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IEEE ACCESS, VOL. XX, XXXXX 2018 1
Synthetic Aperture Radar imaging system for
landmine detection using a Ground Penetrating
Radar on board an Unmanned Aerial Vehicle
Mar´
ıa Garc´
ıa Fernandez, Student Member, IEEE, Yuri ´
Alvarez L´
opez, Senior Member, IEEE, Ana Arboleya
Arboleya, Borja Gonz´
alez Vald´
es, Yolanda Rodr´
ıguez Vaqueiro, Fernando Las-Heras
Andr´
es, Senior Member, IEEE, and Antonio Pino Garc´
ıa, Senior Member, IEEE
Abstract—This work presents a novel system to obtain images
from the underground based on Ground Penetrating Radar
(GPR). The proposed system is composed by a radar module
mounted on board an Unmanned Aerial Vehicle (UAV), which
allows the safe inspection of difficult-to-access areas without being
in direct contact with the soil. Therefore, it can be used to
detect dangerous buried objects, such as landmines. The radar
measurements are coherently combined using a Synthetic Aper-
ture Radar (SAR) algorithm, which requires cm-level accuracy
positioning system. In addition, a clutter removal technique is
applied to mitigate the reflection at the air-soil interface (which is
caused by impedance mismatching). Besides the aforementioned
advantages, the system can detect both metallic and dielectric
targets (due to the use of a radar instead of a metal detector)
and it allows to obtain high-resolution underground images (due
to the SAR processing). The algorithms and the UAV payload
are validated with measurements in both controlled and real
scenarios, showing the feasibility of the proposed system.
Index Terms—Ground Penetrating Radar (GPR), subsurface
sensing and imaging, Synthetic Aperture Radar (SAR), landmine
detection, Unmanned Aerial Vehicle (UAV), drones, Real Time
Kinematic (RTK).
I. INT ROD UC TI ON
THERE has been a massive introduction of UAV-based
systems for remote sensing applications in the last decade
[1], thanks to the improvements in technical features such
as avionics and propulsion systems, capacity of batteries,
M. Garc´
ıa, Y. ´
Alvarez and F. Las-Heras are with ´
Area de Teor´
ıa
de la Se˜
nal y Comunicaciones, Departamento de Ingenier´
ıa El´
ectrica,
Universidad de Oviedo. Gij´
on, 33203 (Asturias), Spain. Email:
{garciafmaria,alvarezyuri,flasheras}@uniovi.es.
A. Arboleya was with Universidad de Oviedo when the measurement
campaign was done but she is now with Departamento de Teor´
ıa de la Se˜
nal
y las Comunicaciones y Sistemas Telem´
aticos y Computaci´
on, Universidad
Rey Juan Carlos. Madrid, Spain. Email: ana.arboleya@urjc.es.
B. Gonz´
alez, Y. R. Vaqueiro, and A. Pino are with Departa-
mento de Teoria do Sinal e Comunicacions, Universidade de Vigo.
Campus Universitario, s/n, 36310 Vigo, Pontevedra, Spain. Email:
{bgvaldes,yrvaqueiro,agpino}@com.uvigo.es.
This work has been supported by the Ministerio de Econom´
ıa y Competi-
tividad - Gobierno de Espa˜
na under projects TEC2014-54005-P (MIRIIEM),
TEC2014-55290-JIN (PORTEMVISION), TEC2015-73908-JIN, TEC2015-
65353-R, and RYC-2016-20280; by the Ministerio de Educaci´
on - Gob-
ierno de Espa˜
na under grant FPU15/06341; by the Gobierno del Princi-
pado de Asturias through the PCTI 2013-2017, FC-15-GRUPIN14-114, and
IDI/2017/000095; and by the Galician Regional Government under project
GRC2015/018 and under agreement for funding AtlantTIC (Atlantic Research
Center for Information and Communication Technologies). This work has
been developed under the framework of the Universidad de Oviedo postdoc-
toral degree Expert in Remotely Piloted and Autonomous Flight Aircrafts.
autonomous navigation capabilities, and ease of sensor in-
tegration, together with a significant reduction in their cost.
These achievements have fostered the use of UAVs in fields
such as precision agriculture and forestry monitoring [2], [3],
and in glaciology [4], where factors such as remoteness and
severe weather conditions limit the extent of human-assisted
measurement campaigns.
Small, lightweight UAVs (less than 3 kg) are being in-
troduced for airborne Synthetic Aperture Radar (SAR)-based
terrain observation, avoiding the need of large aircrafts (es-
pecially for monitoring small size areas). For example, in [5]
a polarimetric radar mounted on a UAV for SAR imaging
applications is described. Similar to other UAV-based SAR
imaging systems [6], [7], it has a Global Navigation Satellite
System (GNSS) receiver and an Inertial Measurement Unit
(IMU) which provide in-flight guidance and positioning infor-
mation. While range resolution is given by the radar band-
width, ranging from few cm [6], [7] to 1-2 m [5], cross-range
resolution is limited by measurement geo-referring uncertainty.
In the case of conventional GNSS receivers, typical uncertainty
ranges from 1 to 3 m.
UAVs have been also proved to be of great help for elec-
tromagnetic compatibility and antenna measurements [8], [9].
In this case, the use of positioning and geo-referring systems
capable of providing cm-level accuracy enabled working at
higher frequency bands, as long as the wavelength is larger
than the positioning and geo-referring uncertainty [8], [9].
Finally, UAV-assisted applications in the area of communica-
tions, mainly devoted to improve connectivity in remote areas,
are being developed [10], [11].
A. Landmine detection: systems and methods
Detection of concealed objects in an opaque medium using
Non-Destructive Testing (NDT) techniques has been of great
interest in sectors such as mining and geology, civil engi-
neering and civil works, and archaeology [12]. These NDT
techniques allow to detect, locate, and, eventually, obtain an
image of the concealed object, avoiding the interaction with
both the object and the surrounding medium [13]. The main
advantages are scanning time and cost savings, as invasive
excavations in the area of interest to search for the objects
are not required, also preventing accidental damaging. Among
the aforementioned fields of application, there are some sce-
narios where the concealed objects are a threat in case of
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2 IEEE ACCESS, VOL. XX, XXXXX 2018
accidental contact, such as weapons or explosives. In these
cases, detection and location have to be carried out under safe
conditions for both the scanning device and the operators. One
of the scenarios of interest is landmine detection. Landmines
cause about 4000 deaths and injuries every year, 90 per
cent corresponds to civilians, happening in those 60 countries
where part of their territory is affected by the deployment
of this kind of countermeasure. The number of landmines
worldwide is estimated between 60 and 70 million. Only in
2016 the total global clearance of landmines was about 170
km2, with at least 232000 landmines destroyed [14].
