Airborne Multi-Channel Ground Penetrating Radar for Improvised Explosive Devices and Landmine Detection

Abstract

An improved Ground Penetrating Radar (GPR) system on board an Unmanned Aerial Vehicle (UAV) is presented in this contribution. The system has been designed for the detection and imaging of buried targets and, in particular, landmines and Improvised Explosive Devices (IEDs). Resting on the hardware and architecture of a previous aerial platform, in the proposed system the scanning area is increased and the detection capabilities are improved. These improvements are achieved by employing two receiving antennas and new processing techniques that increase the Signal-to-Clutter Ratio of the GPR images. Besides, parameters affecting the GPR image resolution, such as the flight speed and the amount of measurements that can be processed together using Synthetic Aperture Radar (SAR) techniques, are also studied. The developed system exhibits several advantages: safety and faster scanning speeds, together with the capability to detect both metallic and non-metallic targets, as shown in the examples presented in this contribution.

Full text

Received August 7, 2020, accepted September 3, 2020, date of publication September 8, 2020,
date of current version September 22, 2020.
Digital Object Identifier 10.1109/ACCESS.2020.3022624
Airborne Multi-Channel Ground Penetrating
Radar for Improvised Explosive Devices
and Landmine Detection
MARÍA GARCÍA-FERNÁNDEZ , YURI ÁLVAREZ LÓPEZ , (Senior Member, IEEE),
AND FERNANDO LAS-HERAS ANDRÉS , (Senior Member, IEEE)
Área de Teoría de la Señal y Comunicaciones, Departamento de Ingeniería Eléctrica, Universidad de Oviedo, 33203 Gijón, Spain
Corresponding author: Yuri Álvarez López (alvarezyuri@uniovi.es)
This work was supported in part by the Ministerio de Educación—Gobierno de España under Grant FPU15/06341, in part by the
Ministerio de Defensa — Gobierno de España and the University of Oviedo under Grant 2019/SP03390102/00000204 / CN-19-002
(SAFEDRONE), in part by the Xunta de Galicia – Axencia Galega de Innovación (GAIN), (‘‘RadioUAV: drones para aplicaciones más allá
de lo visible’’), under Grant 2018-IN855A 2018/10, in part by the Government of the Principality of Asturias (PCTI) and European Union
(FEDER) under Grant IDI/2018/000191, and in part by the Instituto Universitario de Tecnología Industrial de Asturias (IUTA) under
Grant SV-19-GIJON-1-17 (RadioUAV).
ABSTRACT An improved Ground Penetrating Radar (GPR) system on board an Unmanned Aerial Vehicle
(UAV) is presented in this contribution. The system has been designed for the detection and imaging of buried
targets and, in particular, landmines and Improvised Explosive Devices (IEDs). Resting on the hardware and
architecture of a previous aerial platform, in the proposed system the scanning area is increased and the
detection capabilities are improved. These improvements are achieved by employing two receiving antennas
and new processing techniques that increase the Signal-to-Clutter Ratio of the GPR images. Besides,
parameters affecting the GPR image resolution, such as the flight speed and the amount of measurements that
can be processed together using Synthetic Aperture Radar (SAR) techniques, are also studied. The developed
system exhibits several advantages: safety and faster scanning speeds, together with the capability to detect
both metallic and non-metallic targets, as shown in the examples presented in this contribution.
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. INTRODUCTION
Non-Destructive Testing (NDT) techniques have been of
great interest in a wide scope of applications, from mining and
geology, to civil engineering and civil works, archaeology,
and security and defense, among others. NDT techniques
allow to detect, locate, and, eventually, to obtain an image
of the concealed object, avoiding the interaction with the
object and the surrounding medium [1]. Among the different
NDT systems, Ground Penetrating Radar (GPR) is one of the
most powerful techniques for underground imaging thanks
to its capability of providing images of the soil and the
objects buried in it [1]. Similarly to other electromagnetic
wave-based NDT techniques, GPR is based on detecting the
impedance mismatch at the interface between two media.
The associate editor coordinating the review of this manuscript and
approving it for publication was Francesco Benedetto .
This mismatch causes the reflection of the electromagnetic
wave that hits the interface.
GPR systems can be classified according to the angle of
illumination with respect to the soil/ground as follows:
i) Forward-looking GPR systems (FLGPR). The trans-
mitting antenna illuminates the soil under a given angle of
incidence, trying to minimize the reflection coming back
from the air-soil interface [2], [3]. The angle between the
radar antennas and the ground results in only a little part of
the reflected energy being backscattered towards the radar.
Therefore, FLGPR systems require high dynamic range at
the receiver to achieve enough sensitivity to detect the buried
targets.
ii) Down-looking GPR systems (DLGPR). The incident
wave hits normally the ground interface [2], [4]. The distance
from the radar to the ground is smaller than in FLGPR and
they can provide better resolution. In general, the amount of
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M. García-Fernández et al.: Airborne Multi-Channel Ground Penetrating Radar for IED and LM Detection
power backscattered by the buried targets is greater than in
the case of FLGPR, but the clutter is also greater due to the
reflection of the electromagnetic waves in the ground.
In the field of landmine and Improvised Explosive Devices
(IEDs) detection, GPR systems have become an efficient
solution as they are able to detect both metallic and non-
metallic buried targets. In the last decades, different tech-
niques have been proposed to improve the performance of
GPR systems to detect landmines and IEDs [5]–[8]. Com-
pared to other GPR applications, here the main challenge
is to minimize the risk of detonation of landmines/IEDs,
by keeping a safety distance with the area to be scanned
(typically from 3 to 5 m in the case of terrestrial GPR scan-
ners). Besides, the probability of detection has to be maxi-
mized. Thus, airborne-based GPR systems are a promising
technology aiming to address the aforementioned challenges
in landmine and IEDs detection.
A. UAV-BASED GPR SYSTEMS
Unmanned Aerial Vehicles (UAVs), commonly known as
drones, have experienced a great development over the last
years thanks to improvements in avionics and propulsion
systems, capacity of batteries, autonomous navigation capa-
bilities, and ease of sensor integration. Besides, the reduction
on the cost of these devices have enabled the introduction
of UAVs in several fields such as precision agriculture and
forestry monitoring [9], [10], glaciology [11], ground obser-
vation and mapping [12]–[15] and electromagnetic compat-
ibility and antenna measurements [16], [17]. In connection
with the latter, UAVs can be used as well for network coverage
and data connectivity improvement [18], [19].
