The design of hardware and signal processing for a stepped frequency continuous wave ground penetrating radar

Includes bibliographical references.A Ground Penetrating Radar (GPR) sensor is required to provide information that will allow the user to detect, classify and identify the target. This is an extremely tough requirement, especially when one considers the limited amount of information provided by most GPRs to accomplish this task. One way of increasing this information is to capture the complete scattering matrix of the received radar waveform. The objective of this thesis is to develop a signal processing technique to extract polarimetric feature vectors from Stepped Frequency Continuous Wave (SFGWV) GPR data. This was achieved by first developing an algorithm to extract the parameters from single polarization SFCW GPR data and then extending this algorithm to extract target features from fully polarimetric data. A model is required to enable the extraction of target parameters from raw radar data. A single polarization SFCW GPR model is developed based on the radar geometry and linear approximations to the wavenumber in a lossy medium. Assuming high operating frequencies and/or low conductive losses, the model is shown to be equivalent to the exponential model found in signal processing theory. A number of algorithms exist to extract the required target parameters from the measured data in a least squared sense. In this thesis the Matrix Pencil-of-Function Method is used. Numerical simulations are presented to show the performance of this algorithm for increasing model error. Simulations are also provided to compare the standard Inverse Discrete Fourier Transform (IDFT) with the algorithm presented in this thesis. The processing is applied to two sets of measured radar data using the radar developed in the thesis. The technique was able to locate the position of the scatterers for both sets of data, thus demonstrating the success of the algorithm on practical measurements. The single polarization model is extended to a fully polarimetric SFCW GPR model. The model is shown to relate to the multi-dimensional exponential signal processing model, given certain assumptions about the target scattering damping factor. The multi-snapshot Matrix Pencil-of-Function Method is used to extract the scattering matrix parameters from the raw polarimetric stepped frequency data. Those Huynen target parameters that are independent of the properties of the medium, are extracted from the estimated scattering matrices. Simulations are performed to examine the performance of the algorithm for increasing conductive and dielectric losses. The algorithm is also applied to measured data for a number of targets buried a few centimeters below the ground surface, with promising results. Finally, the thesis describes the design and development of a low cost, compact and low power SFCW GPR system. It addresses both the philosophy as well as the technology that was used to develop a 200 - 1600 MHz and a 1 - 2 GHz system. The system is built around a dual synthesizer heterodyne architecture with a single intermediate frequency stage and a novel coherent demodulator system - with a single reference source. Comparison of the radar system with a commercial impulse system, shows that the results are of a similar quality. Further measurements demonstrate the radar performance for different field test cases, including the mapping of the bottom of an outdoor test site down to 1.6 m

Enhanced microwave imaging of the subsurface for humanitarian demining applications

© Cranfield University 2020. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright ownerThis thesis presents a theoretical analysis and applied evaluation deploying ground penetrating radar (GPR) for landmine detection. An original contribution has been made in designing and manufacturing a light-weight, low-cost, fully polarimetric antenna system for GPR, enabling easy transportation and assembly. This facilitates extensive use by various smaller communities in remote areas. By achieving the goal of supplying various smaller communities with advanced ground penetrating radar technology the technological standard of landmine detection can be improved beyond existing solutions such as metal detection or manual probing. The novel radar system itself allows detection of various subsurface targets of different shapes and sizes, metallic and non-metallic, in a number of different soils, such as sand, loam or gravel and therefore can be used in versatile environments. The GPR system has been realised by designing novel light-weight, 3D printed X-band horn antennas, manufactured from single piece plastic then copper electroplated. These antennas are 50% lighter than their commercial equivalents. They are incorporated in an antenna array as a group of four to allow full-polarimetric imaging of the subsurface. High resolution images of landmines and calibration targets were performed in the subsurface over an experimental sand test bed. For performing subsurface measurements in the near-field, four novel gradient-index (GRIN) lenses were designed and 3D printed to be incorporated in the apertures of the Xband antennas. The improved target detection from these lenses was proven by scanning the test bed and comparing the imaging data of the antenna array with and without lensesattached. A rigorous theoretical study of different decomposition techniques and their effect on the imaging and detection accuracy for polarimetric surface penetrating data was performed and applied to the gathered imaging data to reliably isolate and detect subsurface targets. Studied decomposition techniques were Pauli decomposition parameters and Yamaguchi polarimetry decomposition. It was found that it is paramount to use both algorithms on one set of subsurface data to detect all features of a buried target. A novel temporal imaging technique was developed for exploiting natural occurring changes in soil moisture level, and hence its dielectric properties. Contrary to the previously introduced imaging techniques this moisture change detection (MCD) mechanism does not rely on knowledge of the used measurement setup or deploying clutter suppression techniques. This time averaged technique uses several images of a moist subsurface taken over a period while the moisture evaporates from the soil. Each image pixel is weighted by the phase change occurring over the evaporation period and a resulting B-scan image reveals the subsurface targets without surrounding clutter. Finally, a multi-static antenna set-up is examined on its capability for suppressing surface clutter and its limitations are verified by introducing artificial surface clutter in form of pebbles to the scene. The resulting technique was found to suppress up to 30 The GPR antenna system developed in this thesis and the corresponding imaging techniques have contributed to a significant improvement in subsurface radar imaging performance and target discrimination capabilities. This work will contribute to more efficient landmine clearance in some of the most challenged parts of the world