Landmine detection methods can be classified in two main
groups: invasive and non-invasive techniques. Invasive tech-
niques are based on a contact device capable of detonating
the mines [15]. The main disadvantage of these systems is
their impact in the scanned area, as they plow the terrain
while scanning, as well as their limited lifespan. The advantage
is their fast scanning speed, up to 1 square meter per 0.73
seconds. On the other hand, non-invasive techniques allow to
detect the presence of concealed objects thanks to an ade-
quate processing of the received signals. These non-invasive
techniques can be sorted according to the physical principle
in which the detection method is based [16].
i) Electromagnetic induction: based on inducing an electric
current in the concealed metallic objects using a transmitting
coil. The induced current re-radiates an electric field which
is detected by a receiving coil. The main advantage of this
system is its low cost and simplicity. However, it suffers from
a high false-alarm rate when several metallic objects are also
in the scenario under test (shrapnel, bolts, etc.).
ii) Nuclear Quadrupole Resonance (NQR): based on the
detection of the radiofrequency signals emitted by certain
substances that are likely to be in explosive materials. This
technique has high probability of detection, but it involves the
use of complex devices.
iii) Thermal imaging: infrarred sensors are capable of
detecting the different thermal behavior of landmines with
respect to the surrounding medium. In particular, thermal im-
age time series acquisition is proposed in [17], using thermal
response analysis in the time domain to detect landmines. The
main weakness of this methodology is the dependence with
weather conditions that affect soil thermal conductivity, and
thus the thermal contrast between soil and buried landmines.
iv) Ground Penetrating Radar (GPR): it has been considered
as one of the best techniques for underground imaging thanks
to the capability of creating images of the soil and the objects
buried in it [13]. In consequence, GPR has been widely used
for landmine detection [18], [19], [20], [21]. GPR is based on
emitting electromagnetic waves to the soil, whose reflection at
the soil and at potential concealed objects allows to recover a
radar image where these concealed objects can be identified.
It must be remarked that GPR is quite sensitive to the soil
composition and the air-soil interface roughness, requiring
additional signal processing techniques for image artifacts and
clutter removal.
Regardless the operating principle, the application of non-
invasive techniques for landmine detection requires the scan-
ning system to be placed at a safety distance with respect to the
potential placement of the landmine, typically 3-5 m, to avoid
the accidental detonation of the landmine by the scanning
device. To achieve this goal there are several possibilities:
i) Forward-looking radar systems, where the transmitting
antenna illuminates the soil under an angle of incidence such
as the injected power in the soil is maximized [22], [23]. In
this case, due to the angle between the radar and the soil, only
part of the reflected energy is backscattered towards the radar,
thus requiring higher dynamic range in the receiver to detect
the buried targets.
ii) Downward-looking systems, where the incident wave
direction is perpendicular to the soil surface [22], [24]. In this
case, the fact that the transmitted power is not maximized is
partially compensated thanks to the shorter distance between
the radar and the soil; and also the backscattered power is
directed towards the radar (although it also depends on the
geometry of the buried target). In this kind of systems, the
challenge is to achieve normal incidence while keeping the
security distance of 3-5 m. One solution is based on small
lightweight unmanned autonomous robots, capable of perform-
ing detection with a minimum landmine detonation risk [25],
[26]. In these systems, transmitting and receiving antennas are
placed in the air-soil interface at different positions separated
half wavelength, so that the coherent combination of the
received signal at each position results in a bi-dimensional
radar image (in range or depth, and cross-range or movement
direction of the robot). However, the main limitations are
the slow scanning speed (around 5 cm per second) and the
maximum weight of the entire robot to avoid accidental
detonation.
An alternative to the use of terrestrial detection vehicles
and their limitations in terms of scanning speed (and risk of
detonation as they are in touch with the soil) is given by
airborne devices. Among them, Unmanned Aerial Vehicles
(UAVs) or commonly drones have been considered of great
relevance in multiple fields thanks to their versatility and low
cost.
B. Unmanned Aerial Systems for landmine detection
Improvements in UAV technology have made possible the
development of UAV-assisted landmine detection systems, as
they exhibit disruptive advantages such as: i) higher scanning
speed compared to existing solutions in the market based
on autonomous robots; ii) possibility of inspection of remote
areas, unaccessible with other systems; and iii) higher safety
throughout the scanning process, especially when looking for
explosives, since contact with soil is avoided.
A prototype consisting of a metal detector onboard a UAV
that also includes a robotic arm capable of placing a remotely
controlled detonator to blow out the landmine is described in
[27]. This system provides contactless (and thus safe) and fast
scanning capabilities. However, metal detectors cannot distin-
guish between different kinds of metallic targets. Furthermore,
non-metallic buried explosives cannot be detected.
Latest advances for landmine detection are based on placing
a GPR onboard a UAV [28], [29], [30], [31], [32]. The
implemented prototypes are mostly based on a compact GPR
2169-3536 (c) 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
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M. GARC´
IA FERN ´
ANDEZ et al.: SAR IMAGING SYSTEM FOR LANDMINE DETECTION USING A GPR ON BOARD A UAV 3
unit that forwards geo-referred measurements to a ground
station for post-processing and results displaying. Again,
cross-range (horizontal) resolution is limited by positioning
and geo-referring accuracy, mostly relying on GNSS receivers
integrated within the UAV controller. In consequence, these
state-of-the-art systems have been proved to be effective for
detecting buried targets larger than 25-30 cm, and/or exhibiting
significant contrast with the medium (e.g. metallic targets
buried in clay or sand).
However, existing UAV-based GPR systems do not provide
high resolution subsurface images as they do not support SAR
imaging capabilities, that is, GPR measurements collected at
each position of the flying path cannot be coherently com-
bined. This is because positioning and geo-referring accuracy
using GNSS-based techniques is in the order of 50-60 cm
in the best case. Thus, enabling SAR imaging techniques
(i.e. coherent combination of measurements) requires the use
of cm- or mm- level accuracy geo-referring and positioning
techniques.