These advances in UAV technology have made possible the
development of UAV-based GPR systems for non-destructive
testing and imaging of buried targets. This is of special
interest in the field of landmine and IEDs detection. The
main advantages of UAV-based GPR systems are: i) higher
scanning speed compared to solutions based on terrestrial
autonomous robots [20], [21]; ii) capability to scan difficult-
to-access areas; and iii) safety, as contact with soil is avoided,
thus minimizing the risk of accidental detonation.
First attempts to detect IEDs and landmines using airborne-
based systems employed metal detectors [22]. However,
metal detectors cannot detect explosives with low or no metal
content, which limits the range of application of these sys-
tems. Thus, next step was the integration of a GPR on board
a UAV [23].
The different scanning modes for UAV-based GPR systems
are illustrated in Fig. 1 of [24], mainly FLGPR (or side-
looking GPR) and DLGPR. The former has been widely used
for landmine and IED detection, and it has been recently
tested on board UAVs, as shown in [25] and in [24]. If a
side-looking GPR system follows a circular path, Circular-
based Synthetic Aperture Radar processing (CSAR) can be
applied for imaging the ground and buried targets [26]. In the
case of UAV-based DLGPR, [27], [28] propose to use a low-
cost lightweight Stepped Frequency Continuous Wave radar
FIGURE 1. Picture of the implemented prototype, pointing out the main
hardware components.
working in the 550 - 2700 MHz frequency band. Another
prototype of interest, based on a Software Defined Radio
(SDR)-GPR, is described in [29]. [30] makes use of a com-
mercial GPR working at sub-GHz frequencies, thus providing
more penetration depth but at the expense of losing spatial
resolution.
Most UAV-based GPR systems consist of a compact
GPR unit that stores geo-referred measurements for post-
processing. Geo-referring accuracy affects the horizon-
tal (cross-range) resolution of the GPR system. Besides,
it should be within the order of half a wavelength to apply
Synthetic Aperture Radar (SAR) processing, that is, to allow
the coherent combination of the radar measurements taken at
each position. The decrease in the cost of Global Navigation
Satellite Systems (GNSS) - Real Time Kinematic (RTK)
modules has led to their integration in the UAV hardware.
RTK modules are able to provide centimeter-level positioning
accuracy, thus enabling GPR-SAR processing. First results of
UAV-based GPR-SAR are shown in [31], and they have been
later extended to 3D GPR-SAR imaging in [32] and in [26] for
the case of CSAR. Concerning the working frequency band,
it ranges from 300 MHz to 5 GHz, as it provides a good trade-
off between spatial resolution (a bandwidth of 4 GHz gives a
range resolution of 75 mm) and penetration depth (taking into
account that most IEDs and landmines are buried less than
50 cm deep).
In the field of UAV-based GPR systems, the extension from
2D to 3D scans is still limited by the flight autonomy of the
UAVs, as in most of the aforementioned contributions the
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TABLE 1. Comparison of UAV-based GPR systems.
selected UAV provides an average flight-time of 15 minutes.
In the few contributions presenting 3D GPR SAR results [26],
[32], scanned areas per flight range from 5 m2to 40 m2. Thus,
the scanning of larger areas would require other platforms
such as wire-powered UAVs.
In order to provide a comparison of the current state-
of-the-art in the field of UAV-based GPR systems, Table 1
summarizes the main features of the systems mentioned in
this Section I-A.
B. AIM AND SCOPE OF THIS CONTRIBUTION
Results presented in [32] and [26] prove the feasibility
to detect buried targets using UAV-based GPR systems
(DLGPR and side-looking GPR architectures, respectively),
introducing SAR processing to achieve cm-level resolution.
UAV-based GPR systems without SAR processing are limited
in terms of cross-range resolution, being, in general, unable
to detect targets whose size is smaller than 15-20 cm. The
frequency bands of these systems provide a good trade-off
between image resolution and penetration depth.
In this contribution, the system presented in [32] is
improved aiming to achieve better detection capabilities and
to increase the scanning area. First, a 3-element antenna
array is mounted on board the UAV. One antenna is used for
transmission and two for reception, as the radar module has
two receiving channels. The employment of a dual-channel
receiver entails a significant contribution in the field of
UAV-based GPR systems as, up to the authors’ knowledge,
existing UAV-based GPR systems use a single transmitter and
receiver. This allows performing the coherent combination
of the SAR images associated to each of the two receiving
channels of the radar module. Besides, radar processing is
improved by applying a clutter filtering technique based on
Singular Value Decomposition (SVD) and a processing gain
technique to increase the dynamic range. Finally, masked
SAR processing is introduced to further mitigate the clutter
when larger areas are scanned. All these combined improve-
ments result in a Signal-to-Clutter Ratio improvement, which
allows a better detection of buried targets.
II. UAV-BASED UNDERGROUND SAR IMAGING SYSTEM
IMPLEMENTATION
A. OVERVIEW OF THE AIRBORNE-BASED GPR SYSTEM
The UAV-based GPR prototype is based on the architecture
described in [32]. The main systems and subsystems of the
prototype are:
•Flight control subsystem. It consists of a micro-
computer (Raspberry Pi), with an add-on board [33] to
act as UAV flight controller. This add-on board includes
positioning sensors usually mounted on UAVs: an Iner-
tial Measurement Unit (IMU), a barometer and a GNSS
receiver.
•Accurate positioning subsystem to provide cm-level
accuracy. It comprises a LIDAR (Light Detection And
Ranging) altimeter (or rangefinder) and a dual-band
RTK-GNSS system [34]. The latter is composed by an
RTK antenna and an RTK receiver. RTK corrections are
received from a GNSS base station and sent to the RTK
receiver. A dual-band RTK was chosen as it provides bet-
ter accuracy and availability (that is, percentage of time
that corrected coordinates are provided), more robust-
ness (e.g. when working in limited sky view areas), and
faster deployment time compared to single-band RTKs.
Concerning RTK accuracy, it is around 0.5 cm in the
horizontal plane and 1 cm in the vertical direction [32].
With respect to LIDAR, estimated accuracy is around
1.8 cm [31].
