You are here

Award Data

For best search results, use the search terms first and then apply the filters
Reset

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. Formable Preform for Advanced Ceramic Matrix Composite Structures

    SBC: PEPIN ASSOCIATES INC            Topic: DLA23A003

    Pepin Associates, Inc. has developed a unique, aligned discontinuous textile reinforcement for composite structures.  This reinforcement architecture is composed of short, overlapped tow segments.  The architecture allows the textile to stretch in its reinforcement direction.  This ability permits rapid fabrication of complex shaped thermal protection system structures from simple low cost sh ...

    STTR Phase I 2023 Department of DefenseDefense Logistics Agency
  2. Multi-Task Scale Aware Continuous and Localizable Embeddings

    SBC: KITWARE INC            Topic: OSD22A001

    NGA uses deep networks for many tasks including image registration, land cover segmentation, and object detection. Current deep learning approaches develop specialist networks for each task and type of data. Not only is this inefficient, because networks can’t be reused across tasks, this approach ignores correlations between tasks and data sources that can improve performance. In response, we w ...

    STTR Phase I 2022 Department of DefenseNational Geospatial-Intelligence Agency
  3. sUAS Munition Teaming for Advanced Precision Strike

    SBC: CHARLES RIVER ANALYTICS, INC.            Topic: SOCOM21C001

    Precision-guided munitions have demonstrated dramatic effects with minimal collateral damage. New technology developed specifically to deny them accurate guidance information is now feasible, even for non-traditional adversaries. Further, digital communications are flooding the air with signals that interfere with communications many guidance methods rely on. Swarms of small, covert small Uncrewed ...

    STTR Phase I 2022 Department of DefenseSpecial Operations Command
  4. 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
  5. 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
  6. Explainable Query Refinement for Human Machine Teaming

    SBC: KITWARE INC            Topic: SOCOM18B001

    The Intelligence, Surveillance and Reconnaissance (ISR) analysts have a challenging task to extract useful information from huge volumes of data from various sources like Full Motion Video (FMV), Wide Area Motion Imagery (WAMI), satellite imagery, Synthetic Aperture Radar (SAR), and others. Modern Machine Learning (ML) algorithms based on deep learning have greatly advanced computer vision, speech ...

    STTR Phase I 2019 Department of DefenseSpecial Operations Command
  7. Additive Manufacturing Sensor Fusion Technologies for Process Monitoring and Control.

    SBC: SENVOL LLC            Topic: DLA18A001

    The Department of Defense (DoD) has a demand for out-of-production parts to maintain mission readiness of various weapons platforms. Additive manufacturing (AM) is an exciting and promising manufacturing technique that can make out-of-production parts and holds the potential to solve supply chain issues, such as high costs (i.e. for low-volume parts) and sole sourcing risks. The ability of AM to s ...

    STTR Phase I 2018 Department of DefenseDefense Logistics Agency
  8. System for Nighttime and Low-Light Face Recognition

    SBC: Systems & Technology Research LLC            Topic: SOCOM18A001

    Face recognition performance using deep learning has seen dramatic improvements in recent years. This improvement has been fueled in part by the curation of large labeled training datasets with millions of images of hundreds of thousands of subjects.This results in effective generalization for matching over pose, illumination, expression and age variation, however these datasets have traditionally ...

    STTR Phase I 2018 Department of DefenseSpecial Operations Command
  9. Human Performance Optimization

    SBC: REJUVENATE BIO INC            Topic: SOCOM17C001

    Special Operations Forces (SOF) are an integral aspect of the US military. SOF operators are among the most elite and highly qualified individuals in the U.S. military. As such, extraordinary physical and mental demands are placed upon them to excel in extreme environments for extended periods of time. This unrelenting cycle of combat deployments and intense pre-deployment training shortens the fu ...

    STTR Phase I 2018 Department of DefenseSpecial Operations Command
  10. Using Magnetic Levitation for Non-Destructive Detection of Defective and Counterfeit Materiel

    SBC: Nano Terra, Inc.            Topic: DLA15C001

    The introduction of substandard or counterfeit materials into the DoD supply chain can have extremely expensive, and potentially life threatening, consequences. Current techniques used to detect nonconforming materiel can be destructive (e.g., manual sectioning and inspection of a part), time consuming and expensive (e.g., micro-computed tomography, ultrasound), or provide only limited informatio ...

    STTR Phase I 2016 Department of DefenseDefense Logistics Agency
US Flag An Official Website of the United States Government