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The Award database is continually updated throughout the year. As a result, data for FY19 is not expected to be complete until April, 2020.

  1. Ruggedized Agile PNT System (RAPS) for Missile in GPS-Challenged Environments

    SBC: MAYFLOWER COMMUNICATIONS COMPANY, INC.            Topic: A17118

    The objective of this SBIR program is to improve operational performance of US Army missile systems by enhancing position, navigation, and timing (PNT) for operation in GPS contested environments. The PNT system for the missile platforms will also require the system to be ruggedized to withstand shock during launch and operation with speeds spanning many mach.Mayflower proposes to develop and demo ...

    SBIR Phase I 2018 Department of DefenseArmy
  2. High Energy Density Energetic Matrices

    SBC: Physical Sciences Inc.            Topic: A17125

    Physical Sciences Inc. and BAE Systems propose to develop and demonstrate a scalable process to oxidatively resistant nanometallic matrices for use in energetic materials. The multi-functional coated nanometallic matrices will provide an oxidatively stable, hydrophobic, PAX binder compatible nanoparticle fuel to augment explosive performance and insensitive munition compliancy. The proposed proces ...

    SBIR Phase I 2018 Department of DefenseArmy
  3. Advanced Fire Control Radar for Group 1 and 2 Unmanned Surveillance Systems

    SBC: PHYSICAL OPTICS CORPORATION            Topic: A17126

    To address the Armys need for a fire control radar for small UASs, Physical Optics Corporation (POC) proposes to develop an Advanced Fire Control Radar for Group 1 and 2 Unmanned Surveillance Systems (ARGUS). ARGUS is a compact, lightweight, and low power sensor that directly fits in the electronics or payload bay of Group 1 and 2 UASs. It interfaces with the platforms flight computer through stan ...

    SBIR Phase I 2018 Department of DefenseArmy
  4. DEEP FOCUS:USING DEEP LEARNING TO DISCERN TARGETS IN CLUTTERED RADAR

    SBC: CLOSTRA, INC.            Topic: A17133

    Deep Focus applies deep learning neural nets to Apache Fire Control Radar (FCR) targeting and target identification, with applicability to related systems. Recent innovations in deep learning theory and implementation have enabled neural nets to achieve what was once unthinkable:beat humans at complex image recognition skills, safely pilot cars over chaotic road systems, and overwhelm Grandmaster ...

    SBIR Phase I 2018 Department of DefenseArmy
  5. Fiber Laser with Enhanced Coupling

    SBC: Hedgefog Research Inc.            Topic: A17136

    To address the Armys need for a novel approach that enables pump combiners with improved coupling efficiency for high-power fiber laser systems, Hedgefog Research Inc. (HFR) proposes to develop a new Fiber Laser with Enhanced Coupling (FLEC) that minimizes excess heat generation in fiber lasers via efficient mode scrambling of the pump light in the cladding of the active fiber. Specifically, we wi ...

    SBIR Phase I 2018 Department of DefenseArmy
  6. High Coupling Efficiency Optical Pump Combiners for Fiber Laser Systems

    SBC: Photonwares Corporation            Topic: A17136

    Improving the pump coupling efficiency is a technical challenge for high power laser systems.In this program, Photonwares proposes to develop a new approach for fiber optical pump combiners, that promises to significantly increase the coupling efficiency in comparison with current state-of-the-art at the same time also increase the pumping port number.Leveraging our fiber optical pump combiner man ...

    SBIR Phase I 2018 Department of DefenseArmy
  7. Body-aware Robotic Appliqu for Collaborative Evacuation (BRACE)

    SBC: Charles River Analytics, Inc.            Topic: A17139

    The DoD envisions UMS to provide significant operational benefit to dangerous protection tasks, such as CASEVAC. Such systems have the potential to remove first responders from harms way and improve the outcomes of combat casualties by enabling rapid CASEVAC in hostile conditions and environments. Multiple designs for such platforms exist. However, in addition to requiring active remote control fo ...

    SBIR Phase I 2018 Department of DefenseArmy
  8. Multi-Resolution Modeling And Simulation Toolkit (MultiMAST)

    SBC: Charles River Analytics, Inc.            Topic: A16075

    Megacities are rapidly becoming the epicenter of human activity. Their combination of dense terrains and highly interconnected systems requires a different skillset compared to those required for more conventional US military operations. Given the scale and complexity of megacities, real-world training is impractical and beyond the capabilities of the tools currently supporting Live Virtual Constr ...

    SBIR Phase II 2018 Department of DefenseArmy
  9. Medical Augmented Reality for Combat Casualty Care (MARC)

    SBC: Charles River Analytics, Inc.            Topic: A16076

    Tactical Combat Casualty Care (TCCC) is essential for the Department of Defense during force-on-force engagements, but training is currently lacking. While traditional classroom instruction and high-fidelity simulation center training are important, they fail to provide practice in a larger exercise under field conditions in which the realities of combat impact medical treatment. Furthermore, thou ...

    SBIR Phase II 2018 Department of DefenseArmy
  10. Secure, Miniature, Algorithm-enhanced Low-power Low-profile Photoplethysmogram Patch

    SBC: PHYSICAL OPTICS CORPORATION            Topic: A16053

    To address the Armys need for a secure disposable patch that generates a photoplethysmogram (PPG) waveform, Physical Optics Corporation (POC) proposes to continue the development of a new ruggedized Secure, Miniature, Algorithm-Enhanced Low-Power Low-Profile Photoplethysmogram (SMALL-PPG) patch proven feasible in Phase I. SMALL-PPG is based on a novel integration of POCs physiological monitoring t ...

    SBIR Phase II 2018 Department of DefenseArmy

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