real-time rfi detection and mitigation system for microwave radiometers

4928 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 51, NO. 10, OCTOBER 2013

Real-Time RFI Detection and MitigationSystem for Microwave Radiometers

Giuseppe F. Forte, Member, IEEE, Jorge Querol, Adriano Camps, Fellow, IEEE, and Mercè Vall-llossera

Abstract—Microwave radiometers are very sensitive passivesensors that measure the power of the thermal noise within a deter-mined bandwidth. Therefore, any other signal present in the bandmodifies the value of the measured power, and the correspondingestimated antenna temperature, from which the geophysical pa-rameters are retrieved. Due to the high sensitivity and accuracyrequired for these instruments, radio frequency interference (RFI)is becoming more and more a serious problem. On one hand,ground-based or global RFI surveys are helping to understand theoccurrence and types of RFI sources. If RFI does not necessarilyaffect the whole bandwidth, or it is not present during the wholeintegration time, the application of either frequency blanking,time blanking or signal spectrogram techniques can be applied.However, it would be desirable to apply techniques to estimate theRFI signal so that it can be subtracted from the received signalitself so that some useful measurements are still possible. Such areal-time system is currently being developed for RFI detectionand mitigation. This work focuses however in the description andperformance of a wavelet-based RFI-mitigation technique imple-mented in a FPGA hardware back-end. The interfering signalis estimated by using the powerful denoising capabilities of thewavelet transform, and it is then subtracted from the total receivedsignal to obtain a RFI-mitigated noise signal.

Index Terms—Denoising, detection, microwave radiometry,mitigation, radio frequency interference (RFI), wavelet.

I. INTRODUCTION

M ICROWAVE radiometry is routinely used today to ob-tain a number of geophysical parameters. Since it mea-

sures the thermal noise power, microwave radiometers arehighly sensitive and accurate passive instruments. Radio fre-quency interference (RFI) does not only concern microwaveradiometers, although they are more prone to suffer from RFI,since they are passive sensors. Due to its geographical andspectral extensions, RFI is becoming an increasing problem andefficient RFI detection and mitigation techniques are required[1]–[5].

RFI can come from spurious signals and harmonics fromlower frequency bands, from spread-spectrum signals overlap-ping the “protected” bands of operation, or from out-of-bandemissions not properly rejected by the pre-detection filters due

Manuscript received August 30, 2012; revised March 24, 2013; acceptedApril 27, 2013. Date of publication July 23, 2013; date of current versionSeptember 27, 2013. This work was supported in part by funds from theSpanish projects AYA2010-22062-C05-05 and AYA2011-29183-C02-01 fromthe Spanish Ministry of Science and Innovation and EU Feder.

The authors are with Remote Sensing Laboratory, Departament del Teoriadel Senyal i Comunicacions, Universitat Politècnica de Catalunya-BarcelonaTech and IEEC/UPC, 08034 Barcelona, Spain (e-mail: [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/TGRS.2013.2267595

to the finite rejection. In fact, it has also to be consideredthat the number of sources candidate to become unintentionalinterferers is large and growing. RFI sources have differentsignal structures, but the main effect is that they modify thedetected power and the corresponding antenna temperaturefrom which geophysical parameters are retrieved.

The RFI problem is especially important in populatedareas because most of the RFI comes from human activity,especially at low frequencies (e.g., L-band) due to the presenceof wireless devices, computers, etc. Furthermore, RFI may bepresent even during the calibration, producing a systematicerror in the whole data set.

Several techniques to detect the presence of RFI inradiometric measurements have been developed: time andfrequency domain analyses (e.g., [4]–[6]), statistical analysisof the received signal (e.g., [7]–[9]), and spectrogram analysis(e.g., [10], [11]). Some of these techniques have already beenimplemented in real systems. However, in all these techniques,when RFI is detected, and the signal is blanked either in thetime or the frequency domain, the capability to detect thepower of the signal is lost.

