SBIR/STTR Research Award
CT-ID: Contextual Threat Identification
Award amount
$149,998Program and phase
SBIR · Phase IAward year
2022Award details
- Awarding agency
- Department of Defense
- Branch
- Missile Defense Agency
- Recipient company
- CLOSTRA INC
- Proposal award date
- 2021-12-06
- Contract end date
- 2022-06-05
- UEI
- G5CTKNN62KG5
- Solicitation number
- 21.2
- Topic code
- MDA21-019
Research abstract
Traditional deep learning (DL) classifiers often ignore contextual data, limiting complex threat scenario analysis. A new approach, incorporating a wide range of contextual data, is needed to improve object of interest classification, tracking, and targeting. Clostra’s CT-ID (Contextual Threat Identification) will incorporate multi-sensor, time-series, and high-level contextual data, improving threat classification accuracy and allowing salvo firing control to confidently target high-priority objects. Approved for Public Release | 21-MDA-11013 (19 Nov 21)
Technology keywords
Not reported
Other awards by this recipient
Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.
Awards with the same topic code
Verified by an exact topic code match. This is not a text-similarity match.
- Contextual Reasoning for Threat Classification Refinement
- DB&DL: Threat Classification and Reasoning by Dynamic Bayesian Network, Description Logic, and Intuitionistic Fuzzy Sets
- CONSTABLE Contextual Reasoning System for Threat Classification
- Contextual Reasoning for Threat Classification Refinement
- Contextual Reasoning for Threat Classification Refinement
Research funding history
24 SBIR/STTR records share this UEI (2017–2024). Reported award amounts total $14,218,335. Reported amounts are not federal obligations.
Phases: Phase I, Phase II, Phase II – Direct to Phase II
Agencies: Department of Defense, Department of Energy