KEF ROBOTICS INC — Department of Defense SBIR Phase I: CBD212-001
KEF ROBOTICS INC — SBIR Phase I award from Department of Defense.
Phase I SBIR feasibility signal
- Phase I awards fund proof-of-concept work. For capture teams, they mark early interest from Department of Defense in a technical approach.
- Watch for Phase II follow-ons from the same firm/topic family — that conversion path is where budgets and transition pressure rise.
- Obligated amount $167,489. Cross-check similar awards in the same agency and technology tags for going-rate context.
- Topic code CBD212-001 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $167,489
- Agency
- Department of Defense · Office for Chemical and Biological Defense
- Program / Phase
- SBIR · Phase I
- Topic
- CBD212-001
- Solicitation
- 21.2
- NAICS
- —
- Place of performance
- PA
- Period
- 2022-03-18 → 2022-09-21
Description
Concealed chemical threats represent a significant hazard to field operators. Even brief exposure to chemicals can cause lasting physical, physiological, and mental damage. Field operators require tools and techniques to more quickly, safely, and reliably locate and identify chemical threats. Chemical detectors typically excel at either threat classification or threat localization, but seldom both. Conventional sensor fusion techniques through probabilistic inference or fuzzy logic can be used, but these techniques do not exploit low-level features and data coupling between sensor modes that can often improve performance. Learning-based sensor fusion solutions using deep neural networks (DNNs) can surpass the best performance of each sensor individually, as demonstrated in recent work with multi-spectral pedestrian detection or audio-visual sound source localization1,2. In this Phase 1, we will implement an infrared imaging and ion-mobility spectrometer (IMS) multi-modal DNN to perform agent classification and localization and compare its performance to single mode DNNs. In Phase 2 we propose to improve and mature the DL architecture to multiple agents and deploy the solution to a government-built UAS to perform an autonomous CBRNE surveillance technical demonstration Concealed chemical threats represent a significant hazard to field operators. Even brief exposure to chemicals can cause lasting physical, physiological, and mental damage. Field operators require tools and techniques to more quickly, safely, and reliably locate and identify chemical threats. Chemical detectors typically excel at either threat classification or threat localization, but seldom both. Conventional sensor fusion techniques through probabilistic inference or fuzzy logic can be used, but these techniques do not exploit low-level features and data coupling between sensor modes that can often improve performance. Learning-based sensor fusion solutions using deep neural networks (DNNs) can surpass the best performance of each sensor individually, as demonstrated in recent work with multi-spectral pedestrian detection or audio-visual sound source localization1,2. In this Phase 1, we will implement an infrared imaging and ion-mobility spectrometer (IMS) multi-modal DNN to perform agent classification and localization and compare its performance to single mode DNNs. In Phase 2 we propose to improve and mature the DL architecture to multiple agents and deploy the solution to a government-built UAS to perform an autonomous CBRNE surveillance technical demonstration.