Characterization and Mitigation of RFI Signals in Radar Depth Sounder Data of Greenland Ice Sheet

1060 IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY, VOL. 55, NO. 6, DECEMBER 2013

Characterization and Mitigation of RFI Signals inRadar Depth Sounder Data of Greenland Ice SheetKevin Player, Theresa Stumpf, Jie-Bang Yan, Member, IEEE, Fernando Rodriguez-Morales, Member, IEEE,

John Paden, Member, IEEE, and Sivaprasad Gogineni, Fellow, IEEE

Abstract—Accurate measurements of fast flowing outlet glaciersin Greenland and Antarctica are of vital importance to improveice-sheet models that predict the ice-sheets’ contribution to sea-level rise over the next century. Radars with high sensitivity andadvanced processing capabilities are required to sound ice in fast-flowing glaciers. We developed the multichannel coherent radardepth sounder/imager (MCoRDS/I) for ice thickness measure-ments and 3-D imaging of bedrock over important areas of Green-land and Antarctica. Radio frequency interference (RFI) degradesthe sensitivity of the MCoRDS/I system, thus affecting the qualityof the data collected. Data from the 2010 Greenland field season ex-hibited a degraded signal-to-noise ratio (SNR), requiring extensiveRFI analysis, investigation, and mitigation for the MCoRDS/I sys-tem. Measurements were taken in an electromagnetic interference(EMI) chamber on individual sections of the MCoRDS/I system toisolate the sources of RFI. Then, RFI mitigation techniques wereimplemented for the offending sections and EMI chamber mea-surements verified the integrity of the solution prior to the 2011Greenland deployment. Recorded data from the 2011 Greenlandfield season also verified that the RFI mitigation resulted in morethan 20 dB improvement in the SNR compared to the 2010 Green-land data. The reduction of EMI emissions has also been beneficialto data collection in subsequent deployments.

Index Terms—Radar depth sounder, radar noise analysis,radar signal processing, radio frequency interference (RFI), RFImitigation.

I. INTRODUCTION

IN much of the central portion of the Greenland and Antarc-tic ice sheets, where the ice is cold and slow moving, exist-

ing radar instruments and techniques are sufficient for sound-ing and imaging the bed. However, in regions with warm andfast moving ice, such as outlet glaciers, large attenuation, andoff-vertical, rough-surface scattering makes radar sounding ofthese regions challenging. The dynamic nature and influenceof these outlet glaciers on the short- and long-term stability ofthe ice sheets mean that accurate or complete measurements oftheir thickness and shape are important to developing improved

Manuscript received September 7, 2012; revised February 18, 2013; acceptedApril 15, 2013. Date of publication June 7, 2013; date of current versionDecember 10, 2013. This work was supported in part by the National ScienceFoundation under Grant ANT-0424589.

The authors are with the Center for Remote Sensing of Ice Sheets (CRe-SIS), University of Kansas, Lawrence, KS 66045 USA (e-mail: [email protected]; [email protected]; [email protected]; [email protected];[email protected]; [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TEMC.2013.2265046

ice-sheet models to quantify the contribution of large ice sheetsto sea-level rise in a warm climate [1].

The Center for Remote Sensing of Ice Sheets (CReSIS) at theUniversity of Kansas relies on the multichannel coherent radardepth sounder/imager (MCoRDS/I) radar to obtain data fromthe bed and deep internal layers of the Greenland and Antarcticice sheets. The depth sounder is capable of detecting weak bedechoes (∼100 dB below the thermal noise floor) for soundingice that may be up to 5-km thick. However, the high sensitivityalso makes the radar susceptible to radio frequency interference(RFI). RFI degrades the radar’s sensitivity and creates a ma-jor challenge for the unambiguous detection of weak echoesfrom the ice-bed interface of fast-flowing glaciers and ice-sheetmargins.

The characterization and development of algorithms to re-duce RFI in ultra-wideband radars have been investigated ex-tensively [2]–[8]. Much of the earlier work was focused onthe development of algorithms to reduce narrowband RFI inradar data using adaptive filters. Our study deals with the char-acterization of broadband RFI and its reduction using carefulsubsystem modifications, as well as the application of a digitalfilter to reduce narrowband RFI in data.

