Realistic numerical modelling of ground penetrating radar for landmine detection
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Date
27/06/2016Author
Giannakis, Iraklis
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Abstract
Ground-Penetrating Radar (GPR) is a popular non-destructive geophysical
technique with a wide range of diverse applications. Civil engineering, hydrogeophysics,
forensic, glacier geology, human detection and borehole geology are
some of the fields in which GPR has been applied with successful or promising
results. One of the most mainstream applications of GPR is landmine detection.
A lot of methods have been suggested over the years to assist the landmine
detection issue. Metal detectors, trained rats or dogs, chemical methods and
electrical resistivity tomography are –amongst others– some of the suggested
techniques. The non-destructive nature of GPR makes it an attractive choice for
a problem such as demining in which contact to the ground is not allowed. The
main advantage of GPR is its ability to detect both metallic and non-metallic
targets. Furthermore, GPR can provide an insight regarding the nature of the
target (e.g. size, burial depth, type). From the above, it is evident that GPR
can potentially reduce the false alarms emerging from small metallic objects
(e.g. bullets, wires, etc.) usually encountered in battle-fields and industrialised
areas. Combining the robustness of the metal detector with the resolution of
GPR results in a reliable and efficient detection framework which has been
successfully applied in Cambodia and Afghanistan.
Despite the promising, and in some cases impressive results, aspects of
GPR can be further improved in an effort to optimise GPR’s performance and
decrease its limitations. The validation of a GPR system is usually achieved
through the so called Receiver Operation Characteristics (ROC) which depicts
the probability of detection with respect to the false alarm rate. ROC is a
highly nonlinear function which is sensitive to the environment as well as to
the antenna unit.
Landmines are typically small objects, often less than 10 cm diameter, which
are shallow buried, usually in less than 10 cm depth, and sometimes almost
exposed. In order for the landmines to be resolved, high frequency antennas
are essential. The latter are sensitive to soil’s inhomogeneities, rough surface,
water puddles, vegetation and so on. Apart from that, the near field nature of
the problem makes the antenna unit part of the medium which contributes to
the unwanted clutter. The above, outlines the multi-parametric nature of the
problem for which no straightforward approach has yet to be proposed.
Numerical modelling is a practical and solid approach to understand the
physical behaviour of a system. In the case of GPR for landmine detection,
numerical modelling can be a practical tool for designing and optimising
antennas in synthetic but nonetheless realistic conditions. Apart from that,
evaluation of a processing method only to a specific environment is not a
robust approach and does not provide any evidence for its wider inclusivity and
limitations. However, evaluation in different conditions can become costly and
unpractical. Numerical modelling can tackle this problem by providing data for
a wide range of scenarios. An extensive database of simulated responses, apart
from being a practical testbed, can be also employed as a training set for machine
learning. A multi-variable problem like demining, in order to be addressed using
machine learning, requires a large amount of data. These must equally include
all possible different scenarios i.e. different landmines, in different media with
stochastically varied properties and topography. Additionally, different heights
of the antenna and different depths of the landmines must also be examined.
Numerical modelling seems to be a practical approach to achieve an equally
distributed and coherent dataset like the one briefly described above.
Numerical modelling of GPR for landmine detection has been applied in
the past using generic antennas in simplified and clinical scenarios. This
approach can be used in an educational context just to provide a rough
estimation of GPR’s performance. In the present thesis a realistic numerical
scheme is suggested in which, simplifications are kept to a minimum. The
numerical solver, employed in the suggested numerical scheme, is the Finite-
Difference Time-Domain (FDTD) method. Both the dispersive properties and
the Absorbing Boundary Condition (ABC) are implemented through novel
and accurate techniques. In particular, a novel method which implements an
inclusive susceptibility function is suggested and it is shown that surpasses the
performance of the previous approaches while retaining their computational
efficiency. Furthermore, Perfectly Matched Layer (PML) and more specifically
Convolutional Perfectly Matched Layer (CPML) is implemented through a novel
time-synchronised scheme which it is proven to be more accurate compared to
the traditional CPML with no additional computational requirements.
An accurate numerical solver, although essential, is not the only requirement
for a realistic numerical framework. Accurate implementation of the geometry
and the dielectric properties of the simulated model is highly important,
especially when it comes to high-frequency near-field scenarios such as GPR
for landmine detection. In the suggested numerical scheme, both the soil’s
properties as well as the rough surface are simulated using fractal correlated
noise. It is shown, that fractals can sufficiently represent Earth’s topography
and give rise to semi-variograms often encountered in real soils. Regarding the
dielectric properties of the soils, a semi-analytic function is employed which
relates soil’s dielectric properties to its sand fraction, clay fraction, sand density,
bulk density and water volumetric fraction. Subsequently, the semi-analytic
function is approximated using a Debye function that can be easily implemented
to FDTD. Vegetation is also implemented to the model using a novel method
which simulates the geometry of vegetation through a stochastic process. The
experimentally-derived dielectric properties of vegetation are approximated
–similarly to soil’s dielectric properties– with a Debye expansion. The antenna
units tested in the numerical scheme are two bow-tie antennas based on
commercially available transducers. Regarding the targets, three landmines are
chosen, namely, PMN, PMA-1 and TS-50. Dummy landmines are used in order
to obtain their geometrical characteristics and comparison between measured
and numerically evaluated traces are used to tune the dielectric properties of
the modelled landmines. Lastly, water puddles are realistically implemented in
the model in an effort to realistically simulate high-saturated scenarios.
The proposed numerical scheme has been employed in order to test and
evaluate widely used post-processing methods. The results clearly illustrate
that post-processing methods are sensitive to the antenna unit as well as the
medium. This highlights the importance of an accurate numerical scheme as a
testbed for evaluating different GPR systems and post-processing approaches
in wide range of scenarios.
Using an equivalent 2D numerical scheme –restricted to 2D due to computational
constrains– preliminary results are given regarding the effectiveness of
Artificial Neural Network (ANN) subject to an adequate and equally distributed
database. The results are promising, showing that ANN can be successfully
employed for detection as well as classification using only a single trace as
input. A basic requirement to do so is a representative training set. This can
be synthetically generated using a realistic numerical framework. The above,
provide solid arguments for further expanding the proposed machine learning
scheme to the more computationally demanding 3D case.
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