In this study, we present a brief information about GPR data processing and then processing methods are proposed dedicated to false alarm reduction with varying antenna height. We used horizontal filtering in cross-track and continuous wavelet transformation for A-scan signals. Additionally, 2D Wavelets and Gabor filters are applied to the data. Comparative results are presented over real data set obtained from various buried objects. It is observed that the horizontal filtering gives satisfactory results especially in cases where there is variability in antenna height.
A novel feature extraction and buried object identification method for ground penetrating radar data are presented. Discriminative features are obtained by modelling the most dynamic peaks of GPR A-scan signals, utilizing principal component analysis (PCA). Landmine/clutter discrimination is then achieved using fuzzy k-nearest neighbor algorithm. The identification results are presented on a real data set of 700 surrogate landmines and clutter objects, which were collected from three different terrains with various soil types and buried object depths. We show that the proposed method gives outstanding results over this extensive data set.
Millimeter wave absorption relative to background soil can be used for detection landmines with little or no metal content. At these frequencies, soil and landmine absorb electromagnetic energy differently. Stepped frequency measurements from 20 GHz to 60 GHz were used to detect buried surrogate landmines in the soil. The targets were 3 cm and 5 cm beneath the soil surface and coherent transmission and reflection was used in the experimental setup. The measurement set-up was mounted on a handheld portable device, and this device was on a rail for accurate displacement such that the rail could move freely along the scan axis. Measurements were performed with network analyzer and scattering data in frequency domain were recorded for processing, namely for inverse Fourier Transform and background subtraction. Background subtraction was performed through a numerical filter to achieve higher contrast ratio. Although the numerical filter used was a simple routine with minimal computational burden, a specific detection method was applied to the background subtracted GPR data, which was based on correlation summation of consecutive A-scan signals in a predefined window length.
Detection of landmines based on complex resonance frequencies has been studied in the past and no distinctive results have been reported. Especially for low metal content landmines buried at depths greater than 9 cm, resonant frequencies become fairly distributed in the background and no specific frequency of interest can be used. However, in a typical impulse radar, spectral energy density of the transmitted pulse can be very broad and its peak can be located anywhere. Usually, a compromise is made between penetration depth and feature resolution for spectral energy peak allocation. Pulse amplitude, duration, symmetry, its spectral energy distribution, ringing level all affect depth and resolution metrics in a complicated way. Considering receiver dynamic range, we study two distinct pulses having different spectral energy density peaks and their detection ability for landmines with little or no metallic content. We carry out experiments to show that pulse shape/fidelity is critical to obtain desired contrast in post-processing of data.
Proc. SPIE. 8017, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI
KEYWORDS: Target detection, Wavelet transforms, Independent component analysis, Detection and tracking algorithms, Mining, Palladium, Radon transform, Ground penetrating radar, Land mines, General packet radio service
In this study, we provide an extensive comparison of different clutter suppression techniques that are proposed
to enhance ground penetrating radar (GPR) data. Unlike previous studies, we directly measure and present
the effect of these preprocessing algorithms on the detection performance. Basic linear prediction algorithm
is selected as the detection scheme and it is applied to real GPR data after applying each of the available
clutter suppression techniques. All methods are tested on an extensive data set of different surrogate mines
and other objects that are commonly encountered under the ground. Among several algorithms, singular value
decomposition based clutter suppression stands out with its superior performance and low computational cost,
which makes it practical to use in real-time applications.
In this study, a general structure for hand-held multi-sensor mine detection system is proposed. Ideal sensor
configuration for multi-sensor mine detection system, requirements of hardware structures, data transfer issues and
operational restrictions are discussed. The properties of a sample system designed according to the proposed structure
are explained and a new graphical user interface is presented.
In this study, we present generation of Strip-map Synthetic Aperture Radar (SAR) images using impulse GPR system,
and investigate effects of different soil types on SAR images. The SAR images of buried objects have been interpreted
via 2D inverse Fourier transformation. GPR buried target data have been collected from three soil pools having different
dielectric constants and B-scan images have been reconstructed from the received data using mean A-scan signal
subtraction method. In order to reconstruct SAR images, the time domain data collected from multiple observation
points have been transformed to 2D spectral domain. Non-uniform data have been interpolated over spatial Cartesian
grid by using uniform interval. Thus, the SAR images have been reconstructed via 2D inverse FFT of interpolated data
on ky-kz plane. When examined mathematical background of SAR algorithm, the values of different dielectric constants
change the wave number of k. This can lead to deterioration of the SAR imagery. In this study, we investigate the Effect
of the dielectric constant of different soils has been examined on SAR images. Finally, resolution difference between
background removed B-Scan data and SAR images is considered.
