ADVANCED SCIENTIFIC CONCEPTS, LLC — Department of Defense SBIR Phase II: AF182-071
ADVANCED SCIENTIFIC CONCEPTS, LLC — SBIR Phase II award from Department of Defense.
Phase II SBIR 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 $749,994 is consistent with substantial Phase II-scale effort; compare to related awards from the same agency.
- Topic code AF182-071 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $749,994
- Agency
- Department of Defense · Air Force
- Program / Phase
- SBIR · Phase II
- Topic
- AF182-071
- Solicitation
- 18.2
- NAICS
- —
- Place of performance
- CA
- Period
- 2021-05-19 → 2023-05-19
Description
ASC is proposing design enhancements to the 3D GSF LiDAR to expand the space based capabilities by addressing new technologies to better support space situational awareness to protect High Value Assets. These new technologies also support CubeSat tracking, and Rendezvous Proximity Operations (RPO). The new technologies are currently at a low TRL level for space applications. ASC plans to mature the relatively low TRL subsystems in this phase II by demonstrating the capabilities in a relevant environment. To support the new technologies, ASC plans to expand signal processing capabilities embedded within the 3DGSF LiDAR. To accommodate the SP algorithms, additional processing HW will be required. This is accommodated by utilizing a high powered FPGA that can do pre-processing and point cloud manipulation. A Single Board Computer will be included in the circuit card sack to process application specific algorithms. The three new technologies that Phase II addresses are: • High Resolution 3D point cloud derived from real time flash LIDAR and visible sensor image fusion techniques. • Wide Field Of View capability while maintaining low angular resolution by implementing non mechanical beam steering. • Efficient object tracking, classification, and tracking algorithms using Deep Learning Convolutional Neural Networks (DL-CNN).