C. Aim and scope of this contribution
Aiming to overcome the limitations in terms of detection
capabilities of current UAV-based GPR imaging system, this
contribution introduces a system and method for high accuracy
underground SAR imaging, conceptually depicted in Fig. 1.
The developed technology allows the UAV to autonomously
explore a particular area using GNSS coordinates, while trans-
mitting and receiving radio signals using a radar module. The
collected data includes timestamps to enable synchronization
and is sent in real time to a computer, where it is processed to
generate SAR images of the subsurface with a resolution of
centimetres. In addition, algorithms for proper characterization
of the soil and clutter removal have been implemented.
The main innovation of this contribution is the capability
of using SAR-based techniques for subsurface imaging with
range and cross-range resolution of a few cm, overcoming the
limitation of current UAV-based GPR systems where coherent
combination of measurements taken at different positions (i.e.
creating a synthetic aperture) is not possible.
Fig. 1. Concept of the UAV-based GPR system for underground SAR imaging.
II. ME TH OD OL OG Y
As opposed to conventional SAR imaging, where targets
are above the ground, the main purpose of Underground-SAR
[33], [34] is to reconstruct images of underground targets,
taking into account the different wave velocity in the air
and in the soil. Microwave imaging of the ground and the
objects buried in it can be performed by means of SAR-based
algorithms such as migration techniques [35], Delay-And-Sum
(DAS) [36], or Wiener filter-based SAR [37], among others. In
all these cases, soil wave velocity has to be properly estimated
in order to provide a well-focused image and to reduce
false alarms. From the knowledge of the soil constitutive
parameters, namely conductivity and permittivity, soil wave
velocity can be estimated.
Assuming a multiple quasi-monostatic configuration (i.e. the
transmitting and receiving antennas are almost at the same lo-
cation), the basic principle of underground SAR imaging is as
follows: given a set of scattered field measurements collected
on Macquisition points and Nfrequencies, Escatt(rm, fn),
the reflectivity at a single point ρ(r0)can be calculated as
indicated in Eq. 1:
ρ(r0) =
M
X
m=1
N
X
n=1
Escatt(rm, fn)e+j2(φ0+φ1)(1)
where rmis the position of the m-th acquisition point, fn
is the n-th frequency and φ0, φ1are the phase-shifts due to
the wave propagation in the air and in the soil, as depicted in
Fig. 2. These terms are defined in Eq. 2 and Eq. 3:
φ0=k0,n||ri−rm||2(2)
φ1=k0,n√εr||r0−ri||2(3)
k0,n is the free-space wavenumber for the n-th discrete
frequency, εris the relative permittivity of the soil and riis
the refraction point at the air-ground interface, as indicated in
Fig. 2. The refraction point, whose calculation requires solving
a fourth order equation derived from Snell’s law, is estimated
using an iterative algorithm. In case of using a time-domain
acquisition, a Fourier transform is applied to the collected
measurements before the SAR processing.
This simple Delay-And-Sum (DAS) formulation is based
on a coherent combination of the measurements taken at
different rmpositions. Note that the only restriction is that
acquisition points must fulfill Nyquist sampling rate, that is,
the separation between two consecutive points must be smaller
than λmin/2, with λmin =c/fN(cis the speed of light
in free space). In addition to this, acquisition points have
to be accurately geo-referred to minimize uncertainties that
will distort the recovered SAR image. For this purpose, geo-
referring uncertainty should be better than λmin/4in cross-
range and λmin/8in range.
With respect to conventional point-to-point SAR back-
propagation, coherent combination of multiple measurements
improves cross-range resolution. Free-space range ∆rand
cross-range ∆lresolution (under free-space consideration) are
given by Eq. 4 and Eq. 5:
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4 IEEE ACCESS, VOL. XX, XXXXX 2018
∆r=c
2(fN−f1)(4)
∆l=Rλc
2Lap
(5)
where Ris the distance from the radar to the target, λc=
2c/(f1+fN)is the wavelength at the center frequency, and
Lap is the synthetic aperture width.
Fig. 2. Underground SAR imaging technique using an airborne GPR.
As mentioned before, soil characterization is required to get
an estimate of εr. This characterization can be done indirectly
from datasheets generated from previous measurements [38],
[39], [40], or by means of in-situ measurements, which are
more suitable for practical operation of the airborne radar
proposed in this contribution. Methodologies based on GPR
measurements to estimate conductivity and permittivity have
been proposed in [34], [41]. Basically, if the depth of a refer-
ence target is known, then, the permittivity can be estimated
by comparing the distance where the buried target is detected
(decho) with its true depth (dtarg et). Thus, the permittivity is
given by Eq. 6:
εr= (decho/dtarg et)2(6)
If the soil permittivity cannot be estimated, it can be
assumed εr= 1, that corresponds to the case in which
conventional SAR imaging is applied to the soil medium.
Then, the echoes of targets buried in the soil will appear
displaced downwards in the SAR image with respect to their
true position due to the slower propagation speed of the waves
in the soil.
The strong clutter produced by the specular reflection from
the ground surface (i.e. air-soil interface) is one of the main
issues for accurate detection of buried objects using GPR
imaging. Several clutter removal techniques have been pro-
posed, such as time-gating [42], average substraction, and
subspace projection methods [43].
In this contribution, time-gating and average subtraction
techniques are used to improve the quality in the reconstructed
SAR image. Both techniques are applied to the measurements
in the distance domain (which is equivalent to the time domain,
taking into account the relationship between the two-way
distance and the time r=c t/2). With the time-gating
technique, only the reflected signal between 20 cm and 4m
(away from the antennas) is selected. This helps to remove the
coupling between the transmitter and receiver antennas as well
as the effects of radiofrequency cables connecting the antennas
and the radar module. Then, the average of all measurements
along the whole aperture is computed and subtracted from
each measurement, as given by Eq. 7, helping to improve the
contrast in the image and mitigating the clutter.
e
Escatt(rm, r) = Escatt(rm, r)−1
M
M
X
m=1
Escatt(rm, r)(7)
A flowchart of the methodology is shown in Fig. 3, where it
has been assumed that the radar signal is acquired in the time
domain (as in the presented prototype). First, time gating is
applied to each measurement. Then, once all the measurements
have been acquired, their average is computed and subtracted
from each measurement. It must be noticed that the average
is also a time domain signal. Finally, the Fourier Transform
is applied before performing the underground SAR (U-SAR)
processing.
