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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. 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
  2. Advanced Materials and Algorithms for Radiation Detection

    SBC: RD RESEARCH TECHNOLOGY USA LLC            Topic: NGA192003

    We aim to demonstrate the operation of a highly efficient direct thermal neutron detector using isotopically enriched icosahedral Boron Arsenide (B12As2) semiconducting thin film-based device. Schottky and PN junctions using p-type B12As2 thin films are proposed to evaluate the feasibility of our approach. Alpha particles generated as the result of the transmutation reaction of thermal neutrons w ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  3. Bulk Semiconductor Neutron Detection via Advanced Boron Materials

    SBC: LYNNTECH INC.            Topic: NGA192003

    Current traditional radiation detection (neutron detection) components are limited in utilization and need improvements. The Government aims to explore the use of new materials in radiation detection applications, and is seeking the development of a proof of concept solution for at least one new sensor material. Lynntech has developed a material that can improve the state-of-the art in neutron det ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  4. Improved still frames and denoised motion imagery from distressed FMV

    SBC: NANOHMICS INC            Topic: NGA191004

    Producers of imagery intelligence must contend with the distortions and defects in available images. One approach to recovering some the lost spatiotemporal video content during single frame analysis is to use processing techniques that improve spatial quality and resolution of individual frames by exploiting inter-frame correlations. However, the assumptions, enhancement capabilities, and computa ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
  5. Faster Optical Modem for Underwater Data Acquisition

    SBC: SONALYSTS INC            Topic: NGA182001

    To address NGA’s requirements, Sonalysts’ team of world-class experts in underwater optical communication proposes development and implementation of the Precision Optical Navigation Transceiver for Undersea Systems (PONTUS). PONTUS will transfer navigation information from an Underwater Navigation Beacon (UNB) to an Unmanned Undersea Vehicle (UUV) in an electromagnetic-spectrum-denied (e.g., G ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
  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. Densely Connected Neural Networks for Remote Sensing

    SBC: LYNNTECH INC.            Topic: NGA181010

    The objective of this project is to design a software architecture based on densely-connected neural network to perform automatic targetsegmentation and recognition using training datasets of limited size (low-shot). Deep learning architectures have proved to be extremelyeffective at object detection and recognition, but such capability comes at the cost of having large labeled datasets. Such data ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  8. Modularized Transliteration Engine based on DMS

    SBC: Semantic Designs Inc            Topic: NGA06001

    This SBIR project will define a means for specifying and executing multiple transliteration systems to map written words in scripts in a variety of natural languages into Latin scripts. The work will investigate the issues behind transliteration of place names. It will define a domain-specific language, TSL, in which a transliteration system can be coded in a format easily written, understood and ...

    SBIR Phase I 2006 Department of DefenseNational Geospatial-Intelligence Agency
  9. Hardness Validation by Verified Analysis (HV2A) Methodology for Advanced Materials

    SBC: KTECH CORP.            Topic: N/A

    Ktech proposes to develop a HV2A methodology for the assessment of nuclear weapon effects on new materials that are candidates for inclusion in strategic and tactical military systems. The methodology (1) provides an estimate of the system requirements and operation environments for each application specific material/structure, (2) establishes the critical response modes, (3) provides a framework ...

    SBIR Phase I 1998 Department of DefenseNational Geospatial-Intelligence Agency
  10. Geo-registration of Aerial Imagery Using 3-D Volumetric Models

    SBC: Computer Visioin Group, Inc            Topic: NGA11001

    With the advancement of aerial imaging sensors, high quality data equipped with partial sensor calibration models is available. There is a recent research activity in computer vision community that aims to reconstruct 3-d structure of the observed scenes relying on the content of the imagery in fully automated ways. However the research has not matured into robust systems ready for operational set ...

    SBIR Phase I 2011 Department of DefenseNational Geospatial-Intelligence Agency
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