Dissertation/Thesis Abstract

Development of Unexploded Ordnances (UXO) Detection and Classification System Using Ultra Wide Bandwidth Fully Polarimetric Ground Penetrating Radar (GPR)
by Youn, Hyoung-Sun, Ph.D., The Ohio State University, 2007, 196; 10835994
Abstract (Summary)

A novel Unexplored Ordnance (UXO) classification method has been developed using an Ultra-wide bandwidth fully polarimetric Ground Penetrating Radar (GPR) system. The novel GPR system inherited benefits of OSU-ESL fully polarimetric GPR system and reformed in the limitations that were found during the previous study. The research of the novel UXO classification technique has been conducted in four main aspects, such as radar system, GRP antenna, data processing, and classification algorithm. In radar system aspect, a new faster GPR system has been developed to achieve fast measurement speed, smaller size and cheap cost using a digital down converter (DDC) and direct digital frequency synthesis (DDS) technology.

In antenna aspect, a new dual-polarization dielectric loaded horn antenna for UXO detection/classification are developed to have a small footprint, high spatial resolution and better mobility, compared with the conventional OSU/ESL GPR antenna. Performance of the new GPR antenna is compared with that of the previous OSU/ESL GPR antenna.

In data processing aspect, two novel antenna calibration methods that utilize surface wire response and surface reflections, respectively, have been developed. Optimal Frequency Band Selection has been developed to improve SCR and SNR in the time domain data by applying an appropriate filter that matches to the target's spectrum. The strong surface clutter can be removed by the newly developed surface clutter remover. Adaptive temporal-spatial domain smoothing has also been developed to reduce interference from clutter sources or nearby objects. Such data processing techniques improve SCR and CNR of data and consequently provide better detection performance.

Finally, in classification algorithm aspect, the OSU/ESL UXO classification rules based on the spatial distribution of extracted features and early time scattering pattern have been developed. The UXO classification performance of these rules was evaluated by blind field tests conducted in several UXO test site. To achieve more objective, robust and quantitative way of executing the UXO classification, an artificial intelligent (AI) system using neural network and fuzzy inference was also developed based on the OSU/ESL UXO classification rules. Classification performance of the AI system is compared with that of human expert and the results are presented.

Indexing (document details)
Advisor: Lee, Robert
School: The Ohio State University
Department: Electrical Engineering
School Location: United States -- Ohio
Source: DAI-B 79/10(E), Dissertation Abstracts International
Subjects: Electrical engineering
Keywords: Bandwidth, Classification, Detection, Gpr, Ground, Ordnances, Penetrating, Polarimetric, Radar, Unexploded, Uxo
Publication Number: 10835994
ISBN: 978-0-355-97179-8
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