SECTION I. Introduction
In
The last few years, the development of ground penetrating radar (GPR)
systems mounted on board unmanned aerial vehicles (UAVs) has become a
trendy research topic [1].
These kinds of systems bring together the advantages of GPR (remote
detection of buried targets, both metallic and nonmetallic [2])
and UAVs (contactless inspection of difficult to access areas). As a
result, these systems are particularly useful for detecting landmines
and improvised explosive devices (IEDs) as they allow a fast inspection
of the subsurface while keeping a safety distance to avoid accidental
detonations [3], [4], [5], [6], [7]. Furthermore, they have been also successfully used for other applications, such as measurements of soil moisture [8] or snowpack thickness [9].
In
the field of buried threats detection, the ultimate goal of UAV-mounted
GPR systems is to provide a high-resolution radar image of the
subsurface where the hidden targets can be distinguished. To retrieve
such image, the radar measurements gathered with these systems are
coherently combined using a synthetic aperture radar (SAR) algorithm [10], [11] or solving a linear inverse scattering problem [12], [13].
In turn, this requires that the measurements are georeferred with
cm-level accuracy. This challenge has been mainly overcome by
integrating a real-time kinematic (RTK) system on board the UAV, as
proposed in [3].
Several
prototypes of UAV-mounted GPR systems have been already developed for
the detection of buried targets, and some of them have been successfully
tested in different scenarios. After demonstrating the capability of
these prototypes to detect buried threats, research efforts have been
mainly devoted to improving the probability of detection and/or the
quality of the retrieved images [14]. In this sense, in [15],
a method called co-registration has been proposed to achieve a better
focused radar image. Furthermore, to improve the signal to noise ratio, a
clutter mitigation technique called distance-based singular value
decomposition (SVD) filtering is also applied to the measurements
gathered with a UAV-mounted GPR prototype [15]. Another example of improvements in the image quality is presented in [16],
where authors retrieve a model of the soil surface using interferometry
and this model is then used as input in the GPR-SAR processing to
improve the image focusing.
It is thus
timely to face the challenge of increasing the scanning throughput of
UAV-mounted GPR systems (that is, the area inspected in a given time)
without jeopardizing their detection capabilities. To this end, the use
of antenna arrays has been explored in the context of GPR systems
mounted on large terrestrial vehicles. For instance, in [17]
3 transmitter-receiver (TX-RX) antenna pairs and three vector network
analyzers (VNAs) are mounted on a truck to detect landmines, and an
arrangement consisting of six logarithmic periodic antennas elevated
with a lifter mounted on a truck was considered in [18], [19], [20] for humanitarian demining. Another example is also shown in [21], where 31TX-RX antenna pairs and a GPR are placed on a quad bike for nondestructive evaluation applications. In [22],
the authors proposed a system composed by eight pairs of antennas to be
mounted in a vehicle for roadway and utility monitoring applications.
However, the implementation of this kind of solution on a UAV-based
system is not straightforward due to the additional challenges of these
systems. In particular, the weight and size of the payload that can be
mounted on board a UAV are heavily constrained. This means that it is
significantly difficult to integrate multiple relatively large antennas.
Some other challenges are the fact that UAV-mounted GPR systems offer
reduced dynamic range compared to ground-based GPR systems as the
antennas are not in contact with the soil, and that they yield irregular
acquisition grids, among others. The first UAV-based prototypes
employing more than one RX antenna have been presented in [23] (performing 3-D scanning with 1TX and 2RX) and in [24] (performing a single forward–backward sweep with 1TX and 3RX). A multichannel UAV-mounted GPR-SAR system was recently presented in [25]. However, the integration of antenna arrays on board UAV-mounted GPR systems is still a heavily understudied research topic.
The
scope of this article is to analyze different scanning strategies for a
multichannel UAV-mounted GPR-SAR system in terms of scanning throughput
and detection capabilities. In particular, this study aims to test
scanning strategies which provide a tradeoff between the inspection time
and the desired detection performance. For that purpose, the same
antenna array configuration employed in [25], which comprises 3TX and 4RX
antennas integrated into a UAV, is considered. In particular, two
scanning strategies are compared by means of theoretical analysis,
measurements in a controlled environment, as well as measurements of the
actual UAV-mounted multichannel GPR-SAR system. The first strategy is
based on a uniform scheme, while the second relies on a nonuniform
flight path strategy called 3X.
The influence of the UAV navigation and the flight path in free-space
passive UAV-mounted radar imaging has been studied in the literature [26], [27].
However, the performance of multichannel UAV-mounted GPR-SAR systems
when using different scanning strategies (i.e., flight paths) has not
been yet analyzed.
The rest of this article is organized as follows. Section II
describes the system architecture of the UAV-mounted array-based
GPR-SAR system, the methodology used to process the measurements, and
the scanning strategies that have been compared (the uniform and the 3X schemes). A conceptual analysis and an initial validation of the 3X scheme are performed in Section III, whereas Section IV
includes a theoretical comparison of both scanning schemes. The
experimental validation has been conducted in two stages: first, ground
tests have been done using a portable scanner (presented in Section V) and, then, realistic flight tests have been performed (shown in Section VI). Finally, Section VII concludes this article.