Enhanced Microwave Imaging of the Subsurface for Humanitarian Demining Applications

© Cranfield University 2020. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright ownerThis thesis presents a theoretical analysis and applied evaluation deploying ground penetrat ing radar (GPR) for landmine detection. An original contribution has been made in designing and manufacturing a light-weight, low-cost, fully polarimetric antenna system for GPR, enabling easy transportation and as sembly. This facilitates extensive use by various smaller communities in remote areas. By achieving the goal of supplying various smaller communities with advanced ground pene trating radar technology the technological standard of landmine detection can be improved beyond existing solutions such as metal detection or manual probing. The novel radar system itself allows detection of various subsurface targets of different shapes and sizes, metallic and non-metallic, in a number of different soils, such as sand, loam or gravel and therefore can be used in versatile environments. The GPR system has been realised by designing novel light-weight, 3D printed X-band horn antennas, manufactured from single piece plastic then copper electroplated. These an tennas are 50% lighter than their commercial equivalents. They are incorporated in an an tenna array as a group of four to allow full-polarimetric imaging of the subsurface. High resolution images of landmines and calibration targets were performed in the subsurface over an experimental sand test bed. For performing subsurface measurements in the near-field, four novel gradient-index (GRIN) lenses were designed and 3D printed to be incorporated in the apertures of the X band antennas. The improved target detection from these lenses was proven by scanning the test bed and comparing the imaging data of the antenna array with and without lenses attached. A rigorous theoretical study of different decomposition techniques and their effect on the imaging and detection accuracy for polarimetric surface penetrating data was performed and applied to the gathered imaging data to reliably isolate and detect subsurface targets. Studied decomposition techniques were Pauli decomposition parameters and Yamaguchi polarime try decomposition. It was found that it is paramount to use both algorithms on one set of subsurface data to detect all features of a buried target. A novel temporal imaging technique was developed for exploiting natural occurring changes in soil moisture level, and hence its dielectric properties. Contrary to the previously intro duced imaging techniques this moisture change detection (MCD) mechanism does not rely on knowledge of the used measurement setup or deploying clutter suppression techniques. This time averaged technique uses several images of a moist subsurface taken over a period while the moisture evaporates from the soil. Each image pixel is weighted by the phase change occurring over the evaporation period and a resulting B-scan image reveals the subsurface targets without surrounding clutter. Finally, a multi-static antenna set-up is examined on its capability for suppressing sur face clutter and its limitations are verified by introducing artificial surface clutter in form of pebbles to the scene. The resulting technique was found to suppress up to 30 The GPR antenna system developed in this thesis and the corresponding imaging tech niques have contributed to a significant improvement in subsurface radar imaging perfor mance and target discrimination capabilities. This work will contribute to more efficient landmine clearance in some of the most challenged parts of the world.Ph

Scattering Characteristic Extraction Method for Manmade Target Based on Target Null Theory

Scattering characteristic extraction is an essential part of manmade target recognition. However, if two scattering points are in adjacent pixels, scattering characteristic extraction may fail to acquire accurate polarimetric scattering matrices (PSMs) of the weak scattering points due to the contamination caused by the strong scattering points. Target null theory provides a way to solve this problem. By selecting the transmitting and receiving polarization states of radar antennas simultaneously, the echo power of a strong scattering point becomes zero and the contamination effect is avoided. In this paper, a method based on target null theory for scattering characteristic extraction is proposed. First, we optimize the transmitting and receiving polarization states of the radar antenna to suppress the intensities of the strong scattering points to highlight the positions of the weak scattering points in certain polarimetric channels. Second, to suppress the contamination effects of strong scattering points in other polarimetric channels, we establish perturbation correction equations to erase the error generated by the point spread function (PSF) among adjacent scattering points in the radar image. Finally, the solved polarimetric scattering matrices of corresponding positions are implemented for target retrieval. The electromagnetic simulation results demonstrate the effectiveness of the proposed method

