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.
Josh Dowdy, Blake Brockner, Derek T. Anderson, Kathryn Williams, Robert H. Luke, and David Sheen, "Voxel-space radar signal processing for side attack explosive ballistic detection," Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 101821H (Presented at SPIE Defense + Security: April 12, 2017; Published: 3 May 2017); https://doi.org/10.1117/12.2262659.
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