SECTION II. System Description and Methodology
A. System Description
The UAV-mounted array-based GPR-SAR system is shown in Fig. 1. Its architecture is based on that used in the single-channel prototypes [3], [28], [29].
As most commercial UAVs, it includes the following subsystems: a flight
controller; common positioning sensors on board UAVs [in particular,
inertial measurement units, a compass, a barometer, and a conventional
global navigation satellite system (GNSS) receiver]; and a communication
subsystem. Furthermore, in order to enable GPR-SAR processing there are
two additional subsystems: the high-accuracy positioning subsystem and
the radar subsystem (highlighted in Fig. 1
in orange and green, respectively). The former comprises a multiband
multiconstellation RTK receiver (to achieve cm-level positioning
accuracy) and a laser rangefinder (to measure the distance to the
ground). The radar subsystem is in turn composed by a dual-channel
ultrawideband (UWB) radar module (which transmits a pseudorandom signal
and operates within a frequency range from 100MHz to 6GHz)
and the antenna array. The design and initial assembly of the antenna
array were conducted by the research group Teoría do Sinal e
Comunicacions, Universidade de Vigo, and they are not within the scope
of this article. A preliminary study about an array of antennas to be
mounted on a UAV for GPR has been presented in [30].
The
antenna array is formed by two subarrays: one for transmission (with
three Vivaldi antennas and a switch), and another for reception (with
four Vivaldi antennas and another switch). This results in 3TX×4RX=12TX-RX
acquisition channels. This multichannel architecture (3 TX–4 RX) allows
to increase the scanning throughput and the spatial diversity. The
latter means that the different transmitters illuminate the targets from
different angles and this, together with the use of multiple receivers,
enable to view the targets from different look angles. On the other
hand, conventional architectures (with 1 TX and 1–2 RX, such as [31])
can employ antennas working at lower frequencies (as they are usually
heavier and bulkier), which could be needed for inspecting soils with
higher losses (at the expense of increased inspection time).
The
antenna array switching sequence, taking into account that the RX
switch is a dual two-port switch and the radar module has two receiving
channels, comprises the following six steps: i) TX1-RX1,RX3; ii)
TX2-RX1,RX3; iii) TX3-RX1,RX3; iv) TX1-RX2,RX4; v) TX2-RX2,RX4; and vi)
TX3-RX2,RX4. The frequency band of the selected antennas ranges from
approximately 1to 6GHz [31]. However, as the penetration losses increase significantly with frequency, only the frequency band from 1to 3 GHz is used for processing. With the adopted configuration, the full acquisition of the 12 channels takes 280 ms. Assuming the UAV moves at 50 cm/s, this implies that a full acquisition is gathered every 14 cm (along-track), whereas a single measurement (of two channels) is acquired every 2.33
cm. The scanning strategy must ensure that the full observation domain
is sampled fulfilling the Nyquist criterion in both along-track and
across-track directions (y-axis and x-axis, respectively) [32].
B. Methodology
The
prototype is configured to autonomously fly over the region of
interest, following a predefined flight path. As in previous prototypes [23], [29], [33],
the system sends the georeferred radar measurements to the ground
control station in real time. In this case, the header of each radar
measurement also includes a field to identify the active channel
combination (i.e., the TX and the RX antenna).
Radar
measurements are processed to retrieve a 3-D GPR-SAR image of the
inspected scenario according to the flowchart shown in Fig. 2. This methodology, which has been successfully applied for UAV-mounted GPR-SAR systems [15], [29], is composed of four main blocks as follows.
Early preprocessing
(applied to each radar measurement independently). It consists of
computing the impulse response (as the radar transmits a pseudorandom
sequence) and performing a time window.
Preprocessing
(applied to all radar measurements from the same channel). This block,
which mainly aims to mitigate clutter, includes average subtraction,
height correction, and distance-based SVD filtering [15].
It should be noted that clutter mitigation techniques are essential to
detect buried targets and to improve the quality of the retrieved
images. Although there are several sources of clutter in GPR scenarios,
the major contributions are usually the strong reflections coming from
the air–soil interface [34].
Processing
(also applied to all measurements from the same channel). After
transforming the data from time to frequency domain, measurements are
coherently combined by applying a technique called masked SAR. Masked
SAR is a variation of the delay and sum (DAS) algorithm, initially
proposed in [23],
which restricts the measurements that are considered for the
computation of the reflectivity at each given point to those in its
vicinity (i.e., within a region, called mask, centered at the point
whose reflectivity is being computed). The size of the mask is selected
based on different factors, such as the area that is illuminated by the
main beamwidth of the antennas and the positioning accuracy. For these
studies, the size of the implemented mask is Lalong−track=2m (in agreement with the projected antenna beamwidth) times Lacross-track=1m.
The latter limitation has been selected after observing that the
positioning errors are less correlated among different sweeps. As a
result, after applying masked SAR, 12 3-D GPR-SAR images of the area
under inspection are retrieved (one per channel, that is, considering
the samples of both forward and backward along-track sweeps
corresponding to each combination of TX and RX antennas). In case that
co-registration is applied in the last block (postprocessing), 24 images
are obtained (two per channel, one corresponding to the forward
along-track sweeps and the other one to the backward ones).
Postprocessing.