𝐸𝑠𝑐𝑎𝑡𝑡(𝑟
𝑚, 𝑟0)
෨
𝐸𝑠𝑐𝑎𝑡𝑡(𝑟
𝑚, 𝑟)
m-th measurement
Time Gating
𝐸𝑠𝑐𝑎𝑡𝑡(𝑟
𝑚, 𝑟)
𝐸𝑠𝑐𝑎𝑡𝑡(𝑟
𝑚, 𝑓)
Average Subtraction
Fourier Transform
U-SAR processing
𝜌
Average
Computation
Initial Preprocessing
Preprocessing after all
measurements are gathered
Processing
Fig. 3. Flowchart of the methodology.
III. UAV-BAS ED U ND ER GRO UN D SAR IM AGI NG S YS TE M
IM PL EM EN TATION
The proposed airborne-based GPR imaging system for de-
tection of buried objects is composed by the following devices,
represented in Fig. 4 scheme (grouped by subsystems):
•Flight control subsystem, which consists of a micro-
computer (Raspberry Pi), a UAV flight controller and
common positioning sensors (IMU, barometer, GNSS).
•Communication subsystem.
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M. GARC´
IA FERN ´
ANDEZ et al.: SAR IMAGING SYSTEM FOR LANDMINE DETECTION USING A GPR ON BOARD A UAV 5
•Accurate positioning subsystem to provide cm-level ac-
curacy. It includes a Real Time Kinematic (RTK) system
and a LIDAR (Light Detection And Ranging) altimeter.
There are two RTK beacons: one on the UAV and another
on the ground at a fixed position.
•Radar subsystem.
•A ground station (e.g. a laptop), which receives radar
measurements and positioning and geo-referring infor-
mation, and processes it to map radar measurements with
centimeter-level accuracy. Geo-referred measurements are
processed together with the underground SAR imaging
algorithm to create radar images of the soil and objects
buried in it.
Fig. 4. Scheme describing the implementation of the airborne-based GPR.
Description of the connection between different subsystems and devices of
the prototypes.
A UAV model with a payload up to 5 kg has been acquired
[44] to have enough capacity for further improvements of
the prototype with additional sensors or devices. This UAV
provides around 15 min flight with a 2-3 kg payload, which
is enough for initial validation flight tests.
A lightweight, compact impulse radar working in the 3.1
to 5.1 GHz frequency band [45] has been selected, aiming
to obtain a trade-off between range resolution (∆r= 7.5
cm according to Eq. 4), ease of integration in the UAV,
and penetration depth. Radar transmitting and receiving ports
are connected to two customized helix antennas, one having
right-handed circular polarization (RHCP), and the other left-
handed circular polarization (LHCP). It must be noticed that
since the antennas have orthogonal polarizations and their
cross polar discrimination (XPD) is good (around 24 dB at
central frequency), the direct coupling between the antennas
is mitigated, which helps to improve the quality of the results.
These antennas are well matched between 3 to 6 GHz,
having θ−3dB = 47 degrees beamwidth, thus resulting in
D= 12.7dB directivity. As in the case of the radar, there
is a trade-off between the antenna size and its directivity.
Nevertheless, cross-range resolution ∆lgiven by the helix
antenna beamwidth is further improved by means of SAR
techniques.
Communication between UAV, RTK beacons, and the
ground station is managed through a Wireless Local Area
Network (WLAN), deploying a wireless router close to the
area to be scanned to provide coverage to the ground station,
the UAV, and the RTK ground beacon. WLAN operating
frequency can be set to 2.4 GHz or 5.8 GHz as those
frequencies do not interfere with the radar frequency band.
In any case, radar antennas are directive and always pointing
towards the ground so, even in the case of sharing the same
frequency band, co-channel interference would be negligible.
Also, aiming to minimize interference, UAV transmitter and
receiver modules are set to work at 433 MHz.
Concerning UAV positioning and geo-referring system,
RTK [46] has been selected as it provides cm-level accuracy
and ease of deployment and integration within the UAV
controller. RTK ground beacon forwards the corrections that
must be applied to the GNSS signal to the RTK beacon placed
in the UAV (rover beacon) in real time. The latter uses these
corrections to improve the position accuracy down to cm-level.
RTK positioning uncertainty indicated by the manufacturer
[46] is σx=σy= 1.5cm, σz= 3 cm. Taking into account
the maximum working frequency of the radar, f= 5.1GHz,
uncertainty in the horizontal (XY) plane (i.e. in cross-range)
is 0.26λmin for any arbitrary direction in this plane, and in
height (z axis, i.e. range) it is 0.51λmin. Although absolute
positioning error in the horizontal plane is worse than λmin/8,
it must be taken into account that the relative error between
adjacent positions is much smaller than λmin/8, thus enabling
coherent combination of the measurements.
As the positioning uncertainty is twice in the vertical
axis, a more accurate height measurement sensor is required.
Among different possibilities, a LIDAR altimeter [47] has
been chosen, as it is more robust and accurate than an
ultrasound sensor of similar size and cost. The selected LIDAR
altimeter has σz= 1.8cm height measurement uncertainty,
that is 0.31λmin. Again, the relative error between adjacent
positions is much smaller than λmin/8and thus, it does not
significantly affect the results. Nevertheless, it must be noted
that the maximum synthetic aperture length will be limited by
cumulative geo-referring errors.
A picture of the UAV with all the devices and modules
integrated (ready-for-operation configuration) is shown in Fig.
5. In-flight operation mode of the system can be watched at
https://youtu.be/gsKptOPVARI.
IV. SYS TE M VALI DATION
For a proper validation of the airborne-based GPR system,
validation and testing has been divided into several stages:
i) Validation in a controlled environment of the radar mod-
ule [45]: measurements have been conducted using a planar
measurement range [48]. Different kinds of soils (sand, loam,
mixed) have been evaluated aiming to determine the capability
of recovering constitutive parameters of the medium as well
as testing the performance of the radar for detecting buried
objects. Methodology and results have been presented in [34].
ii) On-ground validation: once the radar module has been
tested in a controlled environment, validation in a realistic
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6 IEEE ACCESS, VOL. XX, XXXXX 2018
GNSS antenna
WiFi antenna RTK antenna
External Compass
RTK module
Radar module
RC Receiver
UAV controller
Helix antennas
Fig. 5. Implemented prototype.
scenario has been carried out. In this stage, the main goal is
to evaluate the capability of the system to create underground
SAR images using geo-referring information provided by the
positioning systems onboard the UAV.
iii) In-flight tests: last step is the integration of the payload
(namely the radar module and some of the sensors of the
positioning subsystem) into the UAV. An extensive validation
campaign for different scenarios has been conducted to ensure
proper functionality of the implemented system.
