Skip to Main Content
 

Global Search Box

 
 
 

ETD Abstract Container

Abstract Header

Optimal Sensor Geometries for Tomographic Below Ground Imaging

Daluom, Abdulhakim A. M.
http://orcid.org/0000-0001-8637-8036
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1543505143363275

Abstract Details

2018, Doctor of Philosophy (Ph.D.), University of Dayton, Electrical and Computer Engineering.
This dissertation addresses the optimality of sensor configurations in Ground Penetrating Radar (GPR), for techniques currently under development to image buried targets over relatively small areas. We have developed techniques to compute the optimal sensor geometry for a given bi- and multi-static radar. The Transmitter (Tx) and Receiver (Rx) are assumed to be operating in the microwave frequency band. In this work, we are solving the sensor configurations problem in two ways; using single bistatic radar and multiple bistatic radar. First, one transmitter antenna and one receiver antenna for each bistatic radar geometry are investigated. The transmitter moves, following N locations, and these locations can be arranged depending on the proposed geometries. In addition, the receiver follows M movements or locations that can also be arranged in the same way as the transmitter. Second, the use of N transmitters that are rigidly arranged, and M receivers that are also arranged in the same way is considered. These N and M antennas transmit and receive signals, respectively, at the same time. The transmitters are deployed above ground, while all receivers occupy the space between the surface of the ground and the plane of transmitters. The transmitter radiates a unique waveform at each location, and the receiver processes returns from multiple scattering centers. Based on Maxwell's equations and the use of Green's functions, Radio Frequency (RF) tomography is developed to implement an algorithm for imaging underground targets. The targets are assumed to be inside a Region Of Interest (ROI), and we try to obtain results characterizing the difference between two target positions (horizontal and slanted by 30 degree). The design includes different Tx and Rx geometries, such as concentric circles, octagons, squares, and triangles. Based on these different geometries, we determine which distribution is best for imaging shallow buried targets. Also, based on which of the proposed geometries is the best to image below ground targets, we use this geometry to update the GPR system using Image Segmentation Techniques (IST) to detect and track a stationary or even moving spherical object in pipes underground by scanning the ROI in various interval. During each scan, this GPR system detects and calculates the exact location of the target. In the image segmentation, we use the threshold technique method to localize the object by analyzing the image pixels. Then, the distance and the speed of the object are computed automatically during each scan using mathematical methods. A 3D Electromagnetic (EM) software tool is used to model and simulate the GPR system. Simulation results reveal a varying performance in the absence of significant unknown disturbances. Index Terms - Ground Penetrating Radar, Radio Frequency Tomography, Maxwell's Equations, Green's function, Below Ground Imaging, dry and saturated sand, buried objects, and the Image Thresholding Algorithm.
Michael Wicks, Dr (Committee Chair)
John Loomis, Dr (Committee Member)
125 p.
Electrical Engineering

Recommended Citations

Citations

  • Daluom, A. A. M. (2018). Optimal Sensor Geometries for Tomographic Below Ground Imaging [Doctoral dissertation, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1543505143363275

    APA Style (7th edition)

  • Daluom, Abdulhakim. Optimal Sensor Geometries for Tomographic Below Ground Imaging. 2018. University of Dayton, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1543505143363275.

    MLA Style (8th edition)

  • Daluom, Abdulhakim. "Optimal Sensor Geometries for Tomographic Below Ground Imaging." Doctoral dissertation, University of Dayton, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1543505143363275

    Chicago Manual of Style (17th edition)

dayton1543505143363275
119
© 2018, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.