This paper summarizes an analysis focused on characterizinginterference signals in MCoRDS/I data taken from the NASA P-3B aircraft during the 2010 and 2011 Operation Ice Bridge (OIB)campaigns in Greenland. The paper is organized as follows.Section II discusses the purpose of the RFI analysis and mit-igation performed. Section III describes the basic componentsand features of the MCoRDS/I system. Section IV expoundson the primary metric used to characterize RFI signals and theanalytical process for determining the processing parametersfor analyzing raw radar data. Section V discusses the techniqueused for identifying possible sources of RFI. Section VI intro-duces the hardware strategies and signal-processing techniquesemployed to mitigate the RFI. Section VII characterizes andcompares the radar data from the 2010 and 2011 Greenlandfield seasons. Finally, Section VIII provides the results and adiscussion of the improvements.

II. MOTIVATION

CReSIS designed and developed a radar and a large-antennaarray for operation on the NASA P-3B aircraft. The system wasdeployed as part of the NASA OIB campaign during March–May 2010. During this deployment, we found that broadbandRFI degraded the performance of the MCoRDS/I system sub-stantially. Spectral densities estimated from data taken in thesouth of Greenland over the ice sheet on May 15, 2010 show

0018-9375 © 2013 IEEE

PLAYER et al.: CHARACTERIZATION AND MITIGATION OF RFI SIGNALS IN RADAR DEPTH SOUNDER DATA OF GREENLAND ICE SHEET 1061

Fig. 1. Broadband RFI with nonuniform amplitude as captured by MCoRDS/Iduring the 2010 Greenland campaign.

broadband interference over the frequency range from 180 to210 MHz as shown in Fig. 1. These results were generatedby analyzing data in a time window with no target returns.The radar is designed to obtain a minimum detectable signal(MDS) after pulse compression and synthetic aperture radar(SAR) processing of about –240 dBW/Hz. Thus, the results inFig. 1 indicate that radar sensitivity in the frequency band ofoperation (180–210 MHz) was degraded by more than 30 dB.This RFI was present for most of the data recorded during the2010 Greenland campaign. The purpose of this study was toidentify sources of RFI and mitigate their effect using hardwareand software techniques.

III. MCORDS/I SYSTEM DESCRIPTION

The MCoRDS/I was operated on the NASA P-3B aircraftin 2010 with a large-antenna array consisting of 15 elements.Seven of these elements were mounted under the aircraft fuse-lage and the remaining eight elements under the wings, withfour under each wing, as shown in Fig. 2. The middle seven el-ements mounted under the fuselage were used for transmissionand all15 elements were used to receive reflected and scatteredsignals. The received signals from the 15 elements were timemultiplexed into eight receivers, digitized, presumed, and storedfor further processing. The MCoRDS/I was operated with a sep-arate 15-channel receiver, as illustrated in Fig. 3 during the 2011field season. The basic radar system parameters used for datacollection during 2010 and 2011 are given in Table I.

IV. CHARACTERIZATION OF RFI SIGNALS USING RADAR DATA

A. Radar Data Signal Model

Each recorded signal, Srec(t), is modeled as a sum of signalsfrom three sources as

Srec(t) = SRx(t) + Sn (t) + SRFI(t) (1)

Fig. 2. Antenna configuration for MCoRDS/I on the NASA P-3B aircraft.

Fig. 3. MCoRDS/I system block diagram.

TABLE IBASIC RADAR PARAMETERS

where SRx(t) is the received echo from the target, Sn (t) isthe thermal noise of the system, and SRFI(t) is the RFI signal.To estimate SRFI(t), a window must be chosen that does notcontain backscattered signals. In the application of sounding icesheets, we can assume the final echo for a given record to bethe reflected and scattered return from the ice-bed interface. Theoptimum window in this case corresponds to a window startingat the postbed-echo region of the record and ending at the start ofthe subsequent record. Within this window, SRx(t) is assumedto be zero and the raw data reflect a sum of the RFI signals and


the thermal noise of the system as follows:

Srec(t) = Sn (t) + SRFI(t). (2)

The thermal noise of the system may be modeled as band-limitedGaussian noise. The one-sided spectral density of the noise floor,in watts/hertz, is given in [9] and presented as follows:

Snn (f) =

{kT0NF, fc − B

2< f < fc +

B

20, elsewhere

(3)

where k = 1.3807 × 10−23 J/K (Boltzmann’s constant), T0 isthe receiver temperature in kelvin, B is the receiver Bandwidthin hertz and NF is the receiver noise figure.

