BRIMROSE TECHNOLOGY CORP — Department of Defense STTR Phase II: NASA T8-01
BRIMROSE TECHNOLOGY CORP — STTR Phase II award from Department of Defense.
Phase II STTR prototype / development signal
- Phase II is where Department of Defense funds deeper R&D after feasibility. Incumbents with Phase II history are serious competitors on adjacent topics.
- Use this award as past-performance context and to map customer organizations for STRATFI/TACFI-style transition planning.
- Obligated amount $1,315,112 is consistent with substantial Phase II-scale effort; compare to related awards from the same agency.
- Topic code NASA T8-01 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $1,315,112
- Agency
- Department of Defense · Army
- Program / Phase
- STTR · Phase II
- Topic
- NASA T8-01
- NAICS
- —
- Place of performance
- MD
- Period
- 2023-09-05 → 2025-09-04
Description
Optical detection of objects hidden behind opaque layers is a challenging problem. As such, Person-Borne Improvised Explosive Devices (PB-IEDs) underneath clothing continue to be a persistent threat to the military and law enforcement communities. Safe stand-off detection in crowds is needed for the confirmation, identification, and neutralization of PB-IEDs. Technologies previously investigated include terahertz, millimeter wave, x-ray, radar, and LWIR, however several factors, such as performance, cost, lack of covertness, have prevented wide scale implementation of these technologies. Recent studies on the imaging spectroscopic measurement of cloth fabrics with visible and SWIR Hyperspectral Imaging (HSI) instrument suggests that this spectral region for PB-IED detection presents its potential for detecting object under fabric materials. The proposed approach to counter PB-IED is based on Acousto-Optic Tunable Filter (AOTF) and Liquid Crystal Variable Retarder (LCVR)-based Polarimetric HSI (PHSI), to detect, identify, and visualize hidden objects under the cloth with analysis of PHSI data with deep learning algorithms in real time.