Frequency

Trees Type: Weibull Type: Gaussscale = 1.14 center = 2916shape = 3.1 width = 1609

Grass Type: Weibull Type: Weibullscale = 0.3080 scale = 575shape = 3.02 shape = 3.2

of various notch filters on the improvement factor wascarried out.

We analyzed HPF with a fixed bandwidth and HPFwith a variable bandpass.

1) HPF with a fixed bandwidth. The model of afourth-order elliptic filter was used. This filter providesattenuation in 0 to 400 Hz frequency range and has 2 fixedcut-off frequencies: HPF-13 kHz (corresponds to averagevalues for windblown trees at Table II) and HPF-2 6 kHz.

2) HPF with variable bandpass. The cut-off frequencyof the notch filter will change according to the regressionfunction shown in Fig. 10 and Table IV, ifthe total powerof clutter changes.

The input signal for all filters is the sum of twocomponents. The first component is a sinusoidal signalwhich has the amplitude corresponding to a radar crosssection equal to 0.8 m2 and a varying Doppler frequency(0 to 20 kHz). The second component is a real signal,scattered from windblown trees.

The simulation shows that in the case of nonsteadyclutter, the output signals of all notch filters andimprovement factor variations are random values. Theexamples of variation in SNR at the input and output of afixed-bandwidth notch filter and improvement factorvariations for the same signal are shown in the left panel ofFigs. 12(a), 12(c), and 12(e). The same examples, obtainedfor a filter with a varying bandwidth are shown in the rightpanel of Figs. 12(b), 12(d), and 12(f). Neither SNR norKimp can represent the efficiency of a filter objectively, butthis figure shows that adaptive filtration gives betterresults. The maximum value of the improvement factor ofa filter with fixed bandwidth is 30, while Kimp for a filterwith varying bandwidth can exceed 180.

The influence of the notch filters parameters on theQ-ratio is obtained using numerical modeling. The resultsof modeling are presented on Fig. 13. By analyzing theseresults we can evaluate the filtering efficiency for differenttarget radial velocities. The HPF-1 with cutoff frequency3 kHz shows relatively good results when working withlow-speed targets. However, the efficiency of this filter forall targets which have a Doppler frequency up to 20 kHz,does not exceed 0.4. Thus, this filter can work effectivelyonly 40% of the time. Increasing the cutoff frequency upto 6 kHz improves the efficiency ratio up to Q = 0.89, forhigh-speed targets when Doppler frequency exceeds thecutoff frequency of HPF-2. However, if the target Doppler


Fig. 13. Q-factor dependence on target Doppler frequency for: 1 HPFwith cut-off frequency 3 kHz; 2 HPF with cut-off frequency 6 kHz;

3 - with variable bandpass.

frequency decreases below 6 kHz, the efficiency ratiotends to zero. So this MTI system cannot be used fordetecting a low-speed target. These dependencies showthat an HPF with low cutoff frequency can provide therequired SNR only for targets with a low Dopplerfrequency. Increasing the cutoff frequency causeslow-speed targets to become invisible.

In case of time-variable interference we propose to useas an efficiency ratio of the MTI system the probability(Q) of SNR at the filter output exceeding a preset level.This level of SNR is preliminarily chosen to obtain thenecessary detection characteristics.

An HPF with a variable cutoff frequency has efficiencyof Q that is not worse than HPF-1 in the frequency band0 4 kHz. For high-speed targets with FD > 4 kHz thisHPF has a very high efficiency (Q = 0.9). Small ripples onthe curves of efficiency Q(FD) can be explained byirregularities of the filter bandwidth. They are determinedby the filter model, used in numerical simulations.

V. CONCLUSIONS

This study produced the following main results.

1) The backscattering characteristics of windblowngrass and trees, measured with a high resolution D-bandDoppler radar are presented.

2) Variations in the mean level of the instantaneousbackscattered power and the Doppler spectrum thresholdfrequency are a characteristic of D-band signals scatteredby deciduous forests and grass cover.

3) At comparable wind speed, the intensity ofwindblown forest clutter is 10 15 times higher than theintensities of windblown grass clutters. The Dopplerspectrum bandwidth (and the scatterers maximum speed)for a windblown forest is 4 5 times higher than thecorresponding values for grass cover. The differences inthe Doppler spectrum bandwidth may be caused by

differences in physical (mechanical) properties of thehighest speed scatterers for both cases: trees leaves andspikelets, and their different mass and areas (windage).This knowledge may be used for estimation of quantityand quality biomass, growing at an investigated lot.