This spectrum may be used in verification of thermal noisedata measured by the radar itself. In the field, Sn (t) is measuredexperimentally by disconnecting the antennas from the radar andterminating the receiver with a 50-Ω load. These data are usedas a reference or baseline in identifying the spectral distributionof the RFI. Since the thermal noise spectrum may be measureddirectly from the 50-Ω data, spectral components (broadband ornarrowband) above the baseline are assumed to be RFI.

B. Determining an Appropriate Fast-Time Window

The primary metric used to characterize RFI signals is theestimated, one-sided, auto-spectral density [10] of raw RFI datarecorded by the depth sounder in the field. Raw RFI data areobtained by truncating recorded radar data in the fast time, afterthe bed-echo to obtain an RFI processing block.

To avoid contamination of approximated interference sampleswith chirp energy arriving after tstart (e.g., due to off-nadirbackscattering), we assume a guard band interval, tgb , to accountfor scattered signals from the ice-bed interface. We can thenestimate the realistic time corresponding to the start of the regionin the record containing RFI signals and thermal noise, tstart-act ,using the following expression:

tstart-act = tsurf + tice + tp + tgb (4)

=2Ralt

c+

2Rice−max√

εice

c+ tp + tgb (5)

where tsurf is the two-way travel time of the pulse from theairborne platform to the ice surface, tice is the two-way propa-gation time of the transmit chirp signal through ice of maximumthickness Rice-max , Ralt is the typical platform altitude, c is thespeed of light in free space (3 × 108 m/s), εice is the dielectricconstant of ice (3.15), tp is the transmit pulse length, and tgbis the duration of the guard band in seconds. Finally, let tRFI(in seconds) represent the fast time interval containing only RFIsignals and thermal noise. This interval is defined as

tRFI ≥ tstart−act . (6)

C. Determining Processing Gain Required for Analysis

Characterizing interference signals requires introducing again term, Ganalysis , into the spectral estimation procedure toaccount for the improvement in the SNR that results from ap-plying signal-processing techniques to the received signals. This

is achieved by both coherent and incoherent averaging. A gen-eralized approximation of gain required for the analysis of in-terference signals in terms of a field season’s specific radarconfiguration parameters and geometry is evaluated in dB usingthe following expression:

(Ganalysis)dB ≈ (GPC)dB + (GSAR)dB − (Gpresums)dB(7)

where GPC is the pulse compression gain, GSAR is the SARprocessing gain, and Gpresums is the gain due to the hardwarepresums used in flight, which are part of the radar’s configura-tion. GPC is directly related by the time-bandwidth product ofthe transmitted chirp, which is given by

GPC = 10 log10 (Btp) . (8)

GSAR is determined by the number of along-track (or slow-time)samples NA expected in the synthetized aperture, which can becalculated using the along-track sampling rate pulse repetitionfrequency (PRF),

GSAR = 10 log10 (NA ) (9)

= 10 log10

(2PRF (Ralt + Rice- max) tan

(θA

2

)(v) (nw )

)(10)

where θA denotes the along-track beam-width, ν is the platformvelocity, and nw is the number of distinct transmit waveforms.Finally, Gpresums is simply given by

Gpresums = 10 log10 (p) (11)

where p is the number of hardware presums in the radar config-uration file.

These results suggest that because of signal-processing tech-niques, the radar is sensitive to RFI signals lying below the noisefloor of the receiver’s front-end. Understanding SNR improve-ment due to signal processing is critical to the characterizationof these signals in depth sounder data because it defines the levelof signals capable of degrading the radar’s performance.