For this first prototype of airborne-based GPR the selected
frequency band (3.1 - 5.1 GHz) limits its range of application
to low loss soils. Thus, the results presented in this contribution
will be devoted to sandy soils, with permittivity (εr) ranging
from 2.5 to 4 (depending on the degree of water moisture) and
conductivity (σ) lower than 0.01 S/m.
A. On-ground testing
For the validation of the radar module in a realistic sce-
nario (sandy beach, coordinates 43.533, -5.383), a homemade
portable linear scanner has been used. The radar module [45]
and the helix antennas are mounted on a portable platform
that can be manually displaced along two parallel plastic bars
placed 50 cm above ground and parallel to it. Measurements
were taken along 1 m distance, geo-referring them by means
of the RTK system. The RTK rover beacon was placed on the
portable platform, and the RTK ground beacon around 20 m
away. Measurements and RTK coordinates were sent to the
ground station using a wireless link. A general overview of
the setup is depicted in Fig. 6.
To test the detection capability of the radar, a metallic disc
(of 9 cm radius and 1 cm thickness) was buried at dobj = 15
cm in a sandy soil (with estimated permittivity εr= 3.5 [34],
[40]) as depicted in Fig. 7 (a). In order to illustrate the average
subtraction procedure, the average is shown in Fig. 7 (b), and
the imaging results with and without average subtraction are
compared.
First, imaging results were obtained by just representing
the envelope of the collected measurements (obtained using
the Hilbert transform). This will be called point-to-point
backpropagation, since the measurements are not combined to
improve the resolution of the image. Imaging results are shown
in Fig. 7 (c) and (d), without performing average subtraction
and performing it. In both cases, a buried target is observed,
although its depth and size do not match the true ones. As
expected, the average subtraction helps to improve the quality
of the imaging results. Next, measurements were processed
with the underground SAR imaging algorithm and assuming
εr= 1 for the sand (that is, free-space). Coherent combination
of the measurements taken at each position was done using
the coordinates provided by the RTK system. Underground
SAR imaging results are shown in Fig. 7 (e) and (f), without
and with average subtraction, respectively. Clearly the air-
sand interface can be distinguished, as well as the buried
metallic disc at approximately decho = 28 cm depth, deeper
than expected as free-space conditions were considered in
the underground SAR imaging. Roughness of the air-sand
interface results in a non-uniform backscattering, so the air-
sand interface appears as a non-regular contour in the SAR
image.
Underground SAR imaging results considering εr= 3.5
are depicted in Fig. 7 (g). In this case, the metallic disk is
imaged at the correct depth of decho ≈dobj = 15 cm, as the
relative permittivity of the sand is taken into account in the
underground SAR imaging.
RTK Base Station
RTK antenna
RTK module
RTK module
RTK antenna
Radar module
GPS antenna WiFi antenna
UAV controller
Helix antennas
Fig. 6. Setup for on-ground validation and testing of the GPR. RTK is used
for geo-referring the measurements.
B. In-flight tests and results
Once the payload was properly tested, it was mounted
onboard the UAV for in-flight tests. Positioning subsystem then
comprises RTK [46], LIDAR altimeter [47], and default UAV
positioning systems (inertial sensors, barometer, and standard
GNSS receiver). Combination of the positioning information
provided by these sensors resulted in an accuracy better than
1.5 cm in x, y, and z axes.
Radar measurements are provided at a rate of 50 samples/s,
whereas positioning information is obtained at a rate of 10 Hz.
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M. GARC´
IA FERN ´
ANDEZ et al.: SAR IMAGING SYSTEM FOR LANDMINE DETECTION USING A GPR ON BOARD A UAV 7
Fig. 7. Underground SAR imaging of a buried metallic target. (a) Picture of
the metallic disc buried dobj = 15 cm in the sand. (b) Envelope of the average
signal of all the measurements. (c)-(d) Imaging results applying point-to-point
backpropagation: (c) without removing the average, and (d) removing the
average. (e)-(f) Imaging results applying SAR, considering soil permittivity
εr= 1: (e) without removing the average, and (f) removing the average. (g)
Imaging results applying SAR, assuming soil permittivity to be εr= 3.5and
removing the average.
This value determines the fastest scanning speed of the UAV,
which for the flight test presented in this section is kept below
30 cm/s (1.1 km/h). At that speed, UAV position information
is updated, on average, every time the UAV moves 3 cm in
the horizontal plane (that is 0.5λmin). UAV coordinates are
linearly interpolated in order to use all the radar measurements.
UAV can be operated manually (GNSS-assisted flight
mode), where the operator controls UAV yaw, pitch, and roll
axes. Another possible operation mode is based on waypoints:
a flight path covering the area to be scanned is created, then
uploaded into the UAV controller, so the UAV operator is
just in charge of take off and landing operations. For the
sake of simplicity, in-flight tests presented in this contribution
were done in manual operation mode. Besides, in the case
of straight line flight paths, no significant differences in the
flight path were found between manual and waypoint-based
flight operation.
In-flight tests were done at the airfield for UAVs of the
University of Oviedo, located at (43.522, -5.624). Before
taking off, it was verified that all the systems and subsystems
worked properly. This verification was performed again after
taking off. Measurement acquisition starts when taking off
and finishes when landing. Furthermore, the acquisition can
be remotely controlled from the ground station. To verify the
capability of the system for in-flight SAR imaging (as done in
[5], [6]), a 1-m long and 6 cm wide metallic bar was placed on
the ground, perpendicular to the UAV flight path, as depicted
in Fig. 8 (a). Several forward and backward flights following
a straight path have been done, keeping a flight altitude of
approximately 75 cm above ground (not too low to avoid
turbulences due to the ground effect).
