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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. Learning traffic camera locations using vehicle re-identification

    SBC: Arete Associates            Topic: NGA201005

    In its effort to provide necessary intelligence and analysis, the National Geospatial-Intelligence Agency (NGA) utilizes extensive traffic camera systems. However, the large amount of data overwhelms both analysts and existing processing methods. In order to provide a better understanding and reduce the search space for common problems such as target tracking, it is necessary to extract the camera ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  2. Bounding generalization risk for Deep Neural Networks

    SBC: Euler Scientific            Topic: NGA20A001

    Deep Neural Networks have become ubiquitous in the modern analysis of voluminous datasets with geometric symmetries. In the field of Particle Physics, experiments such as DUNE require the detection of particle signatures interacting within the detector, with analyses of over a billion 3D event images per channel each year; with typical setups containing over 150,000 different channels.  In an ...

    STTR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  3. DeepCounter

    SBC: SOAR TECHNOLOGY, INC.            Topic: DTRA192002

    SoarTech proposes DeepCounter: a system that automatically learns UAS and C-UAS behaviors across multiple tasks using DRL within a 3D simulation environment. Our system addresses several gaps in applying state of the art DRL to learn C-UAS and UAS behaviors. First, our existing DRL framework extends the state of the art to work with variable numbers of heterogenous agents. Our work in Phase I will ...

    SBIR Phase I 2020 Department of DefenseDefense Threat Reduction Agency
  4. Quantum-resistant Blockchain Framework and Software Tool

    SBC: Physical Optics Corporation            Topic: DTRA192003

    To address the Defense Threat Reduction Agency (DTRA) need for a new quantum-resistant blockchain framework that has the capability to preserve mission-critical enterprise data despite catastrophic nuclear, electromagnetic pulse, and/or cyberwarfare attacks on the United States, Physical Optics Corporation (POC) proposes to develop a new Quantum-resistant Blockchain (QChain) framework and software ...

    SBIR Phase I 2020 Department of DefenseDefense Threat Reduction Agency
  5. DeepBx: Modulated Energy X-ray Backscatter Deep Imager

    SBC: Radiabeam Technologies, LLC            Topic: DTRA192004

    DeepBx is an X-ray backscatter inspection system for detection and identification of threats hidden within or behind concrete walls. Four key features distinguish DeepBx from other backscatter approaches: • Ultra-compact Ku-band 1-MeV linac generating energy and current modulated X-ray pulses. Such pulses allow temporal coding of the penetration depth of X-rays within the wall. inspected object. ...

    SBIR Phase I 2020 Department of DefenseDefense Threat Reduction Agency
  6. Sensor-Fusion-Enhanced Multi-camera Dynamic Aerial Photogrammetry

    SBC: Physical Optics Corporation            Topic: DTRA192007

    To address the DTRA’s need for a multi-camera aerial photogrammetry solution accurately measuring dynamic deformations of 3-dimensional surfaces while permitting relative camera movement, Physical Optics Corporation (POC) proposes to develop a new Sensor-Fusion-Enhanced Multi-camera Aerial Photogrammetry (SFEMCAP) system. It will utilize commercial off-the-shelf, low-size, -weight, and -power sa ...

    SBIR Phase I 2020 Department of DefenseDefense Threat Reduction Agency
  7. DEVELOPMENT OF CAMERA-MOTION INSENSITIVE DYNAMIC DIGITAL PHOTOGRAMMETRY USING DIGITAL IMAGE CORRELATION

    SBC: TOYON RESEARCH CORPORATION            Topic: DTRA192007

    Toyon proposes R&D of algorithms that process high-frame-rate video collected by two or more independently moving cameras to measure 3D displacements of remote surfaces. To support this, we will collect data along with ground truth. We will develop an algorithm that processes synchronized video data to estimate 3D camera motion for multiple cameras. We will develop an algorithm that creates high-r ...

    SBIR Phase I 2020 Department of DefenseDefense Threat Reduction Agency
  8. Hardened, Optically-Based Temperature Characterization of Detonation Environments

    SBC: SA Photonics, Inc.            Topic: DTRA19B001

    Improving the effectiveness of counter-WMD operations requires improved understanding of weapon-target interaction. Specifically, time-resolved measurements of temperature and composition are required to allow temporal evolution of a detonation fireball. To address this need, SA Photonics will develop MONITOR, a laser-based temperature diagnostic that will enable wide dynamic range temperature mea ...

    STTR Phase I 2020 Department of DefenseDefense Threat Reduction Agency
  9. Multi-Platform 3-D Radiation Mapping with Enhanced Sensor Data Fusion and Visualization

    SBC: Gamma Reality Inc.            Topic: DTRA192005

    Mapping radiological and nuclear materials over large areas in the context of potential structural and battlefield damage with minimum risk to the Warfighter remains a challenge in the defense community. The current state-of-the-art radiation detection systems require multiple stationary measurements to produce multiple images of large areas in 2D. This project will leverage recent advancements in ...

    SBIR Phase I 2020 Department of DefenseDefense Threat Reduction Agency
  10. A Multi-Branch Network for Automated VNIIRS Assessment of Motion Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: NGA191003

