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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. Advanced High End Gyroscopes for Small Form Factor Inertial Measurement Unit Applications

    SBC: FREEDOM PHOTONICS LLC            Topic: MDA18005

    In this program, Freedom Photonics will develop a novel, miniature gyroscope that meets the requirements of the MDA. This gyroscope will be based on our prior work and demonstration of the gyroscope with similar performance, but larger footprint. Approved for Public Release | 18-MDA-9710 (6 Jul 18)

    SBIR Phase I 2018 Department of DefenseMissile Defense Agency
  2. Nanoscale Vacuum Channel Transistor (NVCT)

    SBC: Innoflight, Inc.            Topic: MDA18007

    Nanoscale Vacuum Channel Transistor (NVCT) technology has been demonstrated as intrinsically insensitive to ionizing radiation effects and robust enough to operate in elevated thermal environments. Nanofabrication processes need to be matured and methods need to be developed to enable advance circuits. The Ballistic Missile Defense System (BMDS) sensor and countermeasure elements can leverage NVCT ...

    SBIR Phase I 2018 Department of DefenseMissile Defense Agency
  3. Structurally-Integrated, Multifunctional Components for Next-Gen Kill Vehicles (1000-445)

    SBC: SI2 TECHNOLOGIES, INC            Topic: MDA18008

    SI2 Technologies, Inc. proposes to improve the volumetric efficiency and reduce the system weight of next-generation kill vehicles (KVs) by developing low profile, lightweight, multifunctional components. Specifically, SI2 will utilize in-house capabilities to design and additively manufacture (AM) a multi-functional structure that includes an S-band datalink antenna integrated within the structur ...

    SBIR Phase I 2018 Department of DefenseMissile Defense Agency
  4. SmallSat Stirling Cryocooler for Missile Defense (SSC-X)

    SBC: WECOSO, INC.            Topic: MDA17T003

    West Coast Solutions (WCS), in collaboration with the Georgia Institute of Technology and Creare LLC, proposes an adaptation of our SmallSat Stirling Cryocooler (SSC) technology in response to STTR Topic MDA17-T003: High-Efficiency, Low-Volume, Space-Qualified Cryogenic-Coolers. In Phase 1 we will scale up a design currently in development for NASA to meet the Missile Defense Agency (MDA) topic re ...

    STTR Phase I 2018 Department of DefenseMissile Defense Agency
  5. Lightweight Structural Components of a Missile Body

    SBC: ALPHASTAR TECHNOLOGY SOLUTIONS LLC            Topic: MDA17T004

    The Ground-Based Interceptor (GBI) missile is the weapon component of the Ground-Based Midcourse Defense (GMD) system that consists of a rocket booster and kinetic kill vehicle. Recently, MDA has sought technologies to improve the performance of the booster vehicle (BV). To date, studies have shown that reductions in weight have a direct impact on overall effectiveness. The current proposal aims t ...

    STTR Phase I 2018 Department of DefenseMissile Defense Agency
  6. Hybrid DNN-based Transfer Learning and CNN-based Supervised Learning for Object Recognition in Multi-modal Infrared Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: 1

    On this effort Toyon Research Corp. and The Pennsylvania State University are developing deep learning-based algorithms for object recognition and new class discovery in look-down infrared (IR) imagery. Our approach involves the development of a hybrid classifier that exploits both transfer learning and semi-supervised paradigms in order to maintain good generalization accuracy, especially when li ...

    STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  7. 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
  8. GEOFF: Geo-location from Edges, Objects, Foundational data, and a Filter

    SBC: SCIENTIFIC SYSTEMS CO INC            Topic: NGA181007

    Ground vehicles with navigation capability (e.g., GPS) can index into foundation data (e.g., Google Maps) to gain situational awareness abouttheir surroundings. When GPS and RF navigation sources are degraded, maintaining situational awareness requires an alternative navigationsource. One alternative source is the foundation data itself. The data contain objects at known 3D locations, which projec ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  9. Variational Object Recognition and Grouping Network

    SBC: INTELLISENSE SYSTEMS INC            Topic: NGA181005

    To address the National Geospatial-Intelligence Agency (NGA) need for overhead imagery analysis algorithms that provide uncertaintymeasures for object recognition and aggregation, Intellisense Systems, Inc. (ISS) proposes to develop a new Variational Object Recognition andGrouping Network (VORGNet) system. It is based on the innovation of implementing a Bayesian convolutional neural network (CNN) ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  10. DNC-GD: Deep Neural Network Compression for Geospatial Data

    SBC: Intelligent Automation, Inc.            Topic: NGA181009

    Following technology advances in high-performance computation systems and fast growth of data acquisition, a technical breakthroughnamed Deep Learning made remarkable success in many research areas and applications. Nevertheless, the progress of hardwaredevelopment still falls far behind the upscaling of deep neural network (DNN) models at the software level. NGA seeks to apply neuralnetwork minia ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
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