This last step is devoted to enhancing the quality of the results. In
order to improve the range resolution of the resulting images, a
technique called equalization (described in [14])
has been developed. Furthermore, as previously mentioned, a method
called co-registration has been also proposed to enhance the image
focusing. This method consists of registering the images corresponding
to the forward and the backward along-track sweeps based on their
intensity [15].
After the equalization and co-registration (if performed), the images
from each channel are coherently combined to retrieve the final 3-D
multichannel GPR-SAR image. Finally, a technique to automatically detect
targets is applied to facilitate the inspection of the images to the
operators. This technique is based on applying a constant false alarm
rate (CFAR) detector [35] to each horizontal plane of the 3-D GPR-SAR image.
Furthermore,
a data subsampling strategy must be adopted to compensate the
nonuniform sampling, avoiding oversampled areas and mitigating the
effect of deviations from the predefined flight path. As in the case of
previous prototypes, the data subsampling strategy based on conditions
discussed in [36] has been used.
Taking into account the characteristics of the system, the spatial resolution of the system can be approximated by (1) for the along-track and across-track directions [32], [37] (where λc is the wavelength at the central frequency, L is the length of the SAR aperture length, and h is the UAV height). Therefore, considering the size of the SAR mask previously indicated, h≈1.5m, and λc=0.15m, the resulting along-track and across-track resolutions are δalong-track≈0.45λc≈6.8cm and δacross-track≈0.79λc≈11.9cm. In the z-axis (i.e., the depth), the spatial resolution depends on the bandwidth (BW) and the wave speed (v) as indicated in (2) [32], [37], where c is the light speed and εr the medium permittivity (i.e., the soil permittivity in case of buried targets)
δuδz≈λcL2/4+h2−−−−−−−−√2L≈v2BW=c2εr−−√BW≈7.5εr−−√cm.(1)(2)
View Source
C. Scanning Strategies
As
mentioned previously, the ultimate goal of the scanning strategy is to
acquire enough measurements which properly cover the whole observation
domain, so that a well-focused radar image can be retrieved by applying a
SAR algorithm. In this contribution, two different scanning strategies
will be compared: a uniform strategy and a nonuniform scheme called 3X. In the former, the along-track sweeps are uniformly spaced at a given across-track distance Δx throughout the whole observation domain. In the latter, the system performs three along-track sweeps separated λmin/2 each, then moves a larger across-track distance (Δx−λmin) to perform another three sweeps, and so forth. The notation U-Δx and 3X-Δx will be used to identify each scanning strategy and the considered across-track distance Δx. A sketch of both schemes is depicted in Fig. 3.
The
most straightforward approach is the uniform scanning strategy, where
the along-track sweeps are separated a distance small enough to acquire
measurements all over the observation domain at a sampling rate
fulfilling Nyquist criterion (in the spatial domain). The other approach
proposed in this contribution, called 3X
scheme, aims at minimizing the presence of grating lobes and, as a
result, generating a uniform illumination as large as possible under the
physical area covered by the array.
To compare the imaging
results provided by the different scanning strategies, the 3-D GPR-SAR
image obtained with each scheme is normalized to its maximum value and
the same dynamic range is employed in the graphs. This provides a fair
and meaningful comparison among the different schemes because what
improves the detectability of the targets is having a higher contrast
between the reflectivity level of the targets and that of the
surroundings.
SECTION III. Preliminary Analysis of the 3X Scheme
This section aims to provide an insight into how the idea of the 3X scheme arose. For the application considered in this contribution, λmin/2 is 5 cm, as the highest frequency used in the measurements processing is 3 GHz. However, as observed in Fig. 4(a), the across-track spacing between the antenna array elements is greater than λmin/2.
Therefore, the first analysis conducted upon the reception of the
antenna array was devoted to characterizing the grating lobes caused by
this spacing. The fields radiated by the array in the XZ plane are shown in Fig. 5. At the lowest frequency considered (f=fmin=1GHz), the field below the array is significantly uniform [see Fig. 5(a)], as the spacing between the antennas is (4/9)λmax, that is, smaller than λmax/2. However, at the highest frequency considered (f=fmax=3 GHz), the antenna spacing is (4/3)λmin, which results in the presence of several grating lobes within the investigation domain [see Fig. 5(b)].
To overcome this issue, the scanning scheme 3X is proposed, aiming to minimize the impact of grating lobes for the whole frequency band (from 1 to 3
GHz). This scheme combines the physical aperture provided by the array
with the virtual aperture achieved by moving the array in the
across-track direction (i.e., in the x-axis) a distance smaller or equal than λmin/2=5cm.
In order to better illustrate the concept, the scheme of the physical
array and the common mid points (CMPs) for each pair of TX-RX are
depicted in Fig. 4(a). Each CMP is denoted as pml, where l=1,2,3 and m=1,…,4 refer to the TX and the RX, respectively. Due to the symmetry of the elements, several TX-RX pairs have the same CMP (e.g., p12=p21), resulting in six different CMPs, which can be denoted as pi, i=1,2,…,6. It should be noted that the along-track displacement of the array (along y-axis) creates an individual path for each CMP (similarly to each individual along-track sweep of the zigzag path of [15]).
