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Signal Processing and Machine Learni...
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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.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Signal Processing and Machine Learning for Explosive Hazard Detection Using Synthetic Aperture Acoustic and High Resolution Voxel Radar./
作者:
Dowdy, Joshua L.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:
60 p.
附註:
Source: Masters Abstracts International, Volume: 79-11.
Contained By:
Masters Abstracts International79-11.
標題:
Computer Engineering. -
電子資源:
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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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.
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