SBIR/STTR Research Award
LCS Radar Modeling for Training (LRMT)
Award amount
$1,000,000Program and phase
STTR · Phase IIAward year
2016Award details
- Awarding agency
- Department of Defense
- Branch
- Navy
- Recipient company
- Intelligent Automation, Inc.
- Proposal award date
- 2016-05-10
- Contract end date
- 2018-05-10
- UEI
- C3QJD7VXG167
- Solicitation number
- 2014.0
- Topic code
- N14A-T012
Research abstract
We propose the design and development of LCS radar modeling for training a radar modeling engine that capture the effects of environment, weather, jamming/interference and operator actions on radar display. The purpose of this engine is to reduce or eliminate the need for live training by faithfully capturing the scenarios encountered by a radar operator. The primary target radars for the proposed effort are LCS radars, including TRS-3D and SEA GIRAFFE.
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.
- Fast Accurate Safe Technology for Jack to Jig (FastJack)
- VMS: Deep Reinforcement Learning for High-fidelity Vehicle Motion Simulation
- VAST-CQA: Video Annotation and Statistics Toolkit for Crowdsourcing Quality Assessment
- SALUTEM: System for Activity Classification and Occupational Exposure Monitoring
- A Multiphysics Analysis Tool for Autoclave Processing of Composite Parts (MATAPro)
Awards with the same topic code
Verified by an exact topic code match. This is not a text-similarity match.
- LCS Radar Modeling for Training (LRMT)
- Modeling the Environment to Train Radar Operators for Littoral Combat Ships (METRO-LCS)
Research funding history
1,182 SBIR/STTR records share this UEI (1987–2021). Reported award amounts total $369,972,257. Reported amounts are not federal obligations.
Phases: Phase I, Phase II, Phase II – Direct to Phase II
Agencies: Department of Defense, National Aeronautics and Space Administration, Department of Transportation, Department of Health and Human Services, National Science Foundation, Department of Homeland Security