Theses and Dissertations
Issuing Body
Mississippi State University
Advisor
Ball, John E.
Committee Member
Anderson, Derek T.
Committee Member
Younan, Nicolas H.
Date of Degree
5-4-2018
Document Type
Graduate Thesis - Open Access
Major
Electrical and Computer Engineering
Degree Name
Master of Science
College
James Worth Bagley College of Engineering
Department
Department of Electrical and Computer Engineering
Abstract
Different signal processing techniques for synthetic aperture acoustic (SAA) and highresolution voxel radar (HRVR) sensing modalities for side-attack explosive ballistic (SAEB) detection are proposed in this thesis. The sensing modalities were vehicle mounted and the data used was collected at an army test site. More specifically, the use of a frequency azimuthal (fraz) feature for SAA and the fusion of a matched filter (MF) and size contrast filter (SCF) for HRVR was explored. For SAA, the focus was to find a signature in the target’s response that would vary as the vehicle’s view on the target changed. For the HRVR, the focus was put on finding objects that were both anomalous (SCF) and target-like (MF). The results in both cases are obtained using receiver operating characteristic (ROC) curves and both are very encouraging.
URI
https://hdl.handle.net/11668/20946
Recommended Citation
Dowdy, Joshua L., "Signal Processing and Machine Learning for Explosive Hazard Detection using Synthetic Aperture Acoustic and High Resolution Voxel Radar" (2018). Theses and Dissertations. 3943.
https://scholarsjunction.msstate.edu/td/3943
Comments
Explosive hazard detection||size contrast filter||matched filter||high-resolution voxel radar||side attack explosive ballistic||Fraz||synthetic aperture acoustic