Efficient Underground Object Detection for Ground Penetrating Radar Signals

2016 ◽  
Vol 67 (1) ◽  
pp. 12 ◽  
Author(s):  
Ibrahim Mesecan ◽  
Ihsan Omur Bucak
Keyword(s):  
Object Detection ◽  
Ground Penetrating Radar ◽  
Electromagnetic Waves ◽  
Support Vector ◽  
Step Method ◽  
Image Scaling ◽  
Detection Ratio ◽  
Pass Through ◽  
B Scan Images ◽  
Ground Penetrating

Ground penetrating radar (GPR) is one of the common sensor system for underground inspection. GPR emits electromagnetic waves which can pass through objects. The reflecting waves are recorded and digitised, and then, the B-scan images are formed. According to the properties of scanning object, GPR creates higher or lower intensity values on the object regions. Thus, these changes in signal represent the properties of scanning object. This paper proposes a 3-step method to detect and discriminate landmines: n-row average-subtraction (NRAS); Min-max normalisation; and image scaling. Proposed method has been tested using 3 common algorithms from the literature. According to the results, it has increased object detection ratio and positive object discrimination (POD) significantly. For artificial neural networks (ANN), POD has increased from 77.4 per cent to 87.7 per cent. And, it has increased from 37.8 per cent to 80.2 per cent, for support vector machines (SVM).

REFLECTION COEFFICIENT OF AN ELECTROMAGNETIC WAVE IN CONDUCTIVE MEDIA

2018 ◽  
pp. 100-106
Author(s):  
M. S. Sudakova ◽  
M. L. Vladov ◽  
M. R. Sadurtdinov
Keyword(s):  
Reflection Coefficient ◽  
Ground Penetrating Radar ◽  
Electromagnetic Waves ◽  
Water Contamination ◽  
Survey Design ◽  
Direct And Inverse Problems ◽  
The Difference ◽  
Ground Penetrating ◽  
Ideal Dielectric

Within the ground penetrating radar bandwidth the medium is considered to be an ideal dielectric, which is not always true. Electromagnetic waves reflection coefficient conductivity dependence showed a significant role of the difference in conductivity in reflection strength. It was confirmed by physical modeling. Conductivity of geological media should be taken into account when solving direct and inverse problems, survey design planning, etc. Ground penetrating radar can be used to solve the problem of mapping of halocline or determine water contamination.

A Ground-penetrating Radar Object Detection Method Based on Deep Learning

2021 ◽  
Author(s):  
Siyu Chen ◽  
Li Wang ◽  
Zheng Fang ◽  
Zhensheng Shi ◽  
Anxue Zhang
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Ground Penetrating Radar ◽  
Detection Method ◽  
Ground Penetrating

scholarly journals Study Effect of Objects Orientation in the Mediums On GPR Signal Reflections Using FDTD Simulation

2018 ◽  
Vol 3 (11) ◽  
pp. 73-77
Author(s):  
Aye Mint Mohamed Mostapha ◽  
Gamil Alsharahi ◽  
Abdellah Driouach
Keyword(s):  
Finite Difference ◽  
Time Domain ◽  
Ground Penetrating Radar ◽  
High Frequency ◽  
Electromagnetic Waves ◽  
Ground Surface ◽  
Propagation Path ◽  
Fdtd Simulation ◽  
Ground Penetrating ◽  
Dielectric Objects

Ground penetrating radar (GPR) is a very effective tool for detecting and identifying objects below the ground surface.  based on  the propagation and reflection of high-frequency electromagnetic waves. The GPR reflection can be affected by many things like the type of objects orientation, their shapes ..ect. The purpose of this paper is to  study by simulation the effect of objects orientation in two different mediums (dry and wet sand) on the GPR signal reflection using Reflexw software which is based on a numerical method known as finite difference in time domain (FDTD).  The simulations that have been realized included a conductor  and dielectric objects. The results obtained have led us to find that the propagation path, the reflection strength and the signal form change with the change of object orientation and nature. To confirm the validity of the results, we compared them with experimental results previously published by researchers under the same conditions.

WISDOM Antenna Pattern in the presence of Rover and Soil

2021 ◽  
Author(s):  
Wolf-Stefan Benedix ◽  
Dirk Plettemeier ◽  
Christoph Statz ◽  
Yun Lu ◽  
Ronny Hahnel ◽  
...  
Keyword(s):  
Pattern Matching ◽  
Ground Penetrating Radar ◽  
Electromagnetic Waves ◽  
Complete System ◽  
Near Field ◽  
System Model ◽  
Soil Types ◽  
Broadband Antenna ◽  
Shallow Subsurface ◽  
Ground Penetrating