Indeed, for typical signal-processing gain values, the min-imum detectable signal for this radar is about –160 dBm(1 × 10−20 W). Assuming a typical antenna gain of 7 dBi (withground plane), the corresponding electric field intensity that canbe detected is about 6.3 nV/m. This is four orders of magni-tude smaller than the allowed radiation for personal computers,which are allowed to radiate up to 150 μV/m at a distance of3 m [13].

V. IDENTIFICATION OF POTENTIAL RFI SOURCES

In order to determine which section of the MCoRDS/I systemwas radiating interference signals, the entire radar system wastaken into an electromagnetic interference (EMI) chamber fortesting. The various radar subsystems were powered on indi-vidually, while an EMI receiving antenna installed inside thechamber was used to monitor the emissions. Table II providesa list of each radar subsystem tested. Fig. 4 shows a block dia-gram of the experimental setup. Two different sets of tests werecompleted. In the first set, the radar equipment under test wasplaced inside an EMI table top test cell. A spectrum analyzer


TABLE IIMCORDS/I SUBSYSTEMS / POWER SUPPLIES TESTED

Fig. 4. EMI measurement setup.

Fig. 5. Measured RFI for the RF Subsystem, NI digitizer, digital subsystems,and 28-V power supply.

installed outside the chamber was used to record the strongestinterference signals from the radar components.

Fig. 5 shows the received signal as detected by the spec-trum analyzer when different parts of the MCoRDS/I systemare turned on. This plot reveals that one of the strongest sourcesof EMI was the high current, 28-V switching power supply usedfor the drain bias of the high-power RF amplifiers Other sub-systems produced narrowband EMI with spectral componentsof about 10 dB above the instrument noise floor. To increase thesensitivity of the measurement to about –160 dBm in a secondset of measurements, a digital receiver similar to that employedwith the radar was used to coherently digitize the EMI signals

Fig. 6. Photograph of the MCoRDS/I system as deployed in 2010 showingunshielded cables.

Fig. 7. Photograph of the MCoRDS/I system as deployed in 2011 showingshielded cables and an upgraded digital system.

(triggering to the radar’s PRF) and record the data for postpro-cessing. A preamplifier stage with about 50 dB of gain was usedin cascade with the antenna to bring the output noise of thereceiver above the quantization noise of the analog-to-digital(A/D) converter. Analysis of these data revealed that other partsof the system, such as the high-speed connection between the re-dundant array of independent disks (RAID) storage unit and themultichannel digitizer, produced a large number of narrowbandspectral components that needed to be suppressed.

VI. RFI MITIGATION TECHNIQUES

A. Hardware Strategies for Suppressing RFI

Given that the 28-V switching supply radiated the largest in-terference signals, it was determined that the best solution wouldbe to replace the 28-V switching supply with a 28-V linearsupply. Furthermore, since the power amplifiers were drawingpulsed current in relation to the PRF, large pulsed currents were


Fig. 8. Measured RFI for the 28-V switching power supply with unshieldedand shielded cables.

carried from the 28-V supply to the power amplifier box. There-fore, all the dc power wires were wrapped with a Zippertubingproduct [11] that could be zipped around the existing wiringharnesses.

The Zippertubing shielding comprised of a metallic meshgrounded at both ends to the power connectors and a rubberjacket to protect the mesh material. A visual comparison of thedc wiring harnesses is shown in Figs. 6 and 7 for unshielded andshielded dc wiring harnesses, respectively, as measured with thespectrum analyzer.

The effectiveness of the shielding was initially determined byperforming the same EMI measurements as above, except withall the dc wiring harnesses shielded. Then the difference in theradiated power level of the 28-V switching supply was calcu-lated, as shown in Fig. 8. The shielding effectively lowered theRFI generated by the 28-V switching supply dc wiring harnessby as much as 23 dB with an average shielding effectiveness ofabout 8 dB.

Since the power amplifiers were drawing pulsed current inrelation to the radar’s PRF, large pulsed currents (>25 A) werecarried from the 28-V supply to the power amplifier box. Furthernoise suppression is achieved by 0/pi interpulse phase modula-tion [14] which is a feature built into the radar’s digital system.Additional reduction in emissions was achieved by upgradingthe digital system to a more compact unit, which eliminated twohigh-current power supplies and unshielded power harnesses.Other parts of the system, such as data cables, were shieldedin a similar fashion. Custom-made shielding enclosures wereadded to the RAID storage units to minimize RFI.