The CMPs of the physical array are spaced 13.33cm, which does not fulfill the Nyquist sampling rate at 3
GHz. For this goal, two virtual CMPs should be added in between.
Therefore, once a full measurement is performed, the array is displaced a
small distance (λmin/2 = 5 cm) across-track (along the x-axis) to acquire another set of measurements, as shown in Fig. 4(b). Then, this procedure is repeated again, obtaining the full set of measurements shown in Fig. 4(c).
Fig. 6
shows the field radiated by the array when considering the measurements
acquired at three across-track positions according to the 3X strategy [the initial position and after two λmin/2 = 5 cm lateral displacements, as depicted in Fig. 4(c)]. Comparing Figs. 5 and 6, there are no noticeable differences at 1GHz. However, at 3 GHz, the radiated field exhibits now a more uniform amplitude within the investigation domain (approximately for x∈[−20,+20]cm) and it is not affected by the presence of grating lobes.
A. Measurements Under Controlled Conditions
In
order to validate the proposed scanning strategy, measurements in a
controlled environment were conducted. The measurement setup is shown in
Fig. 7. The
radar module was placed in a plastic box and the antenna array was
attached to it. This box was manually moved along two plastic tubes in
one direction (y-axis, for reference purposes). For this experiment, along-track measurements were taken every 2 cm along a distance of 0.8 m. Then, once one along-track sweep was completed, the entire setup was displaced laterally (i.e., across-track) 5
cm and the second along-track sweep was conducted. Finally, another
lateral displacement was performed and the third along-track sweep was
acquired. As shown in Fig. 7, a metallic bar used as a reference target was placed 10 cm above the floor, tilted approximately 45∘ with respect to the x-axis and y-axis.
Radar measurements were coherently combined using a DAS technique [38] in the frequency range from 1 to 3 GHz (i.e., the one also used in UAV-mounted GPR experiments). Horizontal cuts (XY planes) of the retrieved radar images located at the same height as the metallic bar (z=0.10m) obtained when considering one, two, and the three along-track sweeps of the 3X scheme are shown in Fig. 8. When only measurements from one along-track sweep are used [see Fig. 8(a)],
the metallic bar can be identified, but the resulting image is severely
degraded by aliasing. If a second along-track sweep is included in the
processing [see Fig. 8(b)], the aliasing is noticeably reduced. Finally, with three along-track sweeps [see Fig. 8(c)],
the quality of the resulting image is greatly improved as this avoids
the aliasing produced by grating lobes, and, as it can be observed, the
shape of the bar is well-reconstructed.
SECTION IV. Theoretical Analysis
As
previously mentioned, the scanning strategy must ensure that the
observation domain is properly sampled (i.e., that enough measurements
are acquired). Therefore, before conducting field tests, a theoretical
analysis of the distribution of the measurements acquired with the array
for the different scanning strategies was performed. For this goal, the
investigation domain is divided into cells, and then the number of
measurements within each cell is analyzed. The size of the cells is set
according to the spatial sampling intervals. For short-range radar
imaging system, assuming the angle subtended by the aperture is less
than the beamwidth of the antenna and the target is a point scatterer,
the spatial sampling interval Δu must satisfy the following criterion (in along-track and across-track directions) [32], [37]:
Δu≈λminL2/4+h2−−−−−−−−√2L(3)
View Sourcewhere
λmin is the minimum working wavelength. In practical systems the spatial sampling criterion is usually set to
λmin/2,
as there is always a moderate distance between the aperture and the
imaged scene. However, instead of considering this conventional
approximation, the spatial sampling interval can be computed according
to
(3), taking into account that the UAV flies at
h≈1.5m and the size of the considered SAR mask. The resulting sampling intervals are
Δalong-track≈0.45λmin and
Δacross-track≈0.79λmin, for the along-track and across-track directions, respectively.
Four different scanning schemes have been compared: two uniform schemes (with Δx=40 cm and Δx=20 cm, denoted as U-40 and U-20 cm, respectively), and two 3X schemes (with Δx=80 cm and Δx=40 cm, denoted as 3X-80 and 3X-40 cm, respectively). In this analysis, an investigation domain of 0.8m×0.8m has been considered (depicted with a black line in Fig. 9). Furthermore, it has been assumed that measurement positions are separated 2 cm along-track (note that two measurements are acquired at each position, as there are two RX channels in the radar module).
In Fig. 9(a)–(d)
the positions of the array center are shown with blue squares, whereas
the CMP positions (that is, the positions where measurements were taken)
are shown with orange dots. The sweeps that provide measurements within
the desired investigation domain are grouped with a green line in the
top of each figure. To compare the performance of the different schemes,
the investigation domain has been discretized in cells of Δacross-track×Δalong-track
size and the number of samples falling within each cell has been
computed. Furthermore, a sampling map showing the sampled cells (in
yellow) and the nonsampled ones (in blue) has been also retrieved. The
number of samples per cell and the sampling maps are shown in Fig. 9(e)–(h) and (i)–(l),
respectively, for the different scanning schemes. It should be noted
that the same color scale, as shown in the colorbar, is used to indicate
the number of samples within each cell in Fig. 9(e)–(h) for a fair comparison between all schemes. Comparing these figures, it can be concluded that both the U-20 cm and the 3X-40 cm schemes provide a full coverage of the investigation domain [as shown in Fig. 9(j) and (l), respectively]. With the 3X-80 cm scheme [see Fig. 9(k)] the edges of the investigation domain are not completely covered, whereas with the U-40 cm [see Fig. 9(i)] there are a significant number of areas without any samples.