In the underground inspection problem, signature of a big target at a certain depth may give equivalent
information to the signature of a smaller target at shallower depth, unless depth information is not used. This
results in a difficulty in the identification process. Therefore, depth information is coming into prominence
in the classification step to increase the identification performance. In this study, we propose a burial depth
estimation method on GPR data. In our work, discrete wavelet transform is used in the preprocessing step.
After this stage, statistical hypothesis tests are utilized to detect the statistical discrepancies in the returning
signals at different depth levels.
In this paper hand-held dual sensor detector development requirements are considered dedicated to buried object
detection. Design characteristics of such a system are categorized and listed. Hardware and software structures,
ergonomics, user interface, environmental and EMC/EMI tests to be applied and performance test issues are studied.
Main properties of the developed system (SEZER) are presented, which contains Metal Detector (MD) and Ground
Penetrating Radar (GPR). The realized system has ergonomic structure and can detect both metallic and non-metallic
buried objects. Moreover classification of target is possible if it was defined to the signal processing software in learning
In this study, identification of the different metallic objects with various burial depths was considered. Metal Detector
(MD) and Ground Penetrating Radar (GPR) were used to obtain metallic content and dielectric characteristic of the
buried objects. Discriminative features were determined and calculated for data set. Six features were selected for metal
detector and one for Ground Penetrating Radar. Twenty classification algorithms were examined to obtain the best
classification method, for this data set. A Meta learner algorithm completed the classification process with 100%
In this study discrimination of two different metallic object classes were studied, utilizing Ground Penetrating Radar
(GPR). Feature sets of both classes have almost the same information for both Metal Detector (MD) and GPR data.
There were no evident features those are easily discriminate classes. Background removal has been applied to original
B-Scan data and then a normalization process was performed. Image thresholding was applied to segment B-Scan GPR
images. So, main hyperbolic shape of buried object reflection was extracted and then a morphological process was
performed optionally. Templates of each class representatives have been obtained and they were searched whether they
match with true class or not. Two data sets were examined experimentally. Actually they were obtained in different time
and burial for the same objects. Considerably high discrimination performance was obtained which was not possible by
using individual Metal Detector data.
In this study buried object detection on the GPR data is examined using CA-CFAR detector. In the first part of the study
the background signals of B-scan frames from a pulse GPR is statistically inspected. The results revealed that the
background signals residual from a removing process of the dominant GPR signals due to air-to-ground interface have
shown K-Distributed statistics. The form and scale parameters of K-Distribution are estimated using the fractional
moments. The background or the clutter signals from three different soils have resulted in distinctive shape parameters.
The shape parameter of the distribution could generally discriminate three soils. In the second part of the study the
receiver loss of CA-CFAR detector is estimated using a numerical method and the Monte-Carlo simulation. The
receiver loss is also associated to the K-Distribution and CA-CFAR detector parameters in the simulation. Time series
with statistical properties similar to those of the real measurements are obtained using SIRV and employed in the
Monte-Carlo simulation. In the third part of the study effectiveness of CA-CFAR detector on B-scan frames is analyzed
by measuring the ROC of the detector. High detection probabilities of buried objects at relatively low SNR data are
obtained by CA-CFAR detector.
Electromagnetic Induction sensor (Metal Detector) has wide application areas for buried metallic object searching, such
as detection of buried pipes, mine and mine like-targets, etc. In this paper, identification of buried metallic objects was
studied. The distinctive features of the signal were obtained, than classification process was performed. Identification
process was realized by utilizing k-Nearest neighbor and Neural Network Classifiers.
Proc. SPIE. 5794, Detection and Remediation Technologies for Mines and Minelike Targets X
KEYWORDS: Target detection, Detection and tracking algorithms, Dielectrics, Inspection, Linear filtering, Signal processing, Dielectric filters, Signal detection, Land mines, General packet radio service
In this paper, we studied the effects of the preprocessing techniques over buried object detection performance. We examined different preprocessing techniques applied before the detection algorithm proposed by Sezgin . It is obtained that used preprocessing techniques decreases false alarm rates in real environment. We used different size of objects and burial depths for both metallic and non-metallic targets.
Proc. SPIE. 5415, Detection and Remediation Technologies for Mines and Minelike Targets IX
KEYWORDS: Target detection, Detection and tracking algorithms, Modulation, Signal processing, Spatial resolution, Signal detection, Environmental sensing, Ground penetrating radar, Land mines, General packet radio service
In this work the detection process of buried objects is presented utilizing Ground Penetrating Radar (GPR). Background removal algorithm is used to obtain the target signature and correlation process is performed to reveal the reflected target energy Then, a detection warning signal is created depending on a special process. In this work, pulsed GPR system with 1 GHz bandwith is used. Scanning speed is 0.33cm/sec in the sweeping direction and this process is repeated in the walking direction with 4 cm spatial resolution.