Detecting explosive hazards in 3D radar imaging through slice based feature extraction and sequential learning
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[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI SYSTEM AT AUTHOR'S REQUEST.] This thesis provides the history and framework of detecting explosive hazards from three-dimensional radar by extracting features through a slice-based perspective and using a sequential classifier. This problem rises from the concerns of saving the lives of many brave men and women who serve this country while in areas of hostility. One of the deadliest problems faced by service men and women are side attacks from explosive hazards while patrolling areas or moving with a convoy. Many advances have been made in upgrading vehicle armor and design to lessen the impact of the explosives and protect those within the vehicles. This thesis specifically looks at the advancements made within radar systems that can create a three-dimensional space in which the vehicle is traveling. The goal of the research within this thesis is to detect explosive hazards proactively and prevent any harm to the operators and passengers within the vehicle. For this a Hidden Markov Model (HMM) has been developed and tested experimentally.
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