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The Award database is continually updated throughout the year. As a result, data for FY24 is not expected to be complete until March, 2025.

Download all SBIR.gov award data either with award abstracts (290MB) or without award abstracts (65MB). A data dictionary and additional information is located on the Data Resource Page. Files are refreshed monthly.

The SBIR.gov award data files now contain the required fields to calculate award timeliness for individual awards or for an agency or branch. Additional information on calculating award timeliness is available on the Data Resource Page.

  1. Recycling Fast-Response Atom Interferometer for Navigation (ReFRAIN)

    SBC: PHYSICAL SCIENCES INC.            Topic: OSD221006

    Physical Sciences Inc. (PSI) will develop a Recycling Fast-Response Atom Interferometer for Navigation (ReFRAIN) as an atom interferometer (AI) accelerometer specially designed to improve inertial navigation systems (INS) on moving platforms.  The ReFRAIN provides sensitivity, dynamic range, bandwidth, and bias stability that matches or exceeds state of the art mechanical accelerometers.  PSI in ...

    SBIR Phase I 2022 Department of DefenseOffice of the Secretary of Defense
  2. High Acceleration and Hypervelocity Inertial Measurement Unit

    SBC: ENGENIUSMICRO LLC            Topic: OSD181001

    Gun-launched applications currently expose inertial measurement units (IMUs) to harsh acceleration, shock, and vibration environments. Furthermore, as they become smarter, they present tighter constraints on size, weight, power, and cost (SWaP-C), while still requiring high levels of performance. New accelerometer technology must reduce SWaP-C while operating through high-g acceleration environmen ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  3. High Acceleration and Hypervelocity Inertial Measurement Unit

    SBC: NANOHMICS INC            Topic: OSD181001

    Nanohmics proposes to develop a chip-scale inertial measurement unit (IMU) for munitions applications. The miniaturization is key to extreme-G survivability and operation. Models and simulations will provide primary support for the feasibility and a hardware demonstration will provide additional proof-of-concept and improve the program risk assessment prior to a Phase II program.

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  4. Electro-optical Seeker

    SBC: CERANOVA CORP            Topic: OSD181002

    Execution of long-range weapons capabilities reduces risk and affords greater warfighter protection. Core enabling technologies for hypersonic projectiles include high-strength lightweight materials, precision avionics, and novel designs. System demands include the ability to withstand both high accelerations (up to 50,000 Gs) and aerothermal shock. Detailed structural engineering and shock modeli ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  5. Electro-optical Seeker

    SBC: POLARIS SENSOR TECHNOLOGIES INC            Topic: OSD181002

    Mission times for high velocity projectiles (HVP) are very short and detection and discrimination of targets must happen quickly and decisively. One way to achieve this is through the enhanced contrast resulting from polarized sensing, which tends to highlight manmade objects and suppress natural background clutter. Thermal polarimetric sensing in a small package has been demonstrated already but ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  6. Low-Cost, Transmit-Only, Active Electronically Steered Array (AESA) with Phase-Only Nulling (1000-471)

    SBC: SI2 TECHNOLOGIES, INC            Topic: SCO182002

    SI2 proposes to leverage prior Government funded efforts to develop a transmit (TX) only, wideband (6:1 bandwidth), low-cost, scalable active electronically scanned array (AESA) that will utilize phase-only nulling to enable radar, Electronic Warfare / Electronic Attack (EW/EA), Information Operations (IO) and other capabilities on multiple platforms across DOD agencies. The array will employ digi ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  7. High Velocity Gun-Launched Projectile and Sabot Structures

    SBC: TEXAS RESEARCH INSTITUTE , AUSTIN, INC.            Topic: SCO182003

    The development of a guided hypervelocity projectile (HVP) must surmount a number of significant technical challenges. Texas Research Institute Austin (TRI Austin) proposes to address leading edge/control surface challenges, sabot design, and the sub-projectile issues through the selection of materials, material processes, and component manufacturing methods. TRI Austin has assembled a team of tec ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  8. Maritime/Systems & Technology Research

    SBC: Systems & Technology Research LLC            Topic: SCO182008

    Airborne radars operating over open water must classify maritime vessels by measuring and exploiting highly-variable radar signatures. Sources of signature variability include within-class ship construction and equipment differences, complex in-situ 6-DoF ship motion caused by ocean waves across a range of sea state conditions, acquisition geometry including grazing angle and maritime-specific RF ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  9. Secure Private Neural Network (SPNN)/Charles River Analytics Inc.

    SBC: CHARLES RIVER ANALYTICS, INC.            Topic: SCO182009

    Deep Neural Networks (DNNs) are becoming widely used in the DoD for image classification, but recent research has shown DNNs are vulnerable to adversary attacks. Adversaries can monitor the DNN training and classification processes to learn attributes of the training data and the DNN. With this information, an adversary can gain valuable insight into the potentially sensitive data used to train th ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  10. Secure Edge Computing with Encrypted Neural Networks

    SBC: VCRSOFT LLC            Topic: SCO182009

    Homomorphic Encryption (HE) allows for training and deployment of neural networks on encrypted data. Furthermore, HE allows for encryption of neural network model parameters such as weights. Thus, HE provides robustness against both black-box and white-box attacks. In the emerging cloud-based AI environments with edge computing nodes, HE enables privacy-preserving training and deployment of neural ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
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