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Proceedings Paper

Multispectral signal processing of synthetic aperture acoustics for side attack explosive ballistic detection
Author(s): Bryce Murray; Derek T. Anderson; Robert H. Luke; Kathryn Williams
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Paper Abstract

Substantial interest resides in identifying sensors, algorithms and fusion theories to detect explosive hazards. This is a significant research effort because it impacts the safety and lives of civilians and soldiers alike. However, a challenging aspect of this field is we are not in conflict with the threats (objects) per se. Instead, we are dealing with people and their changing strategies and preferred method of delivery. Herein, we investigate one method of threat delivery, side attack explosive ballistics (SAEB). In particular, we explore a vehicle-mounted synthetic aperture acoustic (SAA) platform. First, a wide band SAA signal is decomposed into a higher spectral resolution signal. Next, different multi/hyperspectral signal processing techniques are explored for manual band analysis and selection. Last, a convolutional neural network (CNN) is used for filter learning and classification relative to the full signal versus different subbands. Performance is assessed in the context of receiver operating characteristic (ROC) curves on data from a U.S. Army test site that contains multiple target and clutter types, levels of concealment and times of day. Preliminary results indicate that a machine learned CNN solution can achieve better performance than our previously established human engineered Fourier-based Fraz feature with kernel support vector machine classification.

Paper Details

Date Published: 3 May 2017
PDF: 13 pages
Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 101821E (3 May 2017); doi: 10.1117/12.2262651
Show Author Affiliations
Bryce Murray, Mississippi State Univ. (United States)
Derek T. Anderson, Mississippi State Univ. (United States)
Robert H. Luke, U.S. Army Night Vision & Electronic Sensors Directorate (United States)
Kathryn Williams, U.S. Army Night Vision & Electronic Sensors Directorate (United States)

Published in SPIE Proceedings Vol. 10182:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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