First, point-to-point backpropagation results are depicted in
Fig. 8 (b), where it can be observed that the air - ground
interface is fairly noticeable: again, the roughness of the
ground and the grass create non-specular reflections. The
metallic bar is clearly visible as it exhibits higher reflectivity
than the ground, apart from the fact that the flat face of the
metallic bar is parallel to the UAV flight path. The width of
the metallic bar observed in Fig. 8 (b) clearly exceeds the
true 6 cm width. Next, SAR imaging is applied to process
the measurements, combining them coherently according to
the coordinates provided by the positioning subsystem. Re-
sults depicted in Fig. 8 (c) show that, when applying SAR
imaging techniques, the detected metallic plate is narrower, in
agreement with the true width of 6 cm, proving the feasibility
of performing SAR imaging with the implemented airborne-
based radar system.
Assuming that cumulative geo-referring uncertainty still al-
lows coherent combination of measurements along Lap = 1 m,
then theoretical cross-range resolution (Eq. 5) for R= 75 cm
is ∆l= 2.25 cm. This cross-range resolution is significantly
smaller than the projected beam of the helix antenna on the
ground (R cos(θ−3dB ) = 51 cm), consistent with the imaging
results of the bar.
Next, the airborne GPR system was tested for detecting
buried objects. A 78 cm x 56 cm x 43 cm plastic box was
fully filled with sand (with εr= 2.5, as it has a different
composition than the sandy soil of Section IV-A). As digging
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8 IEEE ACCESS, VOL. XX, XXXXX 2018
Fig. 8. (a) Picture of the prototype at the airfield and the metallic bar on
the ground. (b) Imaging results applying point-to-point backpropagation. (c)
Imaging results applying SAR (coherent combination of the measurements).
is not allowed in the airfield, the sandbox was placed on
the ground. During flight operation, UAV tries to maintain
a constant height over the ground, taking into account the
distance to the ground measured by the LIDAR altimeter.
Preliminary tests of the UAV when flying over the sandbox
revealed that the sharp height variation from the ground to
top of the sandbox caused the UAV to overoscillate in height.
From a practical point-of-view, there will not be scenarios
with such a sharp variation, so a setup to produce a smooth
profile was implemented. The proposed solution is shown in
Fig. 9: the sandbox was covered with a plastic canvas which
is transparent to microwaves, but it creates a smooth interface
for the LIDAR altimeter, avoiding the UAV to overoscillate
when flying over it.
The first in-flight test for buried objects detection was
devoted to evaluate the capability of detecting the R= 8 cm
metallic disc shown in Fig. 10 (a) buried at dtarget = 12 cm
deep. For this test, several UAV overflights over the sandbox
Fig. 9. Measurement setup for evaluating detection capabilities of targets
buried in sand. (a) Sandbox filled with sand and canvas supporting frame. (b)
Sandbox covered with the canvas.
covered with the canvas were conducted. Imaging results
for one of these overflights are depicted in Fig. 10 (b)-(d).
Radar image corresponding to point-to-point backpropagation
is shown in Fig. 10 (b), noticing that the air-sandbox interface
and the buried metallic disc cannot be clearly identified.
Next, SAR imaging is applied, first considering εr= 1 and
without removing the average value of the measurements
(Fig. 10 (c)). The improvement with respect to point-to-
point backpropagation can be observed, as both the air-sand
interface and the buried metallic disc can be better detected.
Further improvement can be achieved by removing the average
value of the measurements, Fig. 10 (d). Due to the slower
propagation speed of the radio waves in the sand, the echo of
the metallic disc appears at decho = 20 cm. When the sand
permittivity (εr= 2.5) is considered for underground SAR
imaging (Fig. 10 (e)), the metallic disk is imaged at the correct
depth (12 cm).
Underground SAR imaging improvement over point-to-
point backpropagation results is more noticeable in this ex-
ample (Fig. 10) than in Section IV-A (Fig. 7). The reasons
are: i) in Section IV-A, the radar was moved manually in
the horizontal plane, keeping constant the height. Thus, po-
sitioning and geo-referring uncertainty are smaller than UAV-
based measurements. ii) A sandbox is used for in-flight tests,
that is, a finite domain. The fact of using a finite domain
introduces reflections and echoes that eventually degrade the
quality of the point-to-point backpropagation results in the
case of complex geometry scenarios.
SAR imaging also provides a substantial improvement over
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M. GARC´
IA FERN ´
ANDEZ et al.: SAR IMAGING SYSTEM FOR LANDMINE DETECTION USING A GPR ON BOARD A UAV 9
Fig. 10. Metallic disk buried 12 cm deep in the sandbox. (a) Picture of
the target. (b) Imaging results applying point-to-point backpropagation. (c)-
(e) Imaging results applying underground SAR: (c) considering εr= 1 and
withouth removing the average of the measurements, (d) considering εr= 1
and removing the average of the measurements, (e) considering εr= 2.5 and
removing the average of the measurements. Imaging results (b)-(e) normalized
with respect to the maximum of underground SAR images.
metal detector-based techniques [27], as non-metallic objects
can be detected as well. To prove this feature, a plastic (foam)
disk having the same radius as the metallic one, Fig. 11
(a), has been buried 10 cm deep. SAR image from coherent
combination of the geo-referred GPR measurements collected
during an overflight, considering εr= 1 for underground SAR
imaging, are depicted in Fig. 11 (b). In this case, not only
the air-sandbox interface and the plastic disk are imaged, but
also the reflection created by the sandbox-ground interface is
visible. Introducing sandbox thickness dtarget = 43 cm and the
location of the echo decho = 65 cm in Eq. 6, sand permittivity
is estimated as εr= 2.6, similar to the value estimated at the
laboratory (εr= 2.5).
A synthetic aperture of Lap = 70 cm was considered in
Fig. 11 (b). Flight height above the sandbox was around R=
50 cm, yielding ∆l= 2.6cm cross-range resolution (Eq. 5).
The impact of considering a larger synthetic aperture centered
over the sandbox is shown in Fig. 11 (c), for Lap = 230
cm. Although for this case theoretical cross-range resolution
is ∆l= 0.8cm, in practice, cumulative geo-referring errors
distort the SAR image, introducing some ripple and worsening
cross-range resolution which is within the range of ∆l= 2 −
2.5cm. Note that PVC bars of the canvas frame are visible
in the larger SAR imaging domain shown in Fig. 11 (c) . The
air-ground interface is also noticeable, as well as the sandbox-
ground interface, delayed with respect to the true position as
εr= 1 is considered in this case for SAR imaging.
In order to verify repeatability and reproducibility, SAR
imaging result corresponding to measurements taken in an-
other overflight over the sandbox is shown in Fig. 11 (d).