To further compare the different schemes, a quantitative analysis is provided in Table I, showing the average and the standard deviation of the number of samples per cell (Ns¯¯¯¯¯¯ and σNs,
respectively). If the measurement positions strictly fulfill the
spatial sampling criterion (in both axes), the number of samples per
cell would be exactly 1 (i.e., Ns¯¯¯¯¯¯=1 and σNs=0).
It should be noted that if the sampling is significantly nonuniform,
this can eventually cause a degradation in the SAR image [36]
(as the oversampled areas could have high reflectivity levels even if
no targets are present). The percentage of cells sampled with each
scheme and the number of along-track sweeps required to cover the
desired investigation domain (denoted as coverage and # sweeps in Table I) have been calculated as well.
Comparing the two schemes that provide full coverage, with the U-20 cm scheme the coverage is more uniform (as shown in Fig. 9),
which is in agreement with the fact that the standard deviation of the
number of samples per cell is the smallest. On the other hand, with the 3X-40
cm scheme, there are more samples gathered all over the investigation
domain (also in agreement with having the highest average number of
samples per cell).
Finally, it is worth mentioning that the former scheme requires fewer along-track sweeps than the 3X-40 cm scheme. Therefore, it is expected that with this scheme (U-20
cm) the inspection will be faster (as the same area is covered with
fewer along-track sweeps, i.e., in less time) at the expense of
acquiring a smaller amount of samples compared to the latter scheme (3X-40 cm).
SECTION V. Ground Tests Using a Portable Scanner
After conducting the theoretical analysis, the array was placed in the portable scanner presented in [39]
to perform field tests to compare the imaging results obtained with the
different scanning schemes. In this portable scanner, a plastic box
containing the desired payload can be manually displaced over an area of
approximately 1m×1m.
Besides the antenna array, the payload includes the same UWB radar as
the one integrated into the UAV, as well as a GNSS-RTK system to provide
cm-level accuracy positioning and a microcomputer that gathers all the
information and sends the georeferred radar measurements to a ground
control station in real time.
The array-based GPR-SAR system placed in the portable scanner is shown in Fig. 10, together with a scheme of the targets used in the experimental validation. In particular, two metallic plates of 10 cm ×10 cm were placed on the ground, and a square metallic plate of 16.5 cm side was buried at 4 cm depth. The schemes analyzed in the theoretical analysis (see Section IV) were also compared using the measurements gathered with this portable scanner.
The
positions of the center of the array during the measurements performed
with the portable setup by moving the sliding arm are depicted in Fig. 11(a)–(d)
for the different scanning schemes. As it can be seen, the measurements
were not uniformly acquired, in a similar fashion as the data obtained
with a UAV-based system. In Fig. 11(e)–(h),
the number of samples acquired within each of the cells in which the
investigation domain was discretized is depicted. Finally, the sampled
cells are depicted in yellow in Fig. 11(i)–(l), whilst nonsampled cells are colored in blue. In agreement with the theoretical analysis presented in Section IV, the schemes U-20cm and 3X-40 cm provide the best coverage of the area under inspection (with almost all cells sampled), whereas the scheme U-40 cm gives the worst coverage.
The acquired measurements were processed according to the flowchart explained in Section II,
but without applying SVD filtering since in this case there is a target
on the ground. The horizontal cuts of the 3-D GPR-SAR images at z=0m and z=−0.04m are shown in Fig. 12 for the different scanning strategies. In the images at z=0m, the metallic plate on the ground can be clearly detected in all cases. In the images at z=−0.04m,
also for all the considered scanning schemes, the buried metallic plate
can be distinguished, as well as a secondary reflection from the target
on the ground.
From the analysis of the results depicted in Fig. 12 it is concluded that the schemes U-20 cm and 3X-40
cm provide GPR-SAR images with better quality (less clutter). Comparing
these schemes, it can be observed that the reflection from the buried
target (Fig. 12, plane z=−0.04m) is slightly more noticeable with the 3X-40
cm scheme, which could be due to the fact that more measurements are
gathered with this scheme. Although with the other two schemes (U-40 cm and 3X-80
cm) the two targets are still distinguishable, the clutter level is
noticeably higher. A high clutter level is especially harmful for the
detection of buried targets in those areas where the clutter itself
exhibits a significant contrast with the surroundings. These areas are
indicated with a red and black ellipse in Fig. 12. In realistic applications, these areas could make the detection of small targets (less than 10cm×10cm size) more difficult and/or they could raise the false alarm rate. Therefore, the former schemes (U-20 cm and 3X-40
cm) are preferred for the targeted application (landmines and IEDs
detection) and they have been selected to continue the experimental
validation with the UAV-mounted prototype.