This work is focused in the hardware implementation of anew technique for RFI detection and mitigation [12]. Its mainadvantage, as compared to the previous ones, is that it alwaysproduces an output result, together with an estimation of theinterfering power. The most similar system is the adaptive noisecancelling [13] that consists of a separate, dedicated referencechannel used to obtain an independent estimate of the RFIsignal. There are two data channels: a main channel pointingto the source and containing the RFI signal; and a referencechannel (separated antenna pointing off source) that containsalso the RFI signal. Both channels contain the RFI signal,which are different due to the different propagation paths,but correlated as they come from the same source, making itpossible to eliminate the RFI from the received signal [14]. Theproblem with this method compared to wavelet denoise is thatRFI coming from different places complicates the system muchmore. In Section II, the proposed technique is described andalso the implementation of the concept. The actual hardwareimplementation is described in Section III. Finally, hardwareperformance results for different input test signals are shownin Section IV, and the results and the main conclusions aresummarized in Section V.

II. METHODOLOGY

In [12] a technique to mitigate RFI present in radiometricsignals was proposed and analyzed in detail. It is based on

0196-2892 © 2013 IEEE

FORTE et al.: REAL-TIME RFI DETECTION AND MITIGATION SYSTEM FOR MICROWAVE RADIOMETERS 4929

Fig. 1. RFI mitigation technique: an estimate of the RFI signal s(t) issubtracted from the received signal so as to obtain a quasi RFI-free noise signalfrom [12].

the estimation of the RFI signal using wavelet denoising tech-niques, and its subtraction from the original signal so as toobtain an estimate of the noise-only signal.

A. Denoising Principles

The interfering signal s(t) (RFI) is estimated (s(t)) withoutany a priori knowledge of it. This signal is then subtracted fromthe received signal x(t), to obtain a quasi RFI-free noise signalfrom which the power is detected (Fig. 1).

A Wavelet Transform decomposes a discrete signal f intoa first trend a1 and a first fluctuation d1 : f → (a1|d1). Be-cause Wavelet analysis is based on multi-resolution analy-sis, the Wavelet transform is applied recursively first to thesignal and then to the trend obtained from this transform.The process is repeated a number of steps L (levels): f →(aL|dL|dL−1|dL−2 . . . |d2|d1). After this, denoising is per-formed by neglecting or attenuating the coefficients that arebelow a level. The steps are the following:

• Computation of the wavelet transform of the data as theyenter. The appropriate wavelet family will be discussedlater.

• Computation of the threshold coefficients.• Computation of the inverse wavelet transform with the

remaining components after thresholding.• Subtraction of the denoised signal from the original one to

estimate the noise.

To have a system operating in real time without collapsingthe memory, the data must be processed before the next datablock enters, as sketched in Fig. 2.

B. Implementation Parameters Trade-Off

The parameters that can be selected to optimize the perfor-mance of the generic algorithm described before are analyzedin detail in [12] and are summarized below:

• Wavelet family or basis function used in the decomposi-tion. In [12] 75 different types of wavelet families wereanalyzed. It was found that the minimum interferencerejection (cancellation) is ∼40 dB for high interference tonoise ratios (INR ∼ 20 dB) when using the Haar wavelettransform, but this value increases up to ∼60–70 dB if theoptimum wavelet type for each signal is selected:— the symlet 3 for sinusoidal signals;— the reverse biorthogonal wavelets 1.5 for Doppler

signals;— the discrete approximation of Meyer wavelet for chirp

signals, and— the reverse biorthogonal wavelets 1.3 for PRN signals.

Fig. 2. Conceptual implementation of the pipelined method used to estimatethe interfering signal (“denoise”) with wavelets. The data appears at the sameclock rate with certain latency.