The main features observed in Fig. 11 (b) are present, thus
confirming that even manual flight operation mode is capable
of providing highly-accurate SAR images along the vertical
plane containing the flight path.
When the estimated permittivity of the sand (εr= 2.5) is
introduced in the underground SAR imaging algorithm, Fig.
11 (e), the plastic object and the sandbox-ground interface are
imaged at the correct depth (10 cm and 43 cm respectively).
Last result presented in this contribution is devoted to prove
the capability of the airborne-based GPR to detect two buried
objects. For this experiment, a cylindrical metallic bar (of 2.5
cm radius) was buried 12 cm deep in one side of the sandbox
(perpendicular to the UAV flight path), and a plastic box (with
8.5 cm x 6.5 cm cross-section) was buried 9 cm deep in the
other side of the sandbox. These two targets were 20 cm
away in the horizontal plane. Imaging results are depicted in
Fig. 12. In this case, point-to-point backpropagation results,
Fig. 12 (a), allows identifying the air-sandbox interface and
the two buried objects, although the echos appear far from
the correct position. The sandbox-ground interface is barely
noticed. Resolution is improved when SAR imaging is applied,
Fig. 12 (b)-(c), where the two targets and the sandbox-ground
interface are clearly distinguishable. SAR imaging results
considering εr= 1 and εr= 2.5 are depicted in Fig. 12 (b)
and Fig. 12 (c) respectively.
V. CON CLUSIONS
A UAV-based underground SAR imaging system for the de-
tection of buried objects has been presented. It aims primarily
at detecting explosives such as antipersonnel landmines, but it
can also be used for any other application where detection and
identification of hidden objects is necessary. Results presented
in this contribution have proved: i) that the radar range and
cross-range resolution are ∆r= 7.5cm and ∆l= 2−2.5cm,
respectively, ii) the capability of detecting buried non-metallic
objects, and iii) the repeatability and reproducibility of the
measurements for SAR imaging. A 3 min video summarizing
the features of the system (operating principle and description
of the architecture) and a brief application example, can be
watched at https://youtu.be/gsKptOPVARI.
The prototype and developed algorithms could be of interest
in sectors where the detection of buried objects is essential, as
the aforementioned detection of landmines, pipeline inspec-
tion, or archaeology work. The system can also be used in
the detection of infrastructure defects, walls, roofs and road
2169-3536 (c) 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
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10 IEEE ACCESS, VOL. XX, XXXXX 2018
Fig. 11. Plastic disk buried 10 cm deep in the sandbox. (a) Picture of the
target. Imaging results applying underground SAR, removing the average of
the measurements. First overflight results: (b) considering εr= 1 and Lap =
70 cm, (c) considering εr= 1 and Lap = 230 cm. Second overflight results,
Lap = 70 cm: (d) considering εr= 1, (e) considering εr= 2.5.
inspection. The added value, when compared with similar
systems for non-destructive testing, comes from the fact that
the GPR is mounted on a UAV which prevents physical contact
with the ground during scanning. With respect to similar
airborne GPR prototypes, this system is capable of creating
SAR images with a few cm resolution, enabling detection of
Fig. 12. SAR imaging of two objects (a plastic box and a metallic bar)
buried in the sanbox. (a) Imaging results applying point-to-point backprop-
agation. (b)-(c) Imaging results appyling SAR, removing the average of the
measurements: (b) considering εr= 1 (the profile of the objects has been
plotted shifted proportionally to the delay observed in the SAR image), (c)
considering εr= 2.5. Imaging results normalized with respect to the maximum
of underground SAR images.
small metallic and dielectric objects buried in the ground. The
system has been licensed under the patent [49].
ACK NOW LE DG ME NT
The authors would like to thank Mr. Salvador Ballesteros
Duque for his help concerning drone flight tests, as well as
Mr. Guillermo ´
Alvarez Narciandi, Mr. Marcos Gonz´
alez D´
ıaz,
Prof. Samuel Ver-Hoeye, and Mrs. Janet Pagnozzi for their
help with the setup of the sandbox in the airfield.
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Maria Garcia Mar´
ıa Garc´
ıa-Fernndez (S’15) was
born in Luarca, Spain, in 1992. She received
the M.Sc. degree in telecommunication engineering
from the University of Oviedo, Gijn, Spain, in 2016,
where she is currently pursuing the Ph.D. degree.
She was a Visiting Student at Stanford University,
Palo Alto, CA, USA, in 2013 and 2014, and a
Visiting Scholar at the Gordon Center for Subsurface
Sensing and Imaging Systems (CenSSIS), Northeast-
ern University, Boston, MA, USA in 2018. Since
2013, she has been involved in several research
projects within the Signal Theory and Communications research group, TSC-
UNIOVI, at University of Oviedo. Her current research interests include
inverse scattering, remote sensing, radar systems, imaging techniques, and
antenna measurement and diagnostics.
2169-3536 (c) 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2018.2863572, IEEE Access
12 IEEE ACCESS, VOL. XX, XXXXX 2018
Yuri Alvarez Yuri Alvarez (S’06 - M’09 - SM’15)
was born in Langreo, Spain, in 1983. He received
the M.S. and Ph.D. degrees in telecommunication
engineering from the University of Oviedo, Gij´
on,
Spain, in 2006 and 2009, respectively. He was a
Visiting Scholar at the Department of Electrical
Engineering and Computer Science, Syracuse Uni-
versity, Syracuse, NY, USA, in 2006 and 2008;
a Visiting Postdoc at the Gordon Center for Sub-
surface Sensing and Imaging Systems (CenSSIS)
ALERT (Awareness and Localization of Explosive
Related Threats) Center of Excellence, Northeastern University, Boston, MA,
USA, from 2011 to 2014; and a Visiting Postdoc at ELEDIA Research Center,
Trento, Italy, in 2015. He is currently a Professor with the Signal Theory and
Communications, University of Oviedo, Gijn, Spain. His research interests
include antenna diagnostics, antenna measurement techniques, RF techniques
for indoor location, inverse scattering and imaging techniques, and phaseless
methods for antenna diagnostics and imaging. Dr. Alvarez was the recipient
of the 2011 Regional and National Awards to the Best Ph.D. Thesis on
Telecommunication Engineering (category: security and defense).