SECTION VI. In-Flight Tests
Finally,
flight tests with the UAV prototype were conducted to compare the
different scanning strategies for subsurface radar imaging in a
realistic scenario. Fig. 13
shows the UAV-mounted array-based GPR-SAR system flying over the
inspected scenario. In particular, this validation was performed in the
Spanish military training and shooting range “El Palancar,” located
north of Madrid. The selected area, as well as the position of the
targets buried in it, is shown in Fig. 14. In this area, whose size is 4.5m across-track×12m along-track, 15 targets of different materials, sizes and composition were buried. The details of these targets are specified in Table II.
As shown in this table, these targets included antitank (AT) landmines,
antipersonnel (AP) landmines, a pressure plate (PP), an artillery
shell, a wooden box, and a plastic jug (acting as IED), among others.
The permittivity of the soil has been estimated as εr≈4 (following the procedure described in [40]). For the plastic targets, the permittivity is expected to be closer to εr≈3 (in agreement with the dielectric characterization performed in [41]
for a small AP mine and with that of most explosives). For the wooden
PP [target (iii)], the permittivity is likely to be around εr≈2 [42].
A. Imaging Results
As previously mentioned, the schemes U-20 cm and 3X-40 cm, which both theoretically provide a 100% coverage of the inspected area (as shown in Section IV),
were tested in this scenario. The former required only one flight
(composed of 24 along-track sweeps), whereas the latter needed two
flights to inspect the whole area (as 36 along-track sweeps must be
performed). It is worth noting that without the array, i.e., with a
conventional 1TX-1RX
down-looking GPR (DLGPR) architecture, the number of along-track sweeps
drastically increases. Assuming the practical spatial sampling
criterion of λmin/2=5cm (considered for instance in [31]), 91 along-track sweeps would be required to inspect the whole area. In terms of time, the scanning with the uniform strategy (U-20cm) takes around 10 min, whereas the 3X approach (3X-40cm) requires 15 min for inspecting the 4.5m×12m area (plus the time needed to change batteries between the two flights).
The positions of the UAV during the flights, as well as the number of samples and the sampling maps, are shown in Fig. 15 for the compared scanning schemes (U-20 cm on the left and 3X-40 cm on the right). As shown in Fig. 15(e)–(f), both schemes provide a good coverage of the inspected 4.5m×12m area, although the scheme 3X-40
cm gathers more samples. In particular, 92.7% of the cells within the
inspected area [enclosed by a black and white rectangle on Fig. 15(e)–(f)] are sampled with this scheme, whereas the coverage decreases to 83.1% with the U-20 cm scheme. A summary of the main statistics for both schemes is given in Table III. In agreement with the results of previous sections, the average of the number of samples per cell is higher with the 3X-40
cm approach, whereas the standard deviation is smaller for the uniform
scheme. The reduction of coverage with respect to the theoretical
analysis is mainly due to the deviation of the actual UAV flight path
from the predefined one. As the 3X-40 cm scheme gathers more measurements than the U-20 cm, the reduction in the coverage is smaller for the 3X-40 cm scheme.
The radar measurements were processed following the flowchart explained in Section II
for each scanning strategy. Analyzing the retrieved 3-D GPR-SAR images,
all targets except target (xii), which is a small AP landmine, were
detected with both strategies. Horizontal cuts of these images are shown
in Fig. 16 (left column, U-20 cm scheme, and right column, 3X-40
cm scheme). The targets that are better distinguished are the AT
landmines, the wooden PP, the jug and the wooden box, as they exhibit
higher reflectivity levels. Besides, both the top and bottom interfaces
of these targets are detected. The medium-size AP landmines are also
clearly distinguishable from the surrounding clutter, whereas only one
of the two small-size AP landmines, the one labeled as (vi), is
detected.
It
should be noted that there are numerous factors that affect the
reflectivity levels of the targets and, hence, their detectability. A
higher contrast between the permittivity of the targets and the
surrounding soil facilitates the detection. The attenuation of the waves
increases with the distance but, on the other hand, the detection of
shallowly buried targets (at depths ≤5cm)
can be challenging (as they might be masked by the clutter from the
air–soil interface or their reflection might be mitigated due to the
application of clutter removal techniques). In addition, the target
characteristics (e.g., shape and size) and the radar system (e.g.,
frequency, angle of incidence) have also an impact on the detectability.
For instance, the shape and composition of the AT landmines [targets
(i), (ii), (xi), and (xiii)] and the medium AP landmine [target (iv)]
are similar, but the diameter and thickness of the AT landmines are
almost twice those of the medium AP landmine. As a result, the AT
landmines are better detected than the medium AP landmine, even though
they are buried deeper.
By comparing the images of the selected strategies, it can be concluded that the images of the 3X-40 cm scheme show slightly less clutter than those of the U-20 cm scheme, especially in the layers close to the soil surface (z≥−0.05m). In addition, the reflectivity levels of the targets detected at deep layers are also higher for the scheme 3X-40 cm. This can be observed by comparing the reflectivity of the bottom interface of targets (ix) and (xv) at z=−0.24m.
Both facts could be explained because with this scheme more
measurements are acquired. However, it requires more sweeps (which in
turn required two UAV flights for scanning the scenario considered in
this section) without having a significant impact on the detection
results, as the detected targets are the same as with the U-20 cm scheme.