• Thresholding method and threshold value: The thresh-olding method can be either hard thresholding (sets to zerothe wavelet coefficients smaller than a given threshold)or soft thresholding (sets to zero the wavelet coefficientssmaller than a given threshold and shrinks the waveletcoefficients above that threshold). In [12] it was foundthat in general, the threshold value obtained using thesoft heuristic SURE (Stein’s Unbiased Risk Estimate)thresholding method [15] is the one that performs bestfor any type of signal, except for very weak RFI (INR ∼10−3 . . . 10−4) in which the fixed thresholding performsslightly better.

• Sequence length. In [12] it was found that the minimumnumber of samples per period is ∼20, for an interferencerejection of at least 20 dB using the Haar transform (de-pending on the signal type and the thresholding method).Higher rejections (≥ 40 dB) are achieved at the expenseof longer sequences, and—obviously—increased compu-tational load.

• Decomposition level or number of steps in which the pro-cess of decomposition into a signal trend and a fluctuationis repeated. In [12] it was found that increasing the numberof decomposition levels above 6–7 does not improve thequality of the RFI mitigation, except for PRN (pulsed)signals.

Other considerations:

• Noise correlation due to oversampling: As discussedbefore at least 20 samples are required per signal periodto achieve a good performance (interference rejection≥ 20 dB) of the RFI mitigation algorithm (or 300–400samples per period for an interference rejection ≥ 30 dB,and even more for higher interference rejection ratios),while the Nyquist sampling rate requires just 2 samples per


Fig. 3. Number of floating operations required by the RFI cancellationalgorithm (INR = 100) versus sequence length, for different wavelet types anddecomposition levels equal to a) 6 levels, and b) 12 levels (adapted from [12]).

period. The correlation between consecutive noise sampleswas analyzed in [12] and found to have a negligible impactin the interference mitigation (< 0.1 dB).

• Quantization effects: The analysis performed in [12]shows a negligible impact of the number of bits from4 to 16, only barely distinguishable for INR < −20 dB.

• Computational requirements are strongly dependent onthe wavelet type and sequence length. Results are shownin Fig. 3 (modified from [12]) for different wavelet types.There is an important tradeoff between the achievableinterference rejection (depending on the wavelet type andmainly sequence length), and the computational complex-ity which must be evaluated depending on the application.

For example, a system operating in the frequency bandfrom 1.400 to 1.427 MHz should require a samplingrate of at least 20 samples/period · 27 · 106 periods/s =540 MSamples/s to achieve a RFI rejection of at least20 dB. This high data rate can be reduced by processingthe in-phase and quadrature components of the receivedsignal separately (bandwidth is halved, but the number ofsignals in doubled: I and Q), or by sub-banding in as manybands as required by the hardware limitations.

After a careful analysis of the state of the art FPGAs, itwas considered that only a sub-optimum algorithm could beimplemented for a real-time application. The final selectedparameters are:

• Wavelet family: Due to the very demanding computa-tional requirements the Haar transform is selected, sinceit requires the simplest mathematical operations (sumsand differences). In this case, the Haar wavelet can beimplemented efficiently in real time operation hardware,and it requires fewer operations than other types of waveletfamilies. In addition, it was proved that it has good signalrejection rate against different kinds of RFI signals. Asexplained later, divisions by

√2 will not be performed

during the decomposition process, but will be taken intoaccount in the thresholding.

• Thresholding method and threshold value: A hardthresholding method is applied for simplicity, and to fa-cilitate the computation of the threshold (especially in areal-time application). While the SURE method performsbetter than the Universal method [15], [16], the last one ischosen because it can be implemented in hardware for realtime calculation.

• Number of samples: The number of samples is limited to217, which is a set of samples sufficient broad to estimateinterference signals with samples per period up to 105.

• Decomposition level: While the difference between 6and 12 decomposition levels is small [Fig. 3(a) and (b)]for coefficient multi scaling (different scaling for everydecomposition level), as compared to the dependence withthe wavelet family and sequence length, using coefficientsingle scaling the selected decomposition level is 12 to im-prove the performance in front of spread-spectrum signals.