•Radar subsystem. A lightweight, compact Ultra Wide
Band (UWB) radar, whose frequency band ranges from
100 MHz to 6 GHz [35], was selected. This radar has
one transmitting port, and two receiving ports. Thus,
taking advantage of the number of ports, the radar is
connected to a 3-element antenna array. Each antenna
is a UWB Vivaldi antenna working in the 600 MHz to
6 GHz frequency band [36].
•A ground station, consisting of a conventional laptop,
which receives the radar measurements and position-
ing and geo-referring information. Geo-referred mea-
surements are processed using a GPR-SAR imaging
algorithm to create radar images of the underground
and objects buried in it. The processing algorithm is
described in Section II-B.
•Communication subsystem, composed by a data link
and a radio-control link. The data link, that is, the com-
munication between the UAV and the laptop acting as
ground station, is based on an in-situ deployed Wireless
Local Area Network (WLAN). This WLAN can be set
to work at 2.4 GHz or 5.8 GHz. Thanks to the DLGPR
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configuration and the use of directive antennas, inter-
ferences between the radar and the WLAN are negli-
gible, apart from the fact that both WLAN and UWB
radar use spread spectrum signals. The in-situ deployed
WLAN is connected to a mobile phone to enable Internet
access, so RTK corrections from a GNSS base station
can be received. Concerning the radio-control of the
UAV, 433 MHz transmitting and receiving modules have
been selected.
The UAV model allows mounting a payload up to 5 kg
weight [37], providing capacity for further improvements of
the prototype with additional sensors or devices (e.g. integra-
tion of more antennas, as in this contribution). The overall
weight of the payload composed by the described systems and
subsystems is about 3 kg (excluding batteries), resulting in
about 15 minutes flight (similarly to the flight time achieved
in [32]).
For the experimental validation shown in this contribution,
only the frequency band from fmin =600 MHz to fmax =
3 GHz was selected for radar data processing, since the soil
losses in the measured scenario produce too much attenuation
at higher frequencies.
Finally, a picture of the UAV taken after conducting a flight
is shown in Fig. 1.
B. DATA PROCESSING
The two main data sources of the UAV-based GPR system
come from the positioning and geo-referring information
subsystem and from the radar subsystem. The former are
required to properly geo-refer radar measurements so that
GPR-SAR processing can be applied. It is worth noting that
the geo-referred radar measurements are sent in real-time to
the ground-control station. A flowchart of the data processing
is shown in Fig. 2.
FIGURE 2. Data processing flowchart.
First, positioning information is processed, providing the
x,y,zcoordinates defined according to a local coordinate
system (as explained in detail in [32]). Positioning informa-
tion is also used to select the radar measurements that will
be process (indobs), mainly in order to avoid oversampling in
some areas and to discard non-valuable data [38].
Concerning radar data processing, the basic preprocess-
ing comprises: first, retrieving the impulse response; then,
performing time-gating to select the range of interest; and,
finally, applying average subtraction and height correction to
mitigate the clutter.
In this contribution, the preprocessing stage is improved
applying Singular Value Decomposition (SVD) filtering and
processing gain techniques (as explained in Section III). After
the preprocessing, the Fourier Transform is applied to trans-
form the preprocessed radar data to the frequency domain.
Next, given the coordinates of the measurements (x,y,z)
and the investigation (or imaging) domain (x0,y0,z0), SAR
processing is applied to recover the reflectivity within the
investigation domain for each channel n(n=1, 2) of the
radar module (ρCHn(x0,y0,z0)).
Finally, both channels are coherently combined to obtain a
single reflectivity set (ρ(x0,y0,z0)).
III. IMPROVEMENTS IN DATA PROCESSING
A. SVD FILTERING AND PROCESSING GAIN
In order to further mitigate the clutter whilst improving the
dynamic range, SVD filtering and processing gain techniques
are applied before the SAR processing.
Regarding SVD filtering, it consists of computing the
SVD of the radar measurements for each channel and then
discarding the data corresponding to the most significant
singular values. The radar data matrix contains NMradar
measurements of NSsamples each. Then, applying SVD,
the NM×NSradar data matrix is decomposed into NI=
min(NM,NS) eigenimages. Each eigenimage is associated to
its corresponding singular value σi,i=1,...,NI(where
σ1> σ2> . . . > σNI, i.e. the singular values are in descend-
ing order). The first eigenimages contain highly correlated
information, which corresponds to the strong reflection from
the air-soil interface and should be removed. The difficulty
when applying SVD filtering is to choose how many eigen-
images can be removed without losing information from the
buried targets. In this contribution, a conservative procedure
has been adopted and only the first eigenimage (associated
to σ1) is removed. This helps to mitigate the clutter from the
air-soil interface, while ensuring that the lost of information
from the buried objects is minimized.
Then, a processing gain technique is applied to enhance the
signal coming from the reflection at the buried objects (thus
compensating the attenuation in the soil). The idea behind
this technique is to introduce a gain function g(r) (where r
denotes the range) so that the amplitude of the reflectivity is
increased within a certain depth interval. In this contribution,
a power gain function g(r) has been adopted. This function is
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M. García-Fernández et al.: Airborne Multi-Channel Ground Penetrating Radar for IED and LM Detection
defined as follows:
g(r)=




rα
0,r<r0
rα,r0≤r≤r1
rα
1,r>r1
(1)
where r0is the initial depth at which the signal is amplified,
r1is the final depth, and αis an coefficient that controls the
value of the gain function. These parameters have been set to
r0= −0.2 m, r1= −1.2 m and α=4. This means that
the signal amplitude in the range [r0,r1]=[−0.2,−1.2]
m is amplified by a factor of r4.r0is set to −0.2 m to
avoid amplifying air-soil reflections. As observed in GPR-
SAR images (vertical cuts), the thickness of the air-soil reflec-
tion is around 10-12 cm (considering a −20 dB reflectivity
threshold). Regarding r1, the threshold of −1.2 m is chosen
based on the maximum achievable penetration depth (actually
in the scenario used in this contribution a lower r1threshold
could have be chosen due to the high moisture level of the
soil). It is worth noting that the position r=0 m corresponds
to the location of the air-soil interface.