<p>The WISDOM ground-penetrating radar aboard the 2022 ESA-Roscosmos Rosalind-Franklin ExoMars Rover will probe the shallow subsurface of Oxia Planum using electromagnetic waves. A dual-polarized broadband antenna assembly transmits the WISDOM signal into the Martian subsurface and receives the return signal. This antenna assembly has been extensively tested and characterized w.r.t. the most significant antenna parameters (gain, pattern, matching). However, during the design phase, these parameters were simulated or measured without the environment, i.e., in the absence of other objects like brackets, rover vehicle, or soil. Some measurements of the rover's influence on the WISDOM data were performed during the instrument's integration.</p><p>It was shown that the rover structure and close surroundings in the near-field region of the WISDOM antenna assembly have a significant impact on the WISDOM signal and sounding performance. Hence, it is essential to include the simulations' environment, especially with varying surface and underground.</p><p>With this contribution, we outline the influences of rover and ground on the antenna's pattern and particularly on the footprint. We employ a 3D field solver with a complete system model above different soil types, i.e., subsurface materials with various combinations of permittivity and conductivity.</p>

scholarly journals Particle Center Supported Plane for Subsurface Target Classification based on Full Polarimetric Ground Penetrating Radar

2019 ◽  
Vol 11 (4) ◽  
pp. 405
Author(s):  
Xuan Feng ◽  
Haoqiu Zhou ◽  
Cai Liu ◽  
Yan Zhang ◽  
Wenjing Liang ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Decomposition Methods ◽  
Support Vector ◽  
Classification Methods ◽  
Target Classification ◽  
Data Set ◽  
Particle Center ◽  
Ground Penetrating ◽  
New Criterion

The subsurface target classification of ground penetrating radar (GPR) is a popular topic in the field of geophysics. Among the existing classification methods, geometrical features and polarimetric attributes of targets are primarily used. As polarimetric attributes contain more information of targets, polarimetric decomposition methods, such as H-Alpha decomposition, have been developed for target classification of GPR in recent years. However, the classification template used in H-Alpha classification is preset depending on the experience of synthetic aperture radar (SAR); therefore, it may not be suitable for GPR. Moreover, many existing classification methods require excessive human operation, particularly when outliers exist in the sample (the data set containing the features of targets); therefore, they are not efficient or intelligent. We herein propose a new machine learning method based on sample centers, i.e., particle center supported plane (PCSP). The sample center is defined as the point with the smallest sum of distances from all points in the same sample, which is considered as a better representation of the sample without significant effect of the outliers. In this proposed method, particle swarm optimization (PSO) is performed to obtain the sample centers; the new criterion for subsurface target classification is achieved. We applied this algorithm to full polarimetric GPR data measured in the laboratory and outdoors. The results indicate that, comparing with support vector machine (SVM) and classical H-Alpha classification, this new method is more efficient and the accuracy is relatively high.

Buried nonmetallic object detection using bistatic ground penetrating radar with variable antenna elevation angle and height

2017 ◽  
Author(s):  
Yu Zhang ◽  
Dan Orfeo ◽  
Dylan Burns ◽  
Jonathan Miller ◽  
Dryver Huston ◽  
...  
Keyword(s):  
Object Detection ◽  
Ground Penetrating Radar ◽  
Elevation Angle ◽  
Ground Penetrating

Metal object detection using a forward-looking polarimetric ground penetrating radar

2011 ◽  
Author(s):  
Ethan H.-Y. Chun ◽  
Cornell S. L. Chun
Keyword(s):  
Object Detection ◽  
Ground Penetrating Radar ◽  
Metal Object ◽  
Ground Penetrating ◽  
Forward Looking

scholarly journals Object Detection of Ground-Penetrating Radar Signals Using Empirical Mode Decomposition and Dynamic Time Warping

Forests ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 230 ◽  
Author(s):  
Xi Wu ◽  
Christopher Adam Senalik ◽  
James Wacker ◽  
Xiping Wang ◽  
Guanghui Li
Keyword(s):  
Object Detection ◽  
Empirical Mode Decomposition ◽  
Ground Penetrating Radar ◽  
Dynamic Time Warping ◽  
Dielectric Constants ◽  
Time Warping ◽  
Feature Identification ◽  
Mode Decomposition ◽  
Dynamic Time ◽  
Ground Penetrating

An object detection method of ground-penetrating radar (GPR) signals using empirical mode decomposition (EMD) and dynamic time warping (DTW) is proposed in this study. Two groups of timber specimens were examined. The first group comprised of Douglas fir (Pseudotsuga menziesii) timber sections prepared in the laboratory with inserts of known internal characteristics. The second group comprised of timber girders salvaged from the timber bridges on historic Route 66 over 80 years. A GSSI Subsurface Interface Radar (SIR) System 4000 with a 2 GHz palm antenna was used to scan these two groups of specimens. GPR sensed differences in dielectric constants (DC) along the scan path caused by the presence of water, metal, or air within the wood. This study focuses on the feature identification and defect classification. The results show that the processing methods were efficient for the illustration of GPR information.

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