• Number of bits: For RFI mitigation purposes 4 bits wasthe minimum required ±3.5 · σ ADC window [12]), butin [16] it was shown that for a ±4.05 · σ ADC windowand 8 bits quantization, the estimated antenna temperatureerror was smaller than 0.01 K. For convenience, in oursystem the default number of bits is set to 12 for eachADC, although it can be decreased internally to minimizememory requirements.

III. HARDWARE IMPLEMENTATION

A. Real-Time Computation of the Haar Transform Coefficients

A real-time wavelet transform has been implemented toobtain the Haar transform coefficients (trend and fluctuations)at one clock cycle after the last data sample enters in the set.In our case this means that every clock cycle after the 217thsample, all the coefficients have been calculated.

One thing to take into account is that classical Haar Wavelettransform requires a factor of

√2 in the denominator and it may

become an important computational effort for the hardware. Inthis implementation, this problem has been solved by applyingtwo times this

√2 factor when the inverse transform and

threshold coefficients are calculated. This leads to a scalingfactor of 2 that can be easily handled in binary systems.

Thus, the 1-level classical Haar transform coefficients are ob-tained as shown in (1), for m=1, 2, 3, . . . , N/2 where N=217

am =f2m−1 + f2m√

2(1a)

dm =f2m−1 − f2m√

2. (1b)


The next level is obtained by applying the transform tothe previous level trend coefficients only (am). From this thefollowing general formula is obtained (2), with l = level andm = 1, 2, 3, . . . , N/2:

alm =al−12m−1√2

+al−12m√2

(2a)

dlm =al−12m−1√2

− al−12m√2. (2b)

As has been noticed, since all operations are sums anddifferences the denominator can be extracted as shown in

kl =1

(√2)l

. (3)

Therefore, (2a) and (2b) become (4a) and (4b), provided thatthe factor in (3) is taken into account during the thresholdcoefficient calculation and the inverse transform process

alm = al−12m−1 + al−1

2m (4a)dlm = al−1

2m−1 − al−12m . (4b)

B. Computation of the Threshold Coefficients

For the sake of implementation efficiency the Universalmethod is used to compute the threshold. As shown in (6), itassigns a threshold level equal to the noise standard deviationtimes a constant, where l = level, and N = number of samples.Note that coefficient correction (

√2)l is applied because it was

removed when calculating the transform in (4a) and (4b)

Thresholdl = (√2)l ·

√2 · log(N) · σ(cl). (5)

The standard deviation is computed in real time using (6)which has while data is being processed

σ =

√√√√1

k

k∑i=1

c2i − c2. (6)

The total latency of the threshold coefficient computationstep is of the order of N clock cycles where N is the numberof samples per block (as said N = 217). These values aredetermined by the specific hardware implementation of theoperations.

C. Coefficient Shrinking Process

The coefficient shrinking process consists of discarding allfluctuation coefficients which are above the previously calcu-lated threshold value as in (7)

alk = alk (7a)

dlk =

{0, dlk >= Thresholdldlk, dlk < Thresholdl.

(7b)

This process is equivalent to estimate the RFI signal by keepingfluctuation coefficients above a threshold and subtracting thisvalue from the original input signal.

Fig. 4. Xilinx KC705 base board with the Kintex 7 FPGA used in this work.In top right the FPGA board with the ADC. Behind the FPGA, a signalgenerator to convert the signal to baseband and returning to the originalfrequency after removing the interference. Over the signal generator, a noisegenerator and interference generator. On top right an oscilloscope showing theoriginal interference on yellow and the estimated on cyan.