An example of the impact of SVD filtering and processing
gain is shown in Fig. 3. A set of 1000 radar measurements
collected during a flight has been considered. Fig. 3(a)
shows the measurements after the radar data preprocessing
and before applying SVD filtering and processing gain (see
data processing flowchart of Fig. 2). Results after applying
SVD filtering (where the first eigenimage, corresponding to
the first singular value, σ1, has been removed) are depicted
in Fig. 3(b). It can be noticed that the clutter due to the air-
soil interface is reduced. Then, gain processing is applied,
according to the function defined in (1). From the results
shown in Fig. 3(c), a reduction in the clutter level (and thus,
an increase in the dynamic range) can be observed.
B. SAR PROCESSING ENHANCEMENTS
SAR processing allows obtaining high-resolution radar
images of the subsoil, thanks to the coherent combination
of the measurements taken in the acquisition domain x,y,z.
Range resolution (1Rz) is given by the radar subsystem
bandwidth (BW) according to 1Rz=vp/(2 BW ) (where vpis
the propagation speed of the electromagnetic wave), whereas
cross-range resolution (1Rx,y) depends on the vertical dis-
tance between the acquisition domain and the investigation
domain (h) and the size of the aperture to be considered within
the acquisition domain (Lx,y). Thus, 1Rx,y=λh/Lx,y, where
λis the wavelength at the center frequency of the working
frequency band.
One of the challenges faced with the improvements intro-
duced in the new prototype is related to the larger size of
the acquisition and investigation domains, if compared to
previous works [31], [32]. For this reason, masked SAR
processing has been introduced. It relies on computing the
reflectivity on each voxel of the investigation domain con-
sidering only the acquisition domain points in the vicinity
of the voxel, as depicted in Fig. 4. This approach helps to
FIGURE 3. Example of radar measurements, Escatt taken along a flight
path (in these plots, the zero value of the vertical axis is set at the UAV
position, i.e. 2.35 m above ground). Before SVD filtering and processing
gain (a). After SVD filtering (b). After SVD filtering and processing gain (c).
mitigate the clutter, since the most relevant information for
computing the reflectivity at a certain position is given by the
measurements taken near to it. The size of the mask defining
the measurements to be considered for each voxel depends
on the coherence length along xand yaxes, that is, the length
along with radar measurements can be coherently combined.
As indicated in the data processing flowchart of Fig. 2,
the output of the SAR processing stage is a set of two SAR
images, one per each receiving channel of the radar. Clutter
appearing in each SAR image can be further mitigate by
coherently adding both SAR images, as the phase of the
clutter on each individual channel is expected to be con-
siderably uncorrelated. Besides, the phase corresponding to
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FIGURE 4. Scheme showing the acquisition domain (2D) and the
investigation domain (3D), together with the concept of applying masks
in the investigation domain for SAR processing. Dimensions correspond
to the examples shown in Section IV.
FIGURE 5. Picture of the scenario and buried targets: an anti-personnel
plastic landmine, and a metallic disk. The scheme in the left side of the
picture illustrates the flight path.
reflections on the targets will exhibit a high degree of corre-
lation, so these reflections will be reinforced in the combined
SAR image.
The antenna array placed on board the UAV (see Fig. 1)
consists of three UWB Vivaldi antennas spaced 9.8 cm. The
outer antennas are connected to the receiving channels of the
FIGURE 6. Analysis of the UAV flight path as a function of the along-track
flight speed. Flight speed of 50 cm/s: flight path and measurement
positions (a), probability density function of the UAV speed on the XY
plane (vxy ) (b). Flight speed of 75 cm/s: flight path and measurement
positions (c), probability density function of the UAV speed on the XY
plane (vxy ) (d).
radar, and the central antenna is connected to the transmitter.
Thus, the spacing between the receiving antennas is 19.6 cm.
This distance is taken into account to correct the phaseshift
due to the different position of the receiving antennas. In addi-
tion to this, a calibration stage using a reference metallic disk
placed on the soil has been conducted. This calibration stage
is needed to estimate the value of a phaseshift (1φ) that has
to be introduced between channels 1 and 2 to ensure that the
reflection on the metallic disk observed in each individual
SAR image is in-phase, so that these reflections are combined
constructively.
IV. RESULTS
A. DESCRIPTION OF THE SCENARIO
The improved UAV-based GPR system for IEDs and land-
mine detection has been validated at the airfield for UAVs of
the Technical School of Engineering of Gijón, located at coor-
dinates (43.522, −5.624). Two people are required to conduct
the measurements: one is the responsible of the ground station
(laptop), being in charge of configuring the different subsys-
tems described in Section II-A. This person also supervises
that the system is working as expected. The other person
manages the radio-control unit of the UAV for manual flight
mode (required for takeoff and landing). Concerning the time
required for the preparation of the prototype, thanks to the
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FIGURE 7. SAR image. Horizontal cut z= −70 cm centered at the location of the 18 cm diameter metallic disk buried 25 cm deep.
Normalized reflectivity, in dB. Coherent combination of Rx channels 1 and 2. Without applying SVD filtering nor processing gain (a),
applying SVD filtering (b), applying processing gain (c), applying SVD filtering and processing gain (d).
use of a dual-band GNSS-RTK receiver, maximum position-
ing accuracy is achieved within seconds after powering the
UAV up. In other systems equipped with single-band GNSS-
RTK modules [31], reaching the maximum accuracy can take
several minutes.
A picture of the scenario is shown in Fig. 5together with an
scheme of the flight path followed by the UAV (Fig. 5, left).
The soil of the area-under-test is a loamy soil with a high
degree of moisture, since a hygrometer measures a 40-60%
of water vapor when placed inside this soil. The relative
permittivity of the soil is within εr=5 and εr=8, which
is in agreement with the expected relative permittivity values
for loamy soils. As shown in Fig. 5, two targets have been
buried: the first one is a metallic disk, buried at 25 cm depth,
and the second is an anti-personnel plastic landmine, buried
at 13 cm depth. The coordinates of these targets in the local
coordinate system are indicated in Fig. 5.