B. Software Strategies for Suppressing RFI

Software techniques may also be applied in combination withthe hardware strategies for further RFI reduction. In the case ofour radar system, the receiver may be susceptible to narrowbandinterference signals from near-by radio communication systems,broadcasting stations, critical avionics subsystems, stroboscopiclights, or leakage from local oscillators. Since it is not feasibleto shield all these interfering sources from unknown directions,a digital filter may be implemented to filter out these undesir-able interferences. It is well known that an adaptive line en-hancer (ALE) [12] is capable of removing a narrowband signalembedded in a wideband signal (or vice versa), due to the dif-

Fig. 9. Block diagram of the implemented ALE.

Fig. 10. GPS flight path for the P-3B on May 12, 2010 (dash) and April 12,2011 (solid).

ference between the self-correlation lengths of the two signals.We can, therefore, design a digital ALE that operates on thereceived wideband chirp signal contaminated by narrowbandRFI. Fig. 9 shows the architecture of the ALE implemented inour MATLAB toolbox. By appropriately selecting the decorre-lation delay, the FIR filter length, and the adaptation step size,any stationary narrowband RFI can be estimated and subtractedfrom the desired chirp signal return.

VII. CHARACTERIZATION AND COMPARISON OF RADAR DATA

FROM 2010 AND 2011 GREENLAND MISSIONS

To perform a comparison of the EMI present in radar databefore and after the RFI detection and mitigation techniqueswere applied, we estimated that spectra were generated fromdepth sounder data taken from the 2010 and 2011 OIB missionsin Greenland on the NASA P-3B aircraft over nearly coincidentflight paths.

As explained in Section IV-B, the fast-time window used toisolate the RFI was determined to be approximately 61.86 μs.Measured records from both seasons are shorter in time than thecalculated, ideal start of the processing window, meaning that


Fig. 11. (a) Broadband RFI as captured during the May 12, 2010 flight and (b) narrowband RFI as captured on the during the April 12, 2011 flight.

Fig. 12. GPS flight path for P-3B on (a) May 12, 2010 and (b) April 12, 2011 (red-bold lines indicate the flight tracks along which the radar echograms weretaken).

there are less data available containing only RFI and thermalnoise. Typical record lengths were 60 μs in 2010 and 55.7 μsin 2011. Thus, a shorter guard band, tgb , is assumed in order toadapt to shorter record length. Approximately the last 6 μs ofeach record were used to generate spectral density estimates.

Data segments obtained over outlet glaciers, which are char-acterized by their rough surface topography and wet ice, intro-duce potential clutter and therefore are not used to characterizeRFI signals. Instead data obtained from cold ice in the middle ofthe ice sheet are preferred, especially where ice thickness doesnot exactly equal the maximum ice thickness value of 3.2 km forGreenland. According to the analysis in Section IV, to realizethe minimum SNR improvement, Ganalysis requires a minimumof 10 000 along-track coherent averages over 1 000 000 consec-utive records or range lines.

The characterization is based on the analysis of samples takenfrom May 12, 2010 of the Greenland 2010 mission and April12, 2011 of the Greenland 2011 mission in a colocated region ofSouthern Greenland, as shown in Fig. 10. The estimated noisespectra of the two days are given in Fig. 11. The 2010 season

was characterized by broadband interference that degrades depthsounder SNR by more than 20 dB in certain bands. Indeed,data from some other records (different flight path) of the sameseason even showed as much as 30 dB SNR degradation (e.g.,data shown in Fig. 1).

With the hardware techniques for reducing RFI described inSection VI-A, we were able to achieve a significant reductionof RFI in the 2011 season, as can be seen in Fig. 11(b). It ishowever observed that there remains a relatively strong narrow-band interferer at about 194 MHz in the measured spectrum.We conducted further testing that showed that this narrowbandtone is leaked from one of the local oscillators in our system.Nevertheless, such a narrowband interference signal can be fil-tered out easily during postprocessing with the ALE mentionedin Section VI-B. The resultant spectrum after filtering is alsogiven in Fig. 11(b). The spikes at around 194 and 197 MHzwere almost completely removed.