D. Inverse Haar Transform

The classical inverse Haar transform takes coefficients(a1|d1) back to the original signal f by means of

f=

(a1+d1√

2,a1−d1√

2, . . . ,

aN/2+dN/2√2

,aN/2−dN/2√

2

). (8)

For multilevel resolution, the highest level coefficients are sub-stituted, obtaining (9), with n = highest resolution level, andi = sample number

fi =an

kn+

n∑j=1

(1

kj

)·(−1(

I−1

2j−1 ))·(dj(

i+2j−1

2j

)) . (9)

As noticed at the beginning of this section, the inverse Haartransform must be calculated dividing by a factor of 2 for eachlevel of resolution instead of

√2. Hence, the calculation leads

to (10) for every single sample at the output of this system

fi =a

2L+

L∑j=1

((−1)�

i

2j�

2j· dj� i

2j�

), i=1, 2 . . . , N. (10)

E. Hardware Description

The hardware used for the data conversion and implemen-tation of the signal processing algorithms includes a XilinxKC705 base board with the Kintex 7 FPGA (Fig. 4).

The FMC150, red board in Fig. 4, is a four channel Analogto Digital Converter (ADC) and Digital to Analog Converter(DAC) FPGA Mezzanine Card (FMC). The card provides two14-bit A/D channels and two 16-bit D/A channels which canbe clocked by an internal clock source, optionally locked toan external reference, or an externally supplied sample clock.The design is based on a dual channel 14-bit 250 Msps ADCand a dual channel 16-bit 800 Msps DAC. The FMC150 allowsa flexible control of sampling frequency, analog input gain,and offset correction through serial communication. Its blockdiagram is shown in Fig. 5.


Fig. 5. FMC150 ADC/DAC block diagram.

IV. PERFORMANCE RESULTS

To have a well-defined and controlled evaluation scenario,numerically generated signals (noise plus different types ofinterference at different power levels) have been uploaded intothe FPGA memory for later processing. So far, simulations andhardware tests include sinusoidal interfering signals at powerlevels from −30 dB to +30 dB with respect to the noise power,for different samples per period from 1 to 4 • 109 (this value isunrealistically high, but is included to show the saturation effectcaused by the limitation of the number of samples used in thecalculation).

In Fig. 6(a), it is seen that for a 20 samples per period(or sequence length), sinusoidal RFI of INR = +20 dB isattenuated by almost a factor of 20, while for INR = 0 dB,the attenuation is just a factor of 2. To increase the attenuationof the interference, the number of samples per period must beincreased, e.g., for 100 samples per period, sinusoidal RFI ofINR = +20 dB is attenuated by a factor of ∼19 dB, whilefor INR = +0 dB, the attenuation is a factor of 6 dB, and for1000 samples per period, sinusoidal RFI of INR = +20 dB isattenuated by a factor of 27 dB, while for INR = +0 dB, theattenuation is a factor of 17 dB. To compare with other type ofsignals, Pseudo-Random Noise (PRN) RFI mitigation is shownin Fig. 6(b), and it saturates faster than sinusoidal RFI. Medianfiltering method for RFI mitigation is shown in Fig. 6(c) and isnoticeable that saturates much more easily than wavelet denoisemethod.

Fig. 7(a)–(f) illustrate the performance and results of theRFI estimation process with different INR for a set of 221

samples, with 36 Kilo Samples per period (18000 times Nyquistsampling frequency). Fig. 7(g) and (h) show the results whenthe number of samples is 4096 with just 36 samples per period(18 times Nyquist sampling frequency), the minimum requiredfor an effective threshold estimation. Note that the performanceof the technique improves for higher INRs. Mitigating sinu-soidal RFI is shown in Fig. 7, and PRN RFI on Fig. 8.

Using residual σ2RFI the attenuation is calculated for signals

presented in Figs. 7 and 8. For Fig. 7(a)–(f) where 36 KS perperiod is used, the attenuation for 20 dB, 0 dB and −30 dBinterferences are 35 dB, 20 dB and 7 dB, respectively. ForFig. 7(g) and (h) where 36 samples per period are used, theattenuation for 20 dB interferences is 12 dB.