Given the working frequency band (from fmin =600 MHz
to fmax =3 GHz), theoretical range resolution is
1Rz=6.3 cm in free-space. Concerning cross-range res-
olution, a mask of size Mx=1 m ×My=2 m is
considered when applying SAR processing. This means that
the reflectivity on each voxel is computed considering the
measurements contained in a rectangle of size Mx×My
centered on such voxel. The choice of this mask is based on
the estimation of the along-track and across-track coherence
lengths. The former has been selected based on the analy-
sis shown in [31] for different coherence lengths and their
impact in the SAR images. Across-track coherence length is
shorter than along-track coherence length (1 m instead of 2
m) because geo-referred uncertainties are more correlated
within the same sweep (along-track acquisition). Therefore,
cross-range resolution (at the air-soil interface, h=2.3 m) is
1Rx=λh/Mx=38.3 cm, 1Ry=λh/My=19.2 cm.
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FIGURE 8. SAR image. Vertical along-track cut x= −11 cm centered at the location of the 18 cm diameter metallic disk buried
25 cm deep. Normalized reflectivity, in dB. Coherent combination of Rx channels 1 and 2. Without applying SVD filtering nor
processing gain (a), applying SVD filtering (b), applying processing gain (c), applying SVD filtering and processing gain (d).
As explained in [32], the investigation domain is shrunk in
the XY plane with respect to the acquisition domain to avoid
edge effects in the GPR-SAR images. Thus, an investigation
domain of size Lx0=1.6 m and Ly0=8 m has been
considered (as shown in Fig. 4). This is 0.8 times the size
of the acquisition domain (along both xand yaxes).
B. UAV SCAN SPEED ANALYSIS
The flight path is defined as follows: first, the area to be
scanned is defined using a Geographic Information System
(GIS) tool and then, the separation between waypoints in the x
and yaxes is selected to obtain the waypoints. Once the way-
points are set, they are loaded into the UAV flight controller.
After the takeoff procedure, the UAV flies autonomously
following the pre-defined path based on the waypoints. The
heading of the UAV is kept fixed to the same value (in partic-
ular, fixed to the desired course over ground) during the entire
flight, so that the UAV does not rotate 180◦after reaching the
end of a sweep. Instead, it flies forward and backward alter-
natively. Thanks to this, course and attitude are kept stable
during the entire flight, resulting in a better GPR-SAR image.
Besides, 180◦turns would result in sharp flight oscillations
that could impact the equipment on board the UAV, apart
from limiting the flight time. It is worth noting that radar
measurements are continuously gathered during the whole
flight. A video illustrating how scanning is performed can be
watched at: https://youtu.be/HDUwgka8Dns.
As shown in Fig. 5, the area scanned with the prototype has
a size of Ly=10 m (along-track direction, yaxis) ×Lx=
2 m (across-track direction, xaxis), being 6 cm the spacing
between two consecutive along-track sweeps. This results
in 34 along-track sweeps (17 forward and 17 backward),
so that the overall flight path length is 342 m. As the time
required to complete the flight path defined with waypoints is
around 12 minutes, the average flight speed is around 47 cm/s.
The flight speed on each along-track scan is faster since it
has been set to 75 cm/s. The reason why the average speed
is smaller is because after finishing each along-track sweep
the UAV has to slow down, perform a lateral displacement
of 6 cm, and then increase the speed until reaching again the
along-track speed of 75 cm/s.
UAV flight speed also impacts the smoothness of the UAV
flight path. To analyze this parameter, two scans of the area-
under-test have been conducted at different flight speeds.
Results are shown in Fig. 6(a),(b) for an along-track flight
speed of 50 cm/s, and in Fig. 6(c),(d) for an along-track
flight speed of 75 cm/s. It can be noticed that flying at slower
speed results in less straight along-track trajectories when
comparing Fig. 6(a) and Fig. 6(c). It is consistent with the
fact that the momentum of the UAV (mass ×speed) is smaller
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M. García-Fernández et al.: Airborne Multi-Channel Ground Penetrating Radar for IED and LM Detection
FIGURE 9. SAR image. Vertical along-track cut x= −11 cm centered at the location of the 18 cm diameter metallic disk buried 25 cm
deep. Normalized reflectivity, in dB. Coherent combination of Rx channels 1 and 2. Processing gain is applied. No SVD filtering (a),
SVD filtering: first eigenimage removed (b), first and second eigenimages removed (c), four first eigenimages removed (d).
at slower speeds, so lateral forces (e.g. wind, the proper
feedback of the propellers to follow the waypoints) have more
impact in the UAV flight path.
Fig. 6(b) and Fig. 6(d) show the histogram of the
UAV flight speed when the area-under-test is scanned.
At higher speed (Fig. 6(d)) two peaks can be identified,
the highest corresponding to the UAV along-track speed
(75 cm/s), and the smallest corresponding to lateral dis-
placements from one along-track scan to the next one.
The speed profile/histogram can be used to filter out posi-
tioning and radar measurements (as introduced in [38]),
selecting only the data corresponding to UAV flight speeds
above a certain threshold, which corresponds to along-track
acquisition (e.g. speed greater than 40 cm/s in the case of
Fig. 6(d)). In the case of Fig. 6(b), it is more difficult to filter
the positions corresponding to along-track acquisitions using
the speed profile information, as there is not a distinctive
separation between low-speed operations and the along-track
flight speed of 50 cm/s. Finally, it must be remarked that
higher flight speeds result in larger areas to be scanned. Thus,
results presented hereinafters correspond to an along-track
flight speed operation of 75 cm/s (except for one case shown
to illustrate the impact of the flight path smoothness in the
SAR images). Although faster speeds could be achieved with
the implemented prototype, it has been decided to keep the
operation speed below 100 cm/s for safety reasons.
Concerning the size of the area that can be scanned in a
single flight using the improved prototype, it must be pointed
out that it is within the size of the search lanes defined in
Section 4.1 of [39] suggested for demining procedures.
C. GPR-SAR RESULTS
The following subsection shows GPR-SAR results for the
radar measurements collected during the flight whose path
is shown in Fig. 6(c). Results for the metallic disk will be
analyzed first, considering the improvements explained in
Section III. It must be mentioned that free-space propagation
is considered in the GPR-SAR algorithm (εr=1), so the
echo corresponding to the reflection on the buried targets will
appear deeper than the true position of the targets.
Fig. 7(a) and Fig. 8(a) show the SAR image cuts centered
at the location of the metallic disk when the SAR images of
both receiving channels are coherently combined, but before
applying SVD filtering and processing gain. It can be noticed
that the amplitude of the reflection on the metallic disk is
25 dB below the amplitude of the air-soil interface (located
at z=0 cm), and only around 5 dB above the ground/clutter
level.
