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Proceedings Paper

Voxel-space radar signal processing for side attack explosive ballistic detection
Author(s): Josh Dowdy; Blake Brockner; Derek T. Anderson; Kathryn Williams; Robert H. Luke; David Sheen
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Paper Abstract

Explosive hazards in current and former conflict zones are a serious threat to both civilians and soldiers alike. Significant effort has been dedicated to identifying sensors, algorithms and fusion strategies to detect such threats. However, a challenging aspect of the field is that we are not necessarily at war with the threats (objects). Instead, we are at conflict with people who are constantly evolving their strategies of attack along with their preferred threat. One such method of threat delivery is side attack explosive ballistics (SAEB). In this article, we explore different 3D voxel-space radar signal processing methods for SAEB detection on a U.S. Army provided vehicle-mounted platform. In particular, we explore the fusion of a matched filter (MF) and size contrast filter (SCF). Clustering is applied to the fused result and heuristics are used to reduce the systems false alarm rate. Performance is assessed in the context of receiver operating characteristic (ROC) curves on data from a U.S. Army test site containing multiple target and clutter types, levels of concealment and times of day.

Paper Details

Date Published: 3 May 2017
PDF: 15 pages
Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 101821H (3 May 2017); doi: 10.1117/12.2262659
Show Author Affiliations
Josh Dowdy, Mississippi State Univ. (United States)
Blake Brockner, Mississippi State Univ. (United States)
Derek T. Anderson, Mississippi State Univ. (United States)
Kathryn Williams, U.S. Army RDECOM CERDEC NVESD (United States)
Robert H. Luke, U.S. Army RDECOM CERDEC NVESD (United States)
David Sheen, Pacific Northwest National Lab. (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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