4) The statistical properties and regressiondependencies have to be taken into account in developinghigh-resolution MTI systems, where the clutter Dopplerresponse signals have nonstationary characteristics. It isshown that adaptively filtering ground-based clutterprovides good results when the total power of the receivedsignal was used as the control signal.

Finally, we note that the Doppler signal scattered frommoving vegetation depends not only on the intensity of thewind (as considered in this study), but also on the winddirection. In order to obtain the most intensive andbroadband Doppler response, the experimental data wascollected when the radar was oriented in the downwinddirection. This configuration allowed us to test and showthe efficiency of the proposed MTI algorithm in what weconsidered to be the most difficult environment. For otherwind directions, and in the case of simple scatterers (suchas grass), we speculate that the Doppler shift dependencewould be dominated by the cosine of the angle betweenwind direction and the radar line of sight. For morecomplicated cases (e.g., trees) we might expect a lesssimple dependence on wind direction, due to the complexmovement of tree branches. We leave the investigation ofwind direction as work for a future study.

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[8] Billingsley, J. B.Low-Angle Radar Land Clutter: Measurements and EmpiricalModels. New York: William Andrew, 1999, pp. 35141.

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[10] Billingsley, J. B.Exponential decay in windblown radar ground clutter Dopplerspectra: Multifrequency measurements and model.Massachusetts Institute of Technology, Lexington LincolnLab, Tech. Rep. ADA312399, July 29, 1996.

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[13] Sivan, L.Microwave Tube Transmitters. London: Springer, 1994, pp.5455.

[14] Skolnik, M.Radar Handbook (3rd ed.). New York: McGraw-Hill, 2008.

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[18] Engelberg, S.Digital Signal Processing: An Experimental Approach,London: Springer, 2008, pp. 2951.

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Yuriy V. Goncharenko (S03M08) received the B.S. and M.S. degrees from theKharkov National Polytechnic University, Kharkov, Ukraine, in 1998 and 1999,respectively, and the PhD in radiophysics from the Institute for Radiophysics andElectronic NAS of Ukraine, Kharkov, in 2006.

For 1999 to 2000 he worked at the Institute of Ionosphere, Kharkov. In 2000 hejoined the Institute for Radiophysics and Electronic NAS of Ukraine, where hedeveloped algorithms for microwave remote sensing of sea surface and different typesof land cover. In 2012 he joined the Applied Physics Laboratory at the University ofWashington as Fulbright scholar and implements airborne synthetic aperture radars forremote sensing of sea surface.

Gordon Farquharson (S01M05) received the B.S. (Eng.) and M.S. (Eng.) degreesfrom the University of Cape Town, Cape Town, South Africa, in 1996 and 1999respectively, and the Ph.D. degree in electrical engineering from the University ofMassachusetts, Amherst, in 2005.

From 2004 to 2009, he worked at the National Center for Atmospheric Research,Boulder, CO where he developed microwave and millimeter-wave remote sensingradars for atmospheric research. In 2009, he joined the Applied Physics Laboratory atthe University of Washington, where he develops and uses microwave radar systems forocean remote sensing. He is also an Affiliated Assistant Professor in the ElectricalEngineering Department, where he teaches and supervises students.


Volodimir Gorobets received the M.S. degree in electrical engineering in 1976 fromthe Kharkov Aviation Institute and the Ph.D. in radiophysics in 2006 from the Institutefor Radiophysics and Electronic NAS of Ukraine, Kharkov.

His current field of research is investigations of cm and mm radiowavebackscattering from sea surface.

Viktor Gutnik received the M.S. degree in radio engineering in 1971 from KharkovPolytechnic Institute and the Ph.D. degree in radiophysics from the Institute forRadiophysics and Electronic NAS of Ukraine, Kharkov in 2001.

In 1998 he joined the Institute of Radio Astronomy, Kharkov, Ukraine.

Yuriy A. Tsarin (M07) received his M.S. (with distinction) and Ph.D. degrees in radiophysics from Kharkov State University in 1991 and 1998, respectively.

From the time of his masters graduation he has been almost continuously with theInstitute of Radio Astronomy, Kharkov, Ukraine in various positions - from PhDstudent to Head of the Telecommunication Technologies Lab (since 2007). He was avisiting scientist in TU Hamburg-Harburg in 1996-1997. He was employed in industryas a radar developer in 2001-2002 in Illinois, USA. His research interests includenonlinear dynamics, electromagnetic theory, and high-performance computing.

Dr. Tsarin is a member of the Ukrainian Physical Society.


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