Next, results when SVD filtering is introduced in the radar
measurements processing are depicted in Fig. 7(b) and Fig. 8
(b): a reduction in the clutter can be noticed, together with
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M. García-Fernández et al.: Airborne Multi-Channel Ground Penetrating Radar for IED and LM Detection
FIGURE 10. SAR image. Horizontal cut z= −70 cm centered at the location of the 18 cm diameter metallic disk buried 25 cm.
Normalized reflectivity, in dB. Rx channel 1 (a), Rx channel 2 (b), incoherent combination of Rx channels 1 and 2 (c), and coherent
combination of Rx channels 1 and 2 (d). SVD filtering and processing gain is applied.
the partial filtering of the reflection at the air-soil interface
(Fig. 8(b)). Fig. 7(c) and Fig. 8(c) show the effect of
applying processing gain: the level of the reflection on the
metallic disk is increased in around 6-7 dB, but also the clut-
ter. Finally, the combination of SVD filtering and processing
gain is plotted in Fig. 7(d) and Fig. 8(d), where the reflection
on the metallic disk is enhanced due to the processing gain
contribution and the clutter is significantly reduced thanks to
the SVD filtering. Apart from the reflection on the metallic
disk, another artifact located at x=0.5 m and y=7.5 is
observed, which could be caused by the soil inhomogeneity
(a wetter area or a stone).
As explained in Section III-A, SVD filtering is based on
removing the first eigenimage, associated to the highest sin-
gular value, σ1. To justify why this conservative procedure
has been adopted, SAR image cuts when different eigen-
images are removed are shown in Fig. 9. Results depicted
in Fig. 9(b)-(d) correspond to the cases where one, two,
and four eigenimages are removed (associated to the highest
singular values), whereas Fig. 9(a) corresponds to the case
where SVD filtering is not applied. It can be noticed that the
clutter level increases as more eigenimages (associated to the
highest correlated information, such as air-soil reflections)
are removed.
Concerning the depth at which the echo is located, it is
70 cm below the air-soil reflection. As the true depth is 25 cm,
the soil permittivity can be estimated as εr=(70/25)2=7.8,
which is within the range of the expected relative permittivity
for a loamy soil (εr=5 to 8).
As stated in Section III-B, the goal behind the coherent
combination of the two receiving channels is to reduce the
clutter, as well as increasing the scanned area per unit of
time. The former is due to the fact that clutter appearing
in the SAR images created using only the measurements of
one receiving channel is likely to cancel partially or totally
when the SAR images of both channels are added coherently.
Hence, the reflectivity values corresponding to reflections
at buried targets would be reinforced. Results of the SAR
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M. García-Fernández et al.: Airborne Multi-Channel Ground Penetrating Radar for IED and LM Detection
FIGURE 11. SAR image. Vertical along-track cut x= −11 cm centered at the location of the 18 cm diameter metallic disk buried
25 cm. Normalized reflectivity, in dB. Rx channel 1 (a), Rx channel 2 (b), incoherent combination of Rx channels 1 and 2 (c), and
coherent combination of Rx channels 1 and 2 (d). SVD filtering and processing gain is applied.
FIGURE 12. SAR image when along-track flight speed is 50 cm/s. Cuts z= −70 cm (a) and x= −11 cm (b) centered at the location
of the 18 cm diameter metallic disk buried 25 cm. Normalized reflectivity, in dB. Coherent combination of Rx channels 1 and 2,
applying SVD filtering and processing gain.
images are shown in Fig. 10 (a) and Fig. 11 (a) for channel 1,
and in Fig. 10 (b) and Fig. 11 (b) for channel 2. Horizontal
and vertical cuts of the SAR image corresponding to the
incoherent (power) combination of the SAR images of the
two receiving channels are depicted in Fig. 10 (c) and
Fig. 11 (c). For comparison purposes, results corresponding
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M. García-Fernández et al.: Airborne Multi-Channel Ground Penetrating Radar for IED and LM Detection
FIGURE 13. SAR image. Cuts centered at the location of the 16 cm diameter anti-personnel plastic landmine buried 13 cm.
Normalized reflectivity, in dB. Coherent combination of Rx channels 1 and 2. Without applying processing gain nor SVD filtering: cut
z= −38 cm (a) and x=61 cm (d). Applying processing gain and SVD filtering (first eigenimage removed): cut z= −44 cm (b) and
x=64 cm (e). Applying processing gain and SVD filtering (first and second eigenimages removed): cut z= −46 cm (c) and x=64
cm (f).
to the coherent combination of both channels are shown
in Fig. 10 (d) and Fig. 11 (d). Clutter at x=0.5 m
and y=7.5 m is present in the four compared cuts (at
z= −70 cm and x= −5 cm), but clutter around x=
0 m and y=6.5 m disappears when coherent combination
is applied. Thus, the former artifact observed in the SAR
images could denote the presence of another object (e.g. a
stone) or a wetter area of the soil. Concerning the detectability
of the metallic disk, the amplitude of the reflectivity of the
metallic disk is almost the same in the four compared results
(around −18 dB).
The impact of the flight smoothness in the SAR images
is also analyzed by processing the measurements collected
during the flight conducted at an along-track flight speed
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M. García-Fernández et al.: Airborne Multi-Channel Ground Penetrating Radar for IED and LM Detection
FIGURE 14. Probability density function of the reflectivity within the
investigation domain. Comparison of the impact of SVD filtering and
processing gain (a). Comparison of coherent and incoherent combination
of the SAR images associated to each receiving channel of the radar
module (b). Vertical lines represent the reflectivity of the buried metallic
disk for each analyzed case.
of 50 cm/s (Fig. 6(a,b)). SAR results are shown in Fig. 12,
applying the same processing and improvements as in Fig. 7
(d) and Fig. 8(d). If both figures are compared, it can be
observed that results from the flight at 50 cm/s exhibit higher
clutter, especially within the area corresponding to y=7 m
to y=8.5 m. The presence of more clutter can be due to
the fact that, as the acquisition positions are less uniformly
spaced, gaps greater than half-a-wavelength (5 cm at the
highest frequency) may occur. This results in partial aliasing,
observed in the SAR image as clutter.