Finally, we would like to compare the resultant radarechograms obtained on May 12, 2010 and April 12, 2011along two very similar flight tracks as depicted in Fig. 12. The


Fig. 13. Radar echograms taken along the flight track shown in (a) May 12, 2010 and (b) April 12, 2011.

corresponding echograms are given in Fig. 13. The broadbandRFI contained in the 2010 data manifested itself as verticalstreaks as shown in Fig. 13(a). The artifacts caused by RFI alsomasked a slight portion of the ice bed return. On the other hand,with only the hardware approach taken for RFI suppression inthe 2011 field season, we were already able to obtain a cleanechogram that clearly lays out the ice bed.

VIII. CONCLUSION

We developed a metric to characterize RFI signals and ananalytical process for determining the parameters required forprocessing raw radar data for noise analysis. EMI shieldingand RFI suppression in our high power airborne radar depthsounder/imager have been demonstrated experimentally viaboth hardware and software approaches. Compared to the dataobtained in the 2010 field season, we are able to achieve morethan 20 dB SNR improvement over the operating band from180 to 210 MHz in the 2011 field season. In future work, wewill investigate the potential improvement of the resultant icesheets echogram data offered by the EMI suppression strategiesdiscussed in this paper.

ACKNOWLEDGMENT

The authors would like to thank the crew and pilots fromNASA for sponsoring the P3-B aircraft and for their supportduring the field experiments. We would also like to thank K.Ireland and the staff at the Mobile Technology Laboratory atSprint Applied Research and Advanced Technology Labs forproviding access to the EMI chamber. We gratefully acknowl-edge current and former students and staff at CReSIS, particu-larly Prof. R. Hale, P. Place, D. Gomez-Garcia, R. Crowe, andE. Arnold, who took part in the 2010 and 2011 OIB Greenlandmissions, and who helped with the measurements at Sprint.

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Kevin Player received the B.S. and M.S. degrees inelectrical engineering from the University of Kansas,Lawrence, USA, in 2007 and 2010, respectively.

From 2007 to 2010, he was a Research Assistant,as well as, an Associate Engineer from 2011 to 2012at the Center for Remote Sensing of Ice Sheets (CRe-SIS), University of Kansas, assisting in developingand operating radar systems. His primary researchfocus was in high power, pulsed amplifiers for radarapplications.


Theresa Stumpf is currently working toward thePh.D. degree in electrical engineering at the Univer-sity of Kansas, Lawrence, USA. Her dissertation re-search focuses on ultra-wideband, wide-swath radarimaging of the ice-bed interface.

In 2010, she was a Graduate Research Assistant atthe Center for Remote Sensing of Ice Sheets (CRe-SIS), University of Kansas. In the spring of 2012,she participated in NASA’s Operation IceBridge cam-paign in Greenland by processing CReSIS radar datain the field. Her research interests include radar imag-

ing, MIMO radar, and adaptive signal processing.

Jie-Bang Yan (S’09–M’11) received the B.Eng. de-gree (first class Hons.) in electronic and communica-tions engineering from the University of Hong Kong,Hong Kong, in 2006, the M.Phil. degree in electronicand computer engineering from the Hong Kong Uni-versity of Science and Technology, Hong Kong, in2008, and the Ph.D. degree in electrical and com-puter engineering from the University of Illinois atUrbana-Champaign, Urbana, USA, in 2011.

He was a Croucher Scholar from 2009 to 2011during his Ph.D. study at Illinois. Upon graduation,

he joined the Center for Remote Sensing of Ice Sheets (CReSIS), Universityof Kansas, as an Assistant Research Professor. He holds two U.S. patents anda U.S. patent application related to novel antenna technologies. His researchinterests include design and analysis of MIMO and reconfigurable antennas,antennas and phased arrays for radar systems, RF propagation, and fabricationof on-chip antennas.

Dr. Yan received the Best Paper Award at the 2007 IEEE (HK) AP/MTTPostgraduate Conference and the 2011 Raj Mittra Outstanding Research Awardat Illinois. He serves as a technical reviewer for several journals and conferenceson antennas and electromagnetics. He is a Technical Program Committee Mem-ber of the 2013 IEEE Conference on Microwaves, Communications, Antennasand Electronic Systems (COMCAS), and a local organizing committee mem-ber of the 2013 IGS International Symposium on Radioglaciology: advances inradio frequency, microwave, and digital technologies.

