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Award Data

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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. Plasma Generator for Controlled Enhancement of the Ionosphere

    SBC: Enig Associates, Inc.            Topic: AF15AT22

    Enig Associates, Inc., a small business providing advanced modeling and simulation capabilities to the DoD and DoE, is proposing an innovative and novel electrical approach, using explosive-driven flux compression generators to Joule heat light metal load in sub millisecond time scale from a solid metal state to a first ionization plasma state going through multi-phase transitions to generate an a ...

    STTR Phase II 2016 Department of DefenseAir Force
  2. ROBUST MOVING TARGET HANDOFF IN GPS-DENIED ENVIRONMENTS

    SBC: UTOPIACOMPRESSION,CORPORATION            Topic: AF15AT34

    Unmanned aircraft systems (UAS) are increasingly seen as a cornerstone in developing the future Defense infrastructure and it is critical that they collaborate efficiently and execute complex missions in denied environments. Although great progress has been made in GPS-denied navigation, the target handoff problem in GPS-denied environments has not been extensively studied. In this problem, a trac ...

    STTR Phase II 2016 Department of DefenseAir Force
  3. Small Sample Size Semi-Supervised Feature Clustering for Detection and Classification of Objects and Activities in Still and Motion Multi-spectral Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: AF15AT35

    Toyon Research Corp. and the Penn State Univ. propose research and development of innovative algorithms for classifying objects and activities observed in high-dimensional data extracted from multi-sensor motion imagery. The proposed algorithms include novel feature clustering techniques to enable effective characterization of intra-class and inter-class appearance variations in datasets containin ...

    STTR Phase II 2016 Department of DefenseAir Force
  4. ENHANCING MOTION IMAGERY CLASSIFIERS BY PRINCIPAL COMPONENT FEATURE CLUSTERING

    SBC: LONGSHORTWAY INC.            Topic: AF15AT35

    LongShortWay Inc. and Northeastern University propose a family of feature reduction and ensemble classifier methods based on Principal Component and Dynamic Logic feature clustering algorithms. New methods combine feature clustering with non-linear feature reduction via manifold learning, and bagging, boosting, and stacking ensemble algorithms.

    STTR Phase II 2016 Department of DefenseAir Force
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