Optimal Linear Filtering For Weak Target Detection in Radio Frequency Tomography
Date of Award
Ph.D. in Engineering
Department of Electrical and Computer Engineering
Advisor: Michael C. Wicks
The goal of this research was to develop an algorithm to process measured data using an ``optimal'' geometry in order to reconstruct an image of weak targets when strong scatterers are present. This thesis is focused on Weak Target Detection and Imaging in Radio Frequency Tomography (RFT) for Ground Penetrating Radar (GPR). We first start by simplifying the solution of the inversion problem in Radio Frequency Tomography (RFT) based Ground Penetrating Radar (GPR) in order to minimize the compute time for image reconstruction of shallow buried objects. A further goal is to increase the resolution and sharpen the quality of the reconstructed image as we concentrate on weak target detection and imaging in RFT. We propose an accurate, fast method to reconstruct the image of below ground targets using an optimal linear filter, such as matched filter processing. Moreover, a novel method is proposed to improve RFT to detect and imaging weak targets surrounded by strong scatterers in measurement domain. The match filter is the most common approach used to simplify the solution of the inversion problem in GPR model. The proposed method increases the signal-to-noise ratio (SNR) to enhance the quality of the image. Using this technique leads to a decrease in the reconstruction time. Also, it reduces the data acquisition time which is critical in many commercial GPR applications. Compared with other inversion algorithms such as truncated singular value decomposition (TSVD), matched filter algorithms yield a high quality 2D image of shallow buried objects with minimal computational load or noise effect.However, in case of Multi-Target Detection (MTD), RFT has an inherent weakness, especially when strong interfering scatterers and weak targets are present in the same measurement domain. Strong sidelobes from dominant (strong) scatterers can interfere with the echoes from weak targets, thereby leading to missed target detections, and a decrease in the quality of the reconstructed image. Moreover, these sidelobes form electromagnetic fields in the measurement domain with characteristic patterns centered on strong scatterer. These electromagnetic fields decrease the quality of the reconstructed image and can mask weak targets close to the strong targets in the region of interest. In this work, we proposed a faster, more flexible and more accurate technique to decrease the effect of strong sidelobes on weak targets by modeling the strong scatterers as ``pseudo'' transmitters, using the information from the Dyadic Contrast Function (DCF) to represent the scene under investigation. DCF is analyzed in order to remove the effect of these strong sidelobes.This approach uses a ``Suppression Algorithm (SA)'' to remove the effect of strong sidelobes on weak targets, and to reconstruct images using DCF analysis. The proposed algorithm starts by detecting the dominant scatterers in the reconstructed image domain. Then, each dominant cell in the reconstructed image represented as a DCF using its eigenvalues and eigenvectors to model the strong cell as a pseudo or secondary source. The electromagnetic characteristics, magnitude and phase, of the new source (transmitters) are estimated from the eigenvalues and eigenvectors for each cell in the investigation domain. The magnitude of the largest eigenvalue from the DCF will estimate the magnitude of the pseudo transmitter, while the eigenvector provides information about the orientation of the source. Preforming the eigen-decomposition of the DCF improves the process of locating, imaging, and identifying shallow weak objects. Furthermore, to simplify the inversion problem in the complete forward model, Iterative Reconstruction Algorithms (IRT), such as, Subsurface Multiplicative Algebraic Reconstruction Techniques (SMART) are considered as Row Action algorithms (RA-algorithms), providing faster, more flexible and better accuracy for solving the set of linear systems with non-square matrices and ill- conditioned linear operators. The SMART suppression algorithm offers an approach by modeling the strong scatterers as ``extra'' or pseudo dipoles (transmitters) in the measurement domain. The sidelobes of these dipoles are subtracted from the image to sharpen the quality of the weak target return. The presented algorithm has been verified using simulated RFT data, generated by the computational electromagnetic software FEKO, for regular and irregular targets scenarios. Our research shows, that using information from the DCF, it is possible to obtain high quality imagery of buried weak targets in RFT.
Engineering, Electromagnetics, Electrical Engineering, Radio Frequency Tomography, Ground Penetrating Radar, Weak Target, Strong Target, Dyadic Contrast Function, SMART Algorithm, Suppression Algorithm
Copyright 2020, author
Akroush, Muftah Emhemed, "Optimal Linear Filtering For Weak Target Detection in Radio Frequency Tomography" (2020). Graduate Theses and Dissertations. 6837.