OPTIMIZATION TECHNOLOGIES, INC. — Department of Defense SBIR Phase II: MDA21-008
OPTIMIZATION TECHNOLOGIES, INC. — 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 $1,499,705 is consistent with substantial Phase II-scale effort; compare to related awards from the same agency.
- Topic code MDA21-008 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $1,499,705
- Agency
- Department of Defense · Missile Defense Agency
- Program / Phase
- SBIR · Phase II
- Topic
- MDA21-008
- Solicitation
- 21.2
- NAICS
- —
- Place of performance
- CO
- Period
- 2023-02-08 → 2025-02-07
Description
OptTek will apply our novel mapping, adaptive sampling, and optimization capabilities to help MDA better understand the operational effectiveness of the MDS under multi-threat missile raids. While digital simulations provide faster feedback on missile defense performance than a ground test, the computational costs are still too high to handle the combinatorial explosion of parameter combinations and resultant simulations that are needed to study a raid through enumerating possibilities. Knowing the defense landscape more fully will allow leadership to assess potential concerns in the system and what new technology needs there are in both hardware, software, and tacticsOptTek’s proven approach to the optimization of complex systems, which includes advanced artificial intelligence and machine learning (AI/ML) techniques like metaheuristics, mathematical programming, neural networks, and novel spatial correlated interpolation algorithms, will help an analyst understand the design space of parameters associated with the MDS for defense against multi-threat missile raid scenarios. We will leverage our novel data fusion techniques to combine results from medium-fidelity digital simulations such as AFSIM to help guide the analyst to make efficient use of high-fidelity simulations for systems like EDISS/EDP. By sampling the study space efficiently, reporting meaningful metrics to the analyst, and accurately mapping the results of the simulations, we will extract more useful information from many, many fewer simulation runs than a manual analysis of the space would allow. Approved for Public Release | 22-MDA-11340 (16 Dec 22)