RDRTEC INCORPORATED — Department of Defense STTR Phase I: N21A-T003
RDRTEC INCORPORATED — STTR Phase I award from Department of Defense.
- Amount
- $139,548
- Agency
- Department of Defense · Navy
- Program / Phase
- STTR · Phase I
- Topic
- N21A-T003
- Solicitation
- 21.A
- NAICS
- —
- Place of performance
- TX
- Period
- 2021-06-23 → 2021-12-28
Description
RDRTec proposes performing feasibility trade studies of combining Electro-Optics/Infrared (EOIR) with active radar into a Detect And Avoid (DAA) system for Non-Cooperative Traffic that jointly enables sufficient performance within the Size Weight and Power and Cooling (SWaPC) of Small Unmanned Aerial Systems (sUAS) such as the RQ-7 Shadow or RQ-21 Blackjack. The hypothesis addressed by this study and potential benefit is that a dual sensor (EOIR and radar) non-cooperative traffic sensor can provide sufficient performance, but with less SWaP-C. The approach is to use the EOIR as the primary search sensor that cues the radar for range and range rate measurements. The study will address different antenna topologies, emerging efficient technologies such as Gallium Nitride (GaN), different waveforms, frequencies, clutter, and false alarm mitigation as well as track correlation and data fusion studies that investigate the impacts of resolutions and false alarms on track correlation and data fusion. The University of Arizona College of Optical Science is our world class University partner whose infrastructure includes the Meinel Optical Sciences Building. We propose to accomplish the following technical objectives in Phase 1: Performing cued radar trade study to support EOIR DAA sensor by providing range and range rate measurements. This includes different antenna topologies, emerging efficient technologies such as Gallium Nitride (GaN), different waveforms, clutter, and false alarm mitigation. Performing EOIR trade study to support DAA functions and requirements through the 2D tracker. This includes frequency trades, emerging technologies, resolution trades, etc. Performing track correlation and data fusion studies that investigate the impacts of resolutions and false alarms on track correlation and data fusion.