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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. Heterdyne Interferometer for Triggering Gas Puff PRSs

    SBC: HY-Tech Research Corp.            Topic: N/A

    Large X-ray generators using gas fill loads are a key element in the DTRA simulator program. Consistent relative timing between the injection of the gas shell and the generator current pulse is crucial to producing consistent, high-yield x-ray pulses.To that end HY-Tech proposes replacing existing high voltage trigger pin technology, with a sensitive, all fiber-optic heterodyne interferometer ba ...

    SBIR Phase I 2003 Department of DefenseDefense Threat Reduction Agency
  2. IED neutralization from an airborne platform

    SBC: IMSAR LLC            Topic: DTRA182001

    In cooperation with the Joint Improvised-threat Defeat Organization (JIDO) and the U.S. Army, IMSAR LLC demonstrated its radar systems' ability to successfully detect IED emplacements from the RQ-7B Shadow Unmanned Aerial System (UAS). As part of the Army’s ShadowSAR program, IMSAR operated and maintained these Synthetic Aperture Radar (SAR) sensors for 4 ½ years in support of Operation Endurin ...

    SBIR Phase I 2019 Department of DefenseDefense Threat Reduction Agency
  3. Automated pattern recognition methods to identify nuclear explosions

    SBC: Acorn Science & Innovation, Inc.            Topic: DTRA182005

    Reliable automated pattern recognition in the current system is limited by excessive noise and clutter combined with insufficient and/or ambiguous features. Our team consisting of AcornSI and Leidos thus propose a multi-step approach based on the combined effects of improved detection, feature extraction, phase identification, and global association. A key innovation here is creating a new classif ...

    SBIR Phase I 2019 Department of DefenseDefense Threat Reduction Agency
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