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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. Reinforcement Learning with Intelligent Context-based Exploration (RL-ICE)

    SBC: SOAR TECHNOLOGY INC            Topic: SCO182006

    This effort will extend the RL-ICE capability for low-shot object detection to operate with zero real data within the MWIR band (zero-shot learning). Our goal is to 1.) mature our software, 2.) maximize the performance benefit of synthetic data in training an object detector and, 3.) assess the trade-space of real vs synthetic data by comparing performance to a detector trained on fully real data. ...

    SBIR Phase II 2023 Department of DefenseOffice of the Secretary of Defense
  2. QUINN (Quantum INspired Neural Networks)

    SBC: SOAR TECHNOLOGY INC            Topic: SCO183001

    QUINN (Quantum INspired Neural Networks) is a cybersecurity implementation to be deployed alongside a machine learning system (especially a reinforcement learning, active learning, and computer vision system) to harden said system from both standard generative adversarial attacks (noise injection) and cyber-physical attacks. This protection is provided by three unique components that perform attac ...

    SBIR Phase II 2022 Department of DefenseOffice of the Secretary of Defense
  3. 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
  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. 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
  6. Scalable Low-Cost AESA Transmitter with Phase-Only Nulling

    SBC: EMAG TECHNOLOGIES, INC.            Topic: SCO182002

    In this SBIR project, EMAG Technologies Inc. proposes to develop a compact, low-cost, scalable, transmit-only X-band active phased array antenna with phase-only nulling capability based on our proven VISAT architecture. The proposed AESA will use commercial PCB manufacturing platform and will utilize commercial off-the-shelf (COTS) parts and components for the entire multilayer stack-up. The propo ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  7. Reinforcement Learning with Intelligent Context-based Exploration (RL-ICE)

    SBC: SOAR TECHNOLOGY INC            Topic: SCO182006

    State of the art object detection in satellite imagery currently requires large quantities of hand-labeled satellite images. But what if there exists only very limited satellite imagery of the object, perhaps a single pass? Current deep learning solutions can not learn effective models with this extremely limited data. If, however, there exists model of the object that can be used to synthesize mo ...

    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. QUINN (Quantum INspired Neural Networks)

    SBC: SOAR TECHNOLOGY INC            Topic: SCO183001

    Machine learning models are susceptible to adversarial attacks that make modifications to the input data in order to cause misclassifications. The root cause is the linearity of the decision boundaries of machine learning models in relation to their inputs. One promising direction is to represent the input data as a distribution. Quantum information science entails techniques for working with wave ...

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