Fig. 6. Normalized residual RFI power (σ2noise = 1) versus normalized

digital frequency defined as Fs = 1/(Samples per period) for different INR:+30 dB (black); +20 dB (blue), +10 dB (green); 0 dB (red); −10 dB(cyan); −20 dB (magenta), and −30 dB (yellow) for different interferences andmethods: (a) Sinusoidal, (b) PRN interferences using Haar Wavelet Denoise,and (c) PRN interference using Median filtering technique.


Fig. 7. Sample estimation of a sinusoidal RFI with different interference-to-noise ratios. X axis is sample number and Y axis amplitude (arbitrary units σ2noise=1,

σ2RFI = INR · σ2

noise). The colors are as follows: original noise is yellow, composite signal is red, recovered noise is cyan, modeled interference is blue andoriginal interference is green. (a) INR = +20 dB with 36 Kilo Samples per period, (b) zoom of (a). (c) INR = 0 dB with 36 Kilo Samples per period, (d) zoomof (c). (e) INR = −30 dB with 36 Kilo Samples per period, (f) zoom of (e). (g) INR = +20 dB but with only 36 samples per period, (h) zoom of (g).


Fig. 8. Sample estimation of a PRN RFI with different interference-to-noise ratios. X axis is sample number and Y axis amplitude (arbitrary units σ2noise = 1,

σ2RFI = INR · σ2

noise). The colors are as follows: original noise is yellow, composite signal is red, recovered noise is cyan, modeled interference is blue and orig-inal interference is green. (a) INR = −30 dB with 36 Kilo Samples per period, (b) zoom of (a). (c) INR = 0 dB with 36 Kilo Samples per period, (d) zoom of (c).

Note that the estimation of weak RFI (e.g., sinusoidal RFIwith INR = −30 dB) requires sets of length = 221, with atleast 36 000 samples per signal period for an effective thresholdestimation [12]. This is not feasible for nowadays FPGA’s blockRAM cells (e.g., the new most powerful Virtex 7 has 68 Mbits,at most). To overcome this problem, a FPGA board with amultibank DDR memory is used. The number of banks must beat least the pipeline levels multiplied by the read/write cycles ofthe memory. This improvement to the system proposed in [17]allows the system to continuously acquire, process, and writethe total amount of data needed to mitigate sinusoidal RFI withINR of ∼−30 dB.

V. CONCLUSION

A real-time wavelet denoising-based RFI-mitigation sys-tem has been implemented in a Kintex 7 FPGA. The clockfrequency of the whole system, including the ADC, is

245.75 MHz, giving an estimated latency between the sampleand its interference mitigated of approximately 534 μs.

FPGA results using data (noise + interference) created usingsignal generators for a well-defined and controlled evaluationscenario show that RFI with larger INR is easily mitigatedwith the presented wavelet technique. However, it also allowsrejecting RFI signals with lower INR even if their power isbelow the noise power.

RFI with low INR can be mitigated by increasing the numberof samples and the number of samples per period. Futureresearch will include the performance analysis in front of otherinterfering signals.

The large capacity of the Kintex 7 FPGA allows includingother types of RFI detection/mitigation algorithms as well, suchas statistical methods and spectrogram analysis, whose im-plementation is underway. Also, an improved algorithm usingexternal DDR RAM is being developed, to allow using longersequences and improve the RFI rejection for low INR.


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Giuseppe F. Forte (M’11) was born in GuatemalaCity, Guatemala, in 1979. He received the degreein electronics engineering form the UniversidadFrancisco Marroquín, Guatemala City, Guatemala, in2002. In 2005, he received the Advanced Studies inElectronic Engineering diploma from the UniversitatRovira i Virgili, Tarragona, Spain. He is currentlyworking toward the Ph.D. degree in telecommunica-tions engineering from the Universitat Politècnica deCatalunya (UPC), Barcelona, Spain.

