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The Award database is continually updated throughout the year. As a result, data for FY20 is not expected to be complete until September, 2021.

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.

  1. Command and Control, Modeling and Simulation, Training

    SBC: Sonalysts, Inc.            Topic: MDA13T005

    The Sonalysts Team proposes to investigate, design, and develop a training system leveraging state-of-the art game and agent-based technologies with"TRILOGY,"a Modeling, Simulation, and Training (MST) system capable of three core capabilities: training, rehearsal, and analysis/CONOPS development. Sonalysts will leverage their latest commercial-off-the-shelf (COTS) game engine, Simulation Engine I ...

    STTR Phase I 2014 Department of DefenseMissile Defense Agency
  2. Voxel-based Fusion (VOXION)

    SBC: 21ST CENTURY SYSTEMS, INC.            Topic: AF13AT01

    ABSTRACT: In ISR applications, multiple sensors can be employed to achieve more accurate parameters of interest on targets, improving discrimination and detection. But, the proliferation of sensor systems has created a large volume of multi-sensor data across a number of physical spectra. These resulting measurements can be so disparate that traditional aggregation methods perform poorly given h ...

    STTR Phase I 2014 Department of DefenseAir Force
  3. Secure Efficient Cross-domain Protocols

    SBC: Sonalysts, Inc.            Topic: AF13AT08

    ABSTRACT: The Sonalysts team proposes to develop a solution that facilitates the flow of data between different levels of classification while improving and ensuring both security and data integrity. Our approach involves a set of protocols that provide a unique and disruptive combination of multichannel data dispersion and multi-classification data management techniques to meet our objectives. ...

    STTR Phase I 2014 Department of DefenseAir Force
  4. Near Real-Time Quantification of Stochastic Model Parameters

    SBC: APPLIED MATHEMATICS INC            Topic: A13AT009

    Mathematical models of physical and biological systems contain parameters that need to be estimated from measured data. Models with parameters distributed probabilistically require the estimates of a probability measure over the set of admissible parameters. We propose to use frequentist-based approaches for non-parametrically estimating probability measures that describe the distribution of par ...

    STTR Phase II 2014 Department of DefenseArmy
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