SAR images corresponding to the horizontal and vertical
cuts centered at the location of the anti-personnel plastic
landmine are shown in Fig. 13. The coherent combination
of the two receiving channels is applied for obtaining these
FIGURE 15. Probability density function of the reflectivity within the
investigation domain. Comparison of the impact of SVD filtering when
different number of eigenimages are removed. Vertical lines represent the
reflectivity of the buried metallic disk for each analyzed case.
results. Fig. 13 (a,d) corresponds to the cases where nei-
ther SVD filtering nor processing gain are applied, whereas
the results after considering these improvements are shown
in Fig. 13 (b,e). As in the case of the metallic disk (Fig. 7
and Fig. 8), the amplitude of the reflectivity correspond-
ing to the plastic landmine is increased by 6-7 dB, without
observing a significant impact in the clutter. In the case of
Fig. 13 (e) the interface between the soil and the plastic land-
mine is detected at a depth of z= −44 cm. Besides, another
reflection happening 15 cm deeper can be also observed. This
echo can be the reflection from the lower face of the plastic
landmine, that is, from the interface between the base of the
plastic landmine and the soil. The plastic landmine is 8 cm
thick, so its relative permittivity ( εr,PLM) can be estimated as
follows: εr,PLM =(15/8)2=3.5.
Fig. 13 (c,f) corresponds to the case where the first and sec-
ond eigenimages are removed when SVD filtering is applied.
Similarly to the results depicted in Fig. 9, removing more
eigenimages increases the clutter of the SAR image.
D. QUANTITATIVE ANALYSIS
In order to quantify the improvements introduced in this con-
tribution (the coherent combination of the receiving channels
and the application of SVD filtering and processing gain),
the probability density function of the SAR image normalized
amplitude is computed [32]. Results are shown in Fig. 14.
First, the impact of SVD filtering and processing gain is
shown in Fig. 14 (a), considering the coherent combination of
the SAR images for each receiving channel. The reflectivity
of the metallic disk according to the levels observed in Fig. 7
and Fig. 8are indicated in the figure with vertical lines. The
peak of the probability density function corresponds to the
clutter level of the SAR image, so the Signal-to-Clutter Ratio
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M. García-Fernández et al.: Airborne Multi-Channel Ground Penetrating Radar for IED and LM Detection
FIGURE 16. SAR image. Cuts z= −70 cm (a,b) and x= −11 cm (c,d) centered at the location of the 18 cm diameter metallic disk
buried 25 cm deep. Normalized reflectivity, in dB. Coherent combination of Rx channels 1 and 2, applying SVD filtering and
processing gain. Considering a mask of size Mx×Myto compute the reflectivity on each voxel (a,c), and considering the entire
acquisition domain (Lx×Ly) to compute the reflectivity on each voxel (b,d).
can be estimated as the difference between the amplitude of
the target (in this example, the metallic disk) and the peak of
the probability density function.
The red line corresponds to the case where no pro-
cessing improvements are introduced, noticing that the
Signal-to-Clutter Ratio is around 8 dB (the reflectivity level
of the metallic disk is around −25 dB). When SVD filtering is
introduced (yellow line), the reflectivity level of the metallic
disk remains at −25 dB, but the clutter is reduced, thus result-
ing in an improvement of 3 dB in the Signal-to-Clutter Ratio.
Besides, the level of the probability density function between
−15 dB and −3 dB decreases (Fig. 14 (b), area shaded in red)
as part of the reflection at the air-soil interface is also filtered.
When processing gain is applied (dashed green line), both
the amplitude of the reflection on the target and the clutter
increase, but at a different rate (reflectivity of the metallic disk
increases from −25 dB to −18 dB, and clutter from −33 dB
to −30 dB). Thus, processing gain improves the Signal-to-
Clutter Ratio in around 3-4 dB, similarly to SVD filtering.
Finally, the blue line shows the combination of processing
gain and SVD filtering. It can be noticed that the clutter is
similar to the case where no improvements were introduced
(red line). However, as the amplitude of the reflection on the
target has been improved by means of the processing gain,
the resulting Signal-to-Clutter Ratio when combining SVD
filtering and processing gain increases until 15 dB.
Quantitative analysis of the impact of the coherent combi-
nation of the SAR images is analyzed in Fig. 14 (b). The black
line corresponds to incoherent combination of the receiving
channels when SVD filtering and processing gain are applied
(Fig. 10 (c) and Fig. 11 (c)), and the blue line corresponds
to the coherent combination of the receiving channels when
SVD filtering and processing gain are applied (Fig. 10 (d) and
Fig. 11 (d)). If the incoherent and coherent combinations are
compared, it can be noticed that, whereas the reflectivity of
the metallic disk does not change significantly, the peak of the
clutter decreases around 2-3 dB when coherent combination
is considered. This reduction in the amplitude of the clutter
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M. García-Fernández et al.: Airborne Multi-Channel Ground Penetrating Radar for IED and LM Detection
corresponds to the area shadowed in gray color in Fig. 14 (b),
and supports the fact that the coherent combination of two
SAR images contributes to mitigate the clutter. The impact in
the Signal-to-Clutter Ratio is an increase of around 3 dB.
Next, the probability density function when different num-
ber of eigenimages are removed is plotted in Fig. 15 to quan-
tify the SVD analysis depicted in Fig. 9. It can be observed
that the Signal-to-Clutter Ratio decreases when more eigen-
images are removed, achieving the maximum when only the
first eigenimage is removed.
E. EFFECT OF MASKING THE ACQUISITION DOMAIN
As explained in Section III-B, to compute the reflectivity
on each voxel of the investigation domain, only the radar
measurements within a mask of size Mx=1 m ×My=2
m centered at the x0,y0coordinates of the voxel were consid-
ered. This mask defines the amount of measurements that are
coherently combined to form the SAR image, and its size is
defined from the estimation of the coherence length along x
and yaxes.
The impact of considering a reduced set of measurements
to calculate the reflectivity on each voxel with respect to the
use of all the measurements within the acquisition domain
is assessed in Fig. 16. In this figure, the SAR images at the
horizontal and vertical cuts centered at the metallic disk are
compared with and without applying masking. It could be
expected that the use of all the measurements to compute the
reflectivity on each voxel would result in better resolution
(as the synthetic aperture size would be Lx×Lyinstead of
Mx×My). However, the reality is that the resulting SAR
images (Fig. 16 (b,d)) exhibit more clutter and even worse
cross-range resolution. Cumulative geo-referring errors cause
that position uncertainties between the first and last acquisi-
tions are likely to be greater than the required uncertainty to
apply SAR processing (around 1/10 of the wavelength).
