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The Award database is continually updated throughout the year. As a result, data for FY23 is not expected to be complete until September, 2024.

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. Edge Quantum Processor

    SBC: STREAMLINE AUTOMATION LLC            Topic: SOCOM22DST01

    Quantum technology will become a key enabler of future Air Force superiority. Topological insulator (TI) qubits are inherently stable and fault-tolerant because they exploit local topological symmetries and global boundary conditions of chalcogenide materials to yield unique, emergent quantum states. Wake Forest University and Streamline Automation have been working collaboratively for the last se ...

    STTR Phase I 2022 Department of DefenseSpecial Operations Command
  2. sUAS Munition Teaming for Advanced Precision Strike

    SBC: INVARIANT CORPORATION            Topic: SOCOM21C001

    This task seeks to develop advanced teaming via machine learning between small unmanned air systems and Non Line-of-Sight (NLOS) munitions in GPS denied Environments. Current precision targeting capabilities are robust to state errors from ISR targeting platforms and weapons systems seeking to passively acquire a target. Visual Based Navigation (VBN) provides required state information in GPS deni ...

    STTR Phase I 2022 Department of DefenseSpecial Operations Command
  3. Algorithm Performance Evaluation with Low Sample Size

    SBC: SIGNATURE RESEARCH, INC.            Topic: NGA20C001

    The team of Signature Research, Inc. and Michigan Technological University will develop and demonstrate methods and metrics to evaluate the performance of machine learning-based computer vision algorithms with low numbers of samples of labeled EO imagery. We will use the existing xView panchromatic dataset to demonstrate a proof-of-concept set of tools. If successful, in Phase II, we will extend t ...

    STTR Phase I 2021 Department of DefenseNational Geospatial-Intelligence Agency
  4. System for Nighttime and Low-Light Face Recognition

    SBC: POLARIS SENSOR TECHNOLOGIES INC            Topic: SOCOM18A001

    The objective of this proposal is to develop instrumentation and algorithms for acquiring facial features for facial recognition in low- and no-light conditions.We will use cross-spectrum matching by exploiting infrared polarimetric imagery which tends to show features that match more closely visible imagery than conventional infrared.In addition to thermal infrared, we will also test subjects in ...

    STTR Phase I 2018 Department of DefenseSpecial Operations Command
  5. Novel Circulating RNA-based Markers as Diagnostic Biomarkers of Infectious Diseases

    SBC: CFD RESEARCH CORPORATION            Topic: CBD18A001

    In resource limited settings, rapid and accurate diagnosis of infections is critical for managing potential exposures to highly virulent pathogens,whether occurring from an act of bioterrorism or a natural event. This is especially important for hard to detect intracellular bacterial andalphavirus infections, that overlap symptomatically and often treated empirically due to a lack of reliable and ...

    STTR Phase I 2018 Department of DefenseOffice for Chemical and Biological Defense
  6. Algorithms for Look-down Infrared Target Exploitation

    SBC: SIGNATURE RESEARCH, INC.            Topic: 1

    Signature Research, Inc. (SGR) and Michigan Technological University (MTU) propose a Phase I STTR effort to develop a learning algorithm which exploits the spatio-spectral characteristics inherent within IR imagery and motion imagery.Our archive of modelled and labeled data sets will allow our team to thoroughly capture the variable elements that will drive machine learning performance.The overall ...

    STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  7. Infectious Disease Diagnostics and Differentiation of Viral vs. Bacterial Infections for Point of Care Applications

    SBC: GENECAPTURE, INC.            Topic: CBD15C001

    The modern warfighter faces the constant threat of endemic infections, multi-drug resistant bacteria and Biological Warfare Agents. In order to provide accurate front-line treatment that will curtail the overuse of antibiotics, a rapid and robust molecula

    STTR Phase I 2016 Department of DefenseOffice for Chemical and Biological Defense
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