Ana Arboleya Ana Arboleya-Arboleya received
the M.Sc. degree in telecommunication engineering
in 2009 and the Ph.D. degree in telecommunica-
tion engineering in 2016 from the University of
Oviedo, Spain. From 2008 to 2016, she worked
as a Research Assistant within the Signal Theory
and Communications research group, TSC-UNIOVI,
at the Department of Electrical Engineering in the
University of Oviedo. In 2016, she held a postdoc
position in the EpOC Polytech’ Lab (Electronics for
Connected Objects) of the University of Nice-Sophia
Antipolis, France. She is currently an Associate Professor at the Universidad
Rey Juan Carlos (Madrid, Spain). She was a Visiting Scholar in 2014 and
2015 in the Department of Radio Science and Engineering and MilliLab,
in Aalto University, Finland. Her major research interests comprise antenna
diagnostics and measurement systems and techniques, and high frequency
imaging techniques and applications. Dr. Arboleya was the recipient of the
2017 National Awards of the Official College of Telecommunication Engineers
of Spain to the Best Ph.D. Thesis on Telecommunication Engineering in the
category of security and defense.
Borja Gonzalez Borja Gonz´
alez-Vald´
es (S’09 -
M’12) received the B.S and Ph.D. degrees in electri-
cal engineering from the University of Vigo, Vigo,
Spain, in 2006 and 2010, respectively. From 2006 to
2010, he was with the Antenna and Optical Commu-
nications Group, University of Vigo. From 2008 to
2009, he was a Visiting Researcher with the Gordon
Center for Subsurface Sensing and Imaging Sys-
tems, Northeastern University, Boston, MA, USA.
In 2011, he joined the Awareness and Localization
of Explosives-Related Threats Center of Excellence,
Northeastern University. Since 2015, he has been a Postdoctoral Researcher
affiliated with the AtlantTIC Research Center, University of Vigo. His research
interests include antenna design, inverse scattering, radar, advanced imaging
techniques, and THz technology.
Yolanda Rodriguez Yolanda Rodr´
ıguez-Vaqueiro
(S’12) received the B.S. and M.S. degrees in electri-
cal engineering from the University of Vigo, Vigo,
Spain, in 2009, and the Ph.D. degree in electrical
engineering from Northeastern University, Boston,
MA, USA, in 2015 (after defending her thesis: Com-
pressive Sensing for Electromagnetic Imaging Using
a Nesterov-Based Algorithm). She is a Postdoctoral
Researcher affiliated with the AtlantTIC Research
Center, University of Vigo. In 2011, she obtained a
Research Assistant grant from the ALERT (Aware-
ness and Localization of Explosive Related Threats) Center of Excellence,
Northeastern University. She was also granted as a Junior Researcher with the
University of Vigo. Dr. Rodriguez-Vaqueiro was the recipient of the Research-
Impact Award by the Department of Electrical and Computer Engineering,
Northeastern University (for her work during the Ph.D. studies), the Best Paper
Award in the 2012 IEEE Homeland Security Conference, Honorable Mention
in the Student Paper Competition in the 2013 IEEE APS/URSI Conference,
the Best Paper Award in the 2014 European Conference on Antennas and
Propagation, the Burke/Yannas Award to the most original research study in
the field of bioengineering in the 2015 American Burn Association (ABA)
Meeting, and the Research-Impact Award by the Department of Electrical
and Computer Engineering, Northeastern University, in May 2015.
Fernnado Las-Heras ernando Las-Heras (M’86 -
SM’08) received the M.S. in 1987 and the Ph.D.
in 1990, both in Telecommunication Engineering,
from the Technical University of Madrid (UPM).
He was a National Graduate Research Fellow (1988-
1990) and he held a position of Associate Professor
at the Department of Signal, Systems and Radio-
communications of the UPM (1991-2000). From
December 2003 he holds a Full-Professor position
at the University of Oviedo where he was the
Vice-dean for Telecommunication Engineering at the
Technical School of Engineering at Gijn (2004-2008). As of 2001 he heads
the research group Signal Theory and Communications TSC-UNIOVI at the
Dept. of Electrical Engineering of the University of Oviedo. He was a Visiting
Lecturer at the National University of Engineering in Peru in 1996, a Visiting
Researcher at Syracuse University, New York, in 2000, and a short term
Visiting Lecturer at ESIGELEC in France from 2005 to 2011. He held the
Telefnica Chair on RF Technologies, ICTs applied to Environment and ICTs
and Smartcities at the University of Oviedo (2005-2015). Member of the board
of directors of the IEEE Spain Section (2012-2015), member of the board
IEEE Microwaves and Antennas Propagation Chapter (AP03/MTT17) (2016-
2017), member of the Science, Technology and Innovation Council of Asturias
(2010), and president of the professional association of Telecommunication
Engineers at Asturias. He has led and participated in a great number of
research projects and has authored over 190 technical journal papers, mainly
in the areas of antennas, propagation, metamaterials and inverse problems with
application to antenna measurement (NF-FF, diagnostics and holography),
electromagnetic imaging (security and NDT) and localization, developing
computational electromagnetics algorithms and technology on microwaves,
millimeter wave and THz frequency bands.
2169-3536 (c) 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2018.2863572, IEEE Access
M. GARC´
IA FERN ´
ANDEZ et al.: SAR IMAGING SYSTEM FOR LANDMINE DETECTION USING A GPR ON BOARD A UAV 13
Antonio Pino Antonio Pino-Garc´
ıa (S’87 - M’89
- SM’05) was born in Valdemoro, Madrid, Spain,
in 1962. He received the M.S. degree in 1985 and
the Ph.D. degree in 1989, both in telecommunica-
tions engineering from the Polytechnic University
of Madrid (UPM), Madrid, Spain. From 1985 to
1989, he was with the Radiation Group of UPM
as a Research Assistant. He joined the Department
of Technologies of Communications at University
of Vigo (Spain) as an Associate Professor in 1989,
becoming a Full Professor in 1994. During 1993,
he was a Visiting Researcher at the Center for Electromagnetics Research,
Northeastern University, Boston. His research interests include shaped re-
flector antennas for communication and radar applications, high frequency
backscattering, computational electromagnetics, and THz technology. In these
topics, he is author of more than 100 technical papers in journals and
conferences and he has been an Advisor of 14 Ph.D. students. From 2003
to 2006, he was the Maximum Academic responsible for doctoral studies,
and from 2006 to 2010, he was Vice-Rector of Academic Organization and
Faculty at the University of Vigo.