Ground‐Penetrating Radar for Close‐in Mine Detection

In this chapter, two of the major challenges in the application of ground‐penetrating radar in humanitarian demining operations are addressed: (i) development and testing of affordable and practical ground penetrating radar (GPR)‐based systems, which can be used off‐ground and (ii) development of robust signal processing techniques for landmines detection and identification. Different approaches developed at the Royal Military Academy in order to demonstrate the possibility of enhancing close‐range landmine detection and identification using ground‐penetrating radar under laboratory and outdoor conditions are summarized here. Data acquired using different affordable and practical GPR‐based systems are used to validate a number of promising developments in signal processing techniques for target detection and identification. The proposed approaches have been validated with success in laboratory and outdoor conditions and for different scenarios, including antipersonnel, low‐metal content landmines, improvised explosive devices and real mine‐affected soils

Near-Surface Interface Detection for Coal Mining Applications Using Bispectral Features and GPR

The use of ground penetrating radar (GPR) for detecting the presence of near-surface interfaces is a scenario of special interest to the underground coal mining industry. The problem is difficult to solve in practice because the radar echo from the near-surface interface is often dominated by unwanted components such as antenna crosstalk and ringing, ground-bounce effects, clutter, and severe attenuation. These nuisance components are also highly sensitive to subtle variations in ground conditions, rendering the application of standard signal pre-processing techniques such as background subtraction largely ineffective in the unsupervised case. As a solution to this detection problem, we develop a novel pattern recognition-based algorithm which utilizes a neural network to classify features derived from the bispectrum of 1D early time radar data. The binary classifier is used to decide between two key cases, namely whether an interface is within, for example, 5 cm of the surface or not. This go/no-go detection capability is highly valuable for underground coal mining operations, such as longwall mining, where the need to leave a remnant coal section is essential for geological stability. The classifier was trained and tested using real GPR data with ground truth measurements. The real data was acquired from a testbed with coal-clay, coal-shale and shale-clay interfaces, which represents a test mine site. We show that, unlike traditional second order correlation based methods such as matched filtering which can fail even in known conditions, the new method reliably allows the detection of interfaces using GPR to be applied in the near-surface region. In this work, we are not addressing the problem of depth estimation, rather confining ourselves to detecting an interface within a particular depth range

Three-Dimensional Electromagnetic Scattering from Layered Media with Rough Interfaces for Subsurface Radar Remote Sensing

The objective of this dissertation is to develop forward scattering models for active microwave remote sensing of natural features represented by layered media with rough interfaces. In particular, soil profiles are considered, for which a model of electromagnetic scattering from multilayer rough surfaces with/without buried random media is constructed. Starting from a single rough surface, radar scattering is modeled using the stabilized extended boundary condition method (SEBCM). This method solves the long-standing instability issue of the classical EBCM, and gives three-dimensional full wave solutions over large ranges of surface roughnesses with higher computational e±ciency than pure numerical solutions, e.g., method of moments (MoM). Based on this single surface solution, multilayer rough surface scattering is modeled using the scattering matrix approach and the model is used for a comprehensive sensitivity analysis of the total ground scattering as a function of layer separation, subsurface statistics, and sublayer dielectric properties. The buried inhomogeneities such as rocks and vegetation roots are considered for the first time in the forward scattering model. Radar scattering from buried random media is modeled by the aggregate transition matrix using either the recursive transition matrix approach for spherical or short-length cylindrical scatterers, or the generalized iterative extended boundary condition method we developed for long cylinders or root-like cylindrical clusters. These approaches take the field interactions among scatterers into account with high computational efficiency. The aggregate transition matrix is transformed to a scattering matrix for the full solution to the layered-medium problem. This step is based on the near-to-far field transformation of the numerical plane wave expansion of the spherical harmonics and the multipole expansion of plane waves. This transformation consolidates volume scattering from the buried random medium with the scattering from layered structure in general. Combined with scattering from multilayer rough surfaces, scattering contributions from subsurfaces and vegetation roots can be then simulated. Solutions of both the rough surface scattering and random media scattering are validated numerically, experimentally, or both. The experimental validations have been carried out using a laboratory-based transmit-receive system for scattering from random media and a new bistatic tower-mounted radar system for field-based surface scattering measurements.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91459/1/xduan_1.pd