Besides the visual inspection of the 3-D GPR-SAR images, a CFAR algorithm [35]
was applied to the horizontal cuts of these images. For both scanning
strategies, all targets except targets (x) and (xii) were detected by
the CFAR algorithm. Target (x) is the small empty water bottle, which is
also hard to distinguish in the radar images (Fig. 16, cut z=−0.08m), whereas target (xii) is the small AP landmine which was also not detected by visual inspection of the SAR image.
B. Assessment of Sparse Schemes
Although only the U-20 cm and 3X-40
cm schemes have been selected to perform the flight tests (since they
are the ones that theoretically guarantee a full coverage of the
investigation domain, as analyzed in Section IV), it is also of interest to assess how the sparser U-40 cm and 3X-80 cm schemes would perform during flights. For this purpose, half of the sweeps of the original flights (U-20 cm and 3X-40 cm schemes) have been selected to synthesize the U-40 cm and 3X-80 cm schemes, respectively. The forward sweeps of U-20 cm yield the scheme U-40 cm FW, and the backward sweeps yield U-40 cm BW. Half of the sweeps of 3X-40 cm are used to obtain 3X-80 cm (A) (when starting with the first along-track sweep) and 3X-80 cm (B) (when starting with the fourth along-track sweep). As can be observed in Fig. 17, the clutter level in the radar images increases when considering the U-40 cm and 3X-80 cm schemes compared to the results obtained with the original flights.
To
compare all the schemes, four targets have been selected: the bottom of
the wooden box (xv), one AT landmine (i), the AP butterfly landmine
(vii), and a small AP landmine (vi). The radar images corresponding to
these targets are shown in Fig. 17,
where each row corresponds to one target and each column to one
sampling scheme. Medium and big-size targets (e.g., the wooden box and
the AT landmine) are still clearly detected using the sparser schemes (U-40 cm and 3X-80
cm), although the shape is not so well reconstructed. The AP butterfly
landmine is also detected with the sparser schemes, but it becomes more
evident that the signal to clutter ratio worsens compared to the results
obtained with the original schemes (U-20 cm and 3X-40 cm). Finally, the smallest AP landmine cannot be detected with any of the sparse schemes except 3X-80 cm (A). This is because in the case of U-40 cm schemes the clutter level is similar to the target reflectivity, and for the 3X-80 cm (B) scheme the region around the target is significantly worse sampled than for 3X-80 cm (A). Therefore, it can be concluded that if the goal is to detect only medium to big size targets, the sparser schemes (U-40 cm and 3X-80 cm) can be used to reduce the inspection time even further (in particular, by half the time needed for the original schemes).
C. Discussion
In Section VI-A, the selected scanning schemes (3X−40cm and U−20cm)
have been compared mainly qualitatively, by visually inspecting the
retrieved radar images. This comparison allowed to conclude that the
same targets are detected with both schemes, although the scheme 3X−40cm
provides in general better imaging results (e.g., less clutter
particularly in the layers close to the soil surface, or higher
reflectivity for the deepest targets) at the expense of requiring a
longer flight time.
For quantitatively
comparing the imaging results, a metric called peak signal to clutter
ratio (PSCR) has been employed. The PSCR is defined, for each target, as
follows:
PSCR [dB]=10log10⎛⎝maxx,y∈At|ρ(x,y,zt)|21Nc∑x,y∈Ac|ρ(x,y,zt)|2⎞⎠(4)
View Sourcewhere
ρ(x,y,zt) is the reflectivity (i.e., the radar image) in the slice
z=zt (where the target is located),
At and
Ac are the target and clutter regions (i.e., the pixels corresponding to the target and to the clutter, respectively), and
Nc
is the number of pixels belonging to the clutter area. For the
computation of the PSCR, the clutter region is restricted to an area of
1m×1m centered at the target position (as displayed in Fig.
17), as the key to detect the targets is to distinguish them from the surrounding clutter. The target region,
At,
is defined (for each target) as a rectangle centered at the target
position which encloses the whole target (according to the size of the
target given in Table
II).
The
PSCR has been computed for all buried targets and, in the case of the
biggest targets (square shape AT landmine, jug, and wooden box) the PSCR
corresponding to both the top and bottom interfaces has been
considered. For most targets (in particular, more than 80%), the PSCR
values are higher for the 3X−40cm scheme than for the U−20cm, in agreement with the conclusion achieved by visually inspecting the GPR-SAR images. On average, there is a 1dB improvement in the PSCR when using the 3X−40cm scheme. As an example, for the AT landmine [target (i)], depicted in the second row of Fig. 17, the PSCR for the 3X−40cm strategy is 23.3dB, whereas for the U−20cm strategy it decreases to 22.0dB (i.e., there is 1.3dB difference). In the case of the bottom of the wooden box [target (xv)], the improvement in the PSCR for the 3X−40cm scheme is higher (2.4dB difference), as expected from the increase in the target reflectivity observed in the GPR-SAR images (see first row of Fig. 17).