V. CONCLUSION
An improved UAV-based GPR system for the safe detection
of IEDs and landmines has been presented. Some of the
improvements have been done in the area of radar data pre-
processing, by means of SVD filtering and processing gain.
The former reduces the clutter due to the reflection of the
signal in the air-soil interface, and the latter improves the
Signal-to-Clutter Ratio within a particular depth range, thus
allowing better detection capabilities. Besides, the use of a
dual-channel receiver contributes to clutter reduction by per-
foming the coherent combination of the SAR images obtained
for each channel. As shown in the results, when applying
together SVD filtering, processing gain and coherent combi-
nation, the Signal-to-Clutter Ratio is improved around 7 dB
(from 8 dB to 15 dB). Finally, the employment of masks for
computing the SAR images also contributes to mitigate the
clutter and to improve the target discrimination.
Compared to the previous version of the system, [32],
the SAR imaging area (investigation domain) has been
increased from 1 m ×4 m to 1.6 m ×8 m (across-track
and along-track dimensions respectively). This means that the
scanning capabilities have been increased by a factor of 3
thanks to the improvements described in this contributions
while maintaining the same UAV platform and batteries.
Is is worth noting that, although this UAV-based GPR
system is primarily devoted to detect explosives such as
antipersonnel landmines, it can also be used for other non-
destructive testing GPR applications, such as the detection of
buried civil infrastructure.
Finally, a video summarizing the improvements and results
presented in this contribution can be watched at: https://
youtu.be/8y-rqTZqxAw.
ACKNOWLEDGMENT
The authors would like to thank D. C. Martínez and
A. D. Mitri for their help and support with the UAV flight
preparation at the airfield of the University of Oviedo. The
authors would like to acknowledge Cap. Santiago García
Ramos and the CIED-COE (Col. José Luis Mingote Abad) for
the supervision of the Contract 2019/SP03390102/00000204/
CN-19-002 (‘‘SAFEDRONE’’) and for the technical sugges-
tions concerning the preparation of the tests.
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_Search_and_Clearance.pdf
MARÍA GARCÍA-FERNÁNDEZ was born in
Luarca, Spain, in 1992. She received the M.Sc.
and Ph.D. degrees in telecommunication engi-
neering from the University of Oviedo, Spain,
in 2016 and 2019, respectively. Since 2013, she
has been involved in several research projects with
the Signal Theory and Communications Research
Group, TSC-UNIOVI, University of Oviedo. She
was a Visiting Student with Stanford University,
Palo Alto, CA, USA, in 2013 and 2014, a Visiting
Scholar with the Gordon Center for Subsurface Sensing and Imaging Sys-
tems, Northeastern University, Boston, MA, USA, in 2018, and a Visiting
Researcher at the Radar Department of TNO, The Hague, The Netherlands,
in 2019. Her current research interests include inverse scattering, remote
sensing, radar systems, imaging techniques, antenna measurement and diag-
nostics, and non-invasive measurement systems on board unmanned aerial
vehicles. She was a recipient of the 2020 National Award to the Best Ph.D.
Thesis on Telecommunication Engineering (category: telecommunications
technologies and applications).
YURI ÁLVAREZ LÓPEZ (Senior Member, IEEE)
received the M.S. and Ph.D. degrees in telecom-
munication engineering from the University of
Oviedo, Spain, in 2006 and 2009, respectively.
He was a Visiting Scholar with the Department
of Electrical Engineering and Computer Science,
Syracuse University, Syracuse, USA, in 2008,
a Visiting Postdoc at the Gordon Center for Sub-
surface Sensing and Imaging Systems (CenSSIS)
ALERT Center of Excellence, Northeastern Uni-
versity, Boston, USA, from 2011 to 2014, and a Visiting Postdoc at the
ELEDIA Research Center, Trento, Italy, in 2015. He has been with the Signal
Theory and Communications research group of the University of Oviedo,
Gijón, Spain, since 2006, where he is currently a Professor. 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. He was a
recipient of the 2011 Regional and National Awards to the Best Ph.D. Thesis
on Telecommunication Engineering (category: security and defense).
165942 VOLUME 8, 2020

M. García-Fernández et al.: Airborne Multi-Channel Ground Penetrating Radar for IED and LM Detection
FERNANDO LAS-HERAS ANDRÉS (Senior
Member, IEEE) received the M.S. and Ph.D.
degrees in telecommunication engineering from
the Technical University of Madrid (UPM),
in 1987 and 1990, respectively. He was a National
Graduate Research Fellow, from 1988 to 1990,
and an Associate Professor with the Department of
Signal, Systems and Radiocommunications of the
UPM, from 1991 to 2000. From December 2003,
he holds a Full-Professor position at the University
of Oviedo, where he was the Vice-dean for Telecommunication Engineer-
ing at the Technical School of Engineering, Gijón, from 2004 to 2008.
As of 2001, he heads the research group Signal Theory and Communications
TSC-UNIOVI at the Department of Electrical Engineering of the University
of Oviedo. He was a Visiting Lecturer with the National University of
Engineering, Peru, in 1996, a Visiting Researcher with Syracuse University,
New York, in 2000, and a short term Visiting Lecturer with ESIGELEC,
France, from 2005 to 2011. He held the Telefónica Chair on RF Technolo-
gies, ICTs applied to Environment and ICTs and Smartcities at the University
of Oviedo, from 2005 to 2015. A member of the Board of Directors of
the IEEE Spain Section, from 2012 to 2017, and a Vice-President, from
2020 to 2022, of the board of the joint IEEE MTT-S (Microwave Theory and
Techniques) and AP-S (Antennas and Propagation) Spain Chapter, a member
of the Science, Technology and Innovation Council of Asturias, from 2010 to
2012, and a President of the professional association of Telecommunica-
tion Engineers at Asturias. He has led and participated in a great number
of research projects and has authored over 200 journal articles and over
250 at international conferences on antennas, metamaterials, and inverse
problems with application to antenna measurement, electromagnetic imaging
and localization, developing computational electromagnetics algorithms and
technology on microwaves, millimeter wave, and THz frequency bands.
VOLUME 8, 2020 165943