Fernando Rodriguez-Morales (S’00–M’07) re-ceived the B.S. degree (cum laude) in electron-ics engineering from the Universidad AutonomaMetropolitana, Mexico City, Mexico, in 1999, and theM.Sc. and Ph.D. degrees in electrical and computerengineering from the University of Massachusettsat Amherst, Amherst, USA, in 2003 and 2007,respectively.

From 2000 to 2001, he was in the Department ofPhysics and Astronomy, University of Massachusettsat Amherst, where he collaborated in the develop-

ment of instrumentation for millimeter-wave astronomy. From 2001 to 2006,he was in the Department of Electrical and Computer Engineering, Universityof Massachusetts at Amherst, developing submillimeter-wave receivers. Since2007, he has been with the Center for Remote Sensing of Ice Sheets (CReSIS),headquartered at the University of Kansas, Lawrence, USA, where he supportsand develops RF/microwave sensors and participates in field experiments inPolar Regions.

Dr. Rodriguez-Morales received the Graduate Fellowship from the NationalCouncil for Science and Technology of Mexico.

John Paden (S’95–M’06) received the M.S. andPh.D. degrees in electrical engineering from the Uni-versity of Kansas, Lawrence, USA, studied multi-static 3-D imaging algorithms for remote sensingthrough media and leading the development of twomultichannel radar systems for imaging through icesheets, one broadband system for ground operationand another for airborne operation.

After graduation, he joined Vexcel Corporation, aremote sensing company in Boulder, CO, USA, andworked as a Systems Engineer and a SAR Engineer

for three and a half years before rejoining CReSIS as a Faculty Member in early2010 to lead the signal and data processing efforts. He is currently a ResearchFaculty Member at the Center for Remote Sensing of Ice Sheets (CReSIS),University of Kansas. His research includes field work in Chile, Greenland, andAntarctica.

Sivaprasad Gogineni (M’84–SM’92–F’99) re-ceived the B.E. degree from the University of Mysore,Mysore, India, in 1973, the M.Sc. degree from Ker-ala University, Thiruvananthapuram, India, in 1976,and the Ph.D. degree from the University of Kansas,Lawrence, USA, in 1984.

He was a Manager of the Polar Program, NationalAeronautics and Space Administration, during 1997–1999. He was a Fulbright Senior Scholar at the Uni-versity of Tasmania, Hobart, Australia, in 2002. He iscurrently the Deane Ackers Distinguished Professor

in the Electrical Engineering and Computer Science Department, Universityof Kansas, where he also is the Director of the National Science FoundationScience and Technology Center for Remote Sensing of Ice Sheets. He has beeninvolved with radar sounding and imaging of ice sheets for more than 15 yearsand contributed to the first successful demonstration of synthetic aperture radarimaging of the ice bed through more than 3-km-thick ice. He has authored orcoauthored over 90 archival journal publications and more than 200 technicalreports and conference presentations.

Dr. Gogineni is a member of the URSI Commission F, the American Geo-physical Union, the International Glaciological Society, and the Remote Sensingand Photogrammetry Society. In 1991, he was awarded the Miller Award forEngineering and Research at the University of Kansas, and the Taylor and Fran-cis Publishers Award for Best Letter published in the International Journal ofRemote Sensing. From 1994 to 1997, he was the Editor of the Newsletter of theIEEE Geoscience and Remote Sensing Society. He received the Best-of-SessionAward from the Third International Airborne Remote Sensing Conference in1997 and was given the NASA Terra Award in 1998. In 1999, he was awardedthe United States patent no. 5 867 117 for a swept-step radar system and itsdetection method. He received the Miller Professional Development Awardfor distinguished service to the engineering profession from the University ofKansas in 2000. In 2002, he received the Louise Byrd Graduate Educator Awardat the University of Kansas.

Characterization and Mitigation of RFI Signals in Radar Depth Sounder Data of Greenland Ice Sheet

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