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Signal Processing and Machine Learni...

Dowdy, Joshua L.

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Signal Processing and Machine Learning for Explosive Hazard Detection Using Synthetic Aperture Acoustic and High Resolution Voxel Radar.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Signal Processing and Machine Learning for Explosive Hazard Detection Using Synthetic Aperture Acoustic and High Resolution Voxel Radar./
Author:	Dowdy, Joshua L.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	60 p.
Notes:	Source: Masters Abstracts International, Volume: 79-11.
Contained By:	Masters Abstracts International79-11.
Subject:	Computer Engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10791018
ISBN:	9780355922127

Signal Processing and Machine Learning for Explosive Hazard Detection Using Synthetic Aperture Acoustic and High Resolution Voxel Radar.
Dowdy, Joshua L.

Signal Processing and Machine Learning for Explosive Hazard Detection Using Synthetic Aperture Acoustic and High Resolution Voxel Radar. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 60 p.

Source: Masters Abstracts International, Volume: 79-11.

Thesis (M.S.)--Mississippi State University, 2018.

This item is not available from ProQuest Dissertations & Theses.

Different signal processing techniques for synthetic aperture acoustic (SAA) and high-resolution 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.

ISBN: 9780355922127Subjects--Topical Terms:

1567821
Computer Engineering.

Signal Processing and Machine Learning for Explosive Hazard Detection Using Synthetic Aperture Acoustic and High Resolution Voxel Radar.
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245 1 0 $a Signal Processing and Machine Learning for Explosive Hazard Detection Using Synthetic Aperture Acoustic and High Resolution Voxel Radar.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 60 p.
500 $a Source: Masters Abstracts International, Volume: 79-11.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Ball, John E.
502 $a Thesis (M.S.)--Mississippi State University, 2018.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a Different signal processing techniques for synthetic aperture acoustic (SAA) and high-resolution 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.
590 $a School code: 0132.
650 4 $a Computer Engineering. $3 1567821
690 $a 0464
710 2 $a Mississippi State University. $b Electrical and Computer Engineering. $3 1044030
773 0 $t Masters Abstracts International $g 79-11.
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793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10791018