TURION SPACE CORP — Department of Defense STTR Phase I: AF21S-TCSO1
TURION SPACE CORP — STTR Phase I award from Department of Defense.
Phase I STTR 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 $249,979. Cross-check similar awards in the same agency and technology tags for going-rate context.
- Topic code AF21S-TCSO1 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $249,979
- Agency
- Department of Defense · Air Force
- Program / Phase
- STTR · Phase I
- Topic
- AF21S-TCSO1
- Solicitation
- X21.S
- NAICS
- —
- Place of performance
- CA
- Period
- 2022-08-09 → 2023-01-10
Description
The Space Force urgently needs to accelerate space-asset tasking in the wake of adversarial threats. The 2021 Russian ASAT test and China’s SJ-21 repositioning of Compass G-2 demonstrate malicious co-orbital ASAT capabilities, currently unmatched by USA capabilities. Algorithms capable of reinforcement learning to enable rapid sensor tasking and re-tasking is essential to ensuring the USSF can continue to operate in a proven contested environment. Today’s sensor tasking operations rely primarily on human-in-the-loop systems to assign tasks to spacecraft. A reinforcement learning algorithm integrated with a model-based simulation of on-orbit assets can accelerate planning for remote rendezvous, proximity, and sensing operations. Steven’s Institute of Technology (first-place winner of the 2021 Hyperspace Challenge) has successfully demonstrated a reinforcement learning algorithm to efficiently allocate ground-based sensor tasks for commercial and DoD use cases. Turion Space Corporation (TSC) proposes a Phase I STTR in collaboration with Steven’s Institute of Technology to adapt and extend this reinforcement learning approach with a predictive model that optimizes orbital sensor task allocation to reduce resource usage and extend asset lifetime.