Since 2010 he has been involved in the RemoteSensing Laboratory of the UPC, performing the design and implementationof algorithms, electronics and field experiments related to remote sensing andradio frequency interference detection and mitigation.

Jorge Querol was born in Forcall, Castelló, Spain,in 1987. He received the B.S. degree in electronicsengineering and the B.S. degree in telecommuni-cation engineering from the Universitat Politècnicade Catalunya (UPC), Barcelona, Spain, in 2011 and2012, respectively. He is currently working towardthe M.S. degree in photonics at UPC.

He did an internship with the Institut de Cièn-cies Fotòniques, Castelldefels, Spain, during Sum-mer 2010, developing signal processing algorithmsfor medical imaging. From 2011 to 2012, he was

with the Kungliga Tekniska Högskolan, Stockholm, Sweden, with an Erasmusfellowship. There, he collaborated with the Network Systems Laboratoryworking with control algorithms for efficient energy storage and distributionsystems. Since 2012, he is working on his M.S. Thesis at the Teoria del Senyali Comunicacions department, UPC, implementing real-time wavelet-based RFImitigation algorithms for microwave radiometry applications.

In 2012, he was the recipient of the Lear Corporation award to the bestacademic records of the year in electronics engineering.

Adriano Camps (S’91–A’97–M’00–SM’03–F’11)was born in Barcelona, Spain, in 1969. He receivedthe M.S. degree in telecommunications engineeringand Ph.D. degree in telecommunications engineeringfrom the Universitat Politècnica de Catalunya (UPC),Barcelona, Spain, in 1992 and 1996, respectively.

In 1991 to 1992, he was at the ENS des Télé-communications de Bretagne, Brest, France, withan Erasmus Fellowship. Since 1993, he has beenwith the Electromagnetics and Photonics Engineer-ing Group, Department of Signal Theory and Com-

munications, UPC, where he was first Assistant Professor, Associate Professorin 1997, and Full Professor since 2007. In 1999, he was on sabbatical leave atthe Microwave Remote Sensing Laboratory, of the University of Massachusetts,Amherst, MA, USA. Since 1993, he has been deeply involved in the Euro-pean Space Agency SMOS Earth Explorer Mission, from the instrument andalgorithmic points of view, performing field experiments, and more recentlystudying the use of GNSS-R techniques to perform the sea state correctionneeded to retrieve salinity from radiometric observations. His research interestsare focused in microwave remote sensing, with special emphasis in microwaveradiometry by aperture synthesis techniques and remote sensing using signalsof opportunity (GNSS-R).

Dr. Camps has received a number of national and international awards fortechnical and scientific contributions to the fields of microwave interferometricradiometry and GNSS-R, as well for teaching innovation in the new curriculaat Telecom Barcelona. He has published more than 110 peer-reviewed journalpapers, and more than 250 international conference presentations.

Mercè Vall-llossera received the B.S. and Ph.D.degrees in telecommunication engineering in 1990and 1994 at the Polytechnic University of Catalonia(UPC), Barcelona, Spain.

She has been lecturing and doing research at theDepartment of Signal Theory and Communicationsat the UPC from 1990 to 1997 as Assistant Professorand from 1997 until present as Associate Profes-sor. She spent a sabbatical year at the ConcordiaUniversity (Montreal) with the scholarship of the“Programme Québécois de Bourses d’excellence”

(1996–97): “Stages de Formation postdoctorale au Québec pour jeunesdiplômés étrangers” applying the FDTD method in the analysis of the effectof mobile telephone to the body. At the beginning her research activities wererelated to numerical methods in electromagnetics and antenna analysis anddesign. At present, from 2000 her research is ainly related to passive remotesensing, geophysical parameters retrieval: soil moisture and ocean salinityretrieval from L-band radiometric measurements. She has been participatingin several projects for the preparation of the Soil Moisture and Ocean Salinity(SMOS) mission by ESA.

real-time rfi detection and mitigation system for microwave radiometers

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