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Standoff technologies for the detection of Explosively Formed Penetrators (EFPs)

Award Information
Agency: Department of Defense
Branch: Army
Contract: W909MY-14-C-0016
Agency Tracking Number: A141-039-0577
Amount: $99,950.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: A14-039
Solicitation Number: 2014.1
Solicitation Year: 2014
Award Year: 2014
Award Start Date (Proposal Award Date): 2014-06-24
Award End Date (Contract End Date): 2015-04-24
Small Business Information
1242 Chestnut Street
Newton, MA 02464
United States
DUNS: 000000000
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Joe Keranen
 Senior Research Scientist
 (802) 683-9169
Business Contact
 Edmund Reiter
Title: President
Phone: (617) 851-6152
Research Institution

White River Technologies, Inc. (WRT), in collaboration with User Systems Inc. (USI), propose Advanced Features for Forward-Looking EFP Detection to addresses the need within the US Army for advanced detection and feature extraction algorithms to improve standoff detection of emplaced roadside Explosively Formed Penetrators (EFPs) from forward-looking sensors. The proposed research will advance capabilities in roadside EFP detection and discrimination by exploiting advanced radar processing and model-based and statistical features extracted from forward-looking vehicle-mounted sensors. EFP improvised explosive devices are armor piercing shape charges typically constructed from open steel cylinders, explosive, and a concave metal end that, when detonated, becomes a large, high-speed directional projectile. Current EFP detection methods are plagued by high false alarm rates due to the heterogeneous background environment containing anthropogenic clutter and vegetation. Additionally, EFP devices are often concealed during emplacement using foliage and innocuous clutter items. We will investigate development of EFP discrimination features from Forward-Looking Ground Penetrating Radar (FLGPR) and Light Detection and Ranging (LIDAR) sensors using scattering models and statistical measures. We will investigate adaptation of airborne and space-based SAR and GPR processing and feature extraction to FLGPR data and exploitation of advances in ground-based mobile LIDAR technology towards robust EFP classification.

* Information listed above is at the time of submission. *

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