Reservoir Labs, Inc. — Department of Defense SBIR Phase II: MDA15-008
Reservoir Labs, 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 $1,049,995 is consistent with substantial Phase II-scale effort; compare to related awards from the same agency.
- Topic code MDA15-008 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $1,049,995
- Agency
- Department of Defense · Missile Defense Agency
- Program / Phase
- SBIR · Phase II
- Topic
- MDA15-008
- Solicitation
- 15.2
- NAICS
- —
- Place of performance
- NY
- Period
- 2019-08-13 → 2021-08-12
Description
In this project, Reservoir Labs continues to prototype and refine our Kinematic Invariant Space Maximum Entropy Tracker (KISMET) algorithm. KISMET takes an innovative approach to the problem of orbital track reconstruction and discrimination from radar data. In this Phase II effort, the approach is extended to handle fusion of data from multiple, geometrically diverse sensors, and to work with EO/IR sensors and Doppler radar measurements, in addition to range and angle measurements from radars. We extract key information related to ballistic missile break-up events by working in a state-space of orbital invariants only. The invariants account for the geopotential of the oblate earth to a high order of approximation. KISMET adopts a novel space-time viewpoint, where time is not measured by the counting of measurements, or by seconds on a clock, but by the accumulation of information. This line of thinking leads not to a conventional track filter, but to a set of static constrained optimization problems that we attack using new methods arising from research in the field of convex optimization. During Phase II of this program, we will continue to focus on developing and demonstrating our algorithms on measured data, as well as building an improved software prototype. Approved for Public Release | 19-MDA-9932 (21 Feb 19)