In Section VI-B, the performance of the sparse schemes (3X−80cm and U−40cm)
was analyzed, concluding that medium to big size targets are still
clearly distinguishable although the quality of the GPR-SAR images
worsens. This is also in agreement with the behavior observed for the
PSCR values. For instance, for the AP butterfly landmine [target (vii),
third row of Fig. 17], the PSCR for the 3X−40cm scheme is 15.6dB (decreasing to 13.0 and 14.5dB for 3X−80cm (A) and (B), respectively), whereas for U-20 cm is 15.9dB (decreasing to 13.5dB for U−40cm FW and BW). In the case of the small AP mine [target (vi), fourth row of Fig. 17], the PSCR for 3X−40cm is 12.6dB and it worsens slightly to 11.8dB for 3X−80cm (A). However, for the 3X−80cm (B) the PSCR drops to 5.9dB because, as previously mentioned, the area around the target is worse sampled than for the 3X−80cm (A).
The performance of the scanning schemes during the flight tests (when inspecting an area of 4.5m×12m) is summarized in Table IV,
where the average PSCR and the required flight time for each scheme are
displayed. As can be observed, the highest PSCR is obtained for the 3X−40cm scheme. The U−20cm scheme yields similar performance as 3X−40cm (only 1dB
difference in the PSCR), while the sparse schemes present lower PSCR
values. In contrast, the sparse schemes require significantly shorter
flight times than the 3X−40cm and the U−20cm
schemes to inspect a given area. These results are in agreement with
the previous discussion from a qualitative point of view, showing that a
tradeoff between imaging performance (image quality/detectability of
small targets) and flight time must be established when choosing a given
scanning scheme. In particular, in a context where the detection of
small targets is crucial and/or detailed mapping is desired, the dense
schemes should be prioritized at the expense of longer flight times (U−20cm for achieving a higher inspection speed or 3X−40cm
for better imaging results). In contrast, the sparse schemes can be of
interest in applications involving the detection of medium to large size
targets when a fast survey speed is required (with U−40cm providing the fastest inspection).
SECTION VII. Conclusion
This
article has analyzed several scanning strategies to increase the
scanning throughput of an array-based GPR-SAR system on board a UAV. In
particular, two different strategies have been compared: a uniform
scheme (where the sweeps are separated a given distance) and a
nonuniform strategy called 3X
(which aims to minimize the presence of grating lobes and to achieve a
uniform illumination in the area under the array). Both strategies have
been analyzed following a theoretical analysis (proving that a 100%
coverage of the investigation domain can be achieved) and with
experiments (both on ground and in-flight).
Results show that the same targets can be detected with both (dense) scanning schemes (i.e., U-20 cm and 3X-40 cm). The latter scheme (3X-40
cm) yields radar images with slightly less clutter (especially at
shallow layers) and, furthermore, the deepest targets exhibit higher
signal levels. However, it requires more sweeps than the uniform
approach (U-20
cm) and it is not as easy to implement (as a UAV mission control
software must be developed to define such a nonuniform flight path). It
has also been shown that the sparser schemes (U-40cm and 3X-80 cm) could also be used if the goal is to detect medium to big size targets. In the case of the uniform sparse scheme (U-40cm), only 5min would be needed to inspect an area of 60m2, further increasing the survey speed of the system.
Taking into account the above-mentioned considerations, for detailed mapping the dense scanning schemes (U-20 cm and 3X-40 cm) should be used. In particular, to obtain better signal to clutter levels, the scheme 3X-40
cm is beneficial, at the expense of longer flights. Alternatively, if
the goal is to reduce the inspection time (without jeopardizing the
detection of the targets), the U-20 cm should be employed. Moreover, the inspection time can be furthered decreased (leveraging the sparse U-40 cm or 3X-80 cm schemes) if only the detection of medium to large targets is required.
Compared
to the conventional single-channel DLGPR architecture, using the
array-based GPR-SAR system results in a drastic reduction on the number
of required sweeps to inspect a given area. In particular, to inspect an
area of 4.5m×12m only 39.6% of the sweeps needed with a conventional single-channel DLGPR architecture are required with the 3X-40 cm scheme, and only 26.4% with the uniform strategy (U-20 cm). This means that the (dense) uniform strategy would allow to inspect an area of 60m2 in around 10min
without jeopardizing the detection capabilities. This constitutes a
significant improvement, paving the way to the operational use of this
technology.
ACKNOWLDGMENT
The authors would like to thank the personnel
of the Counter Improvised Explosive Devices Center of Excellence (C-IED
CoE) and the Ministry of Defense of Spain for their counseling and
support on the topic of landmine and IED detection, as well as for the
preparation of the scenarios for the flight tests. For the purpose of
open access, the author has applied a Creative Commons Attribution (CC
BY) licence to anyAuthor Accepted Manuscript version arising. Data
supporting this study cannot be made available due to legal/security
concerns.
![Fig. 17. - GPR-SAR images obtained with the array-based GPR-SAR system on board the UAV for several targets (by rows, the wooden box, an AT landmine, the AP butterfly landmine and a small AP landmine) and for different scanning schemes [by columns, $U$-$\text{20}$ cm, $U$-$\text{40}$ cm FW, $U$-$\text{40}$ cm BW, $\text{3}X$-$\text{40}$ cm, $\text{3}X$-$\text{80}$ cm (A) and $\text{3}X$-$\text{80}$ cm (B)]. The PSCR is indicated in white for each case.](https://ieeexplore.ieee.org/mediastore_new/IEEE/content/media/4609443/10330207/10384674/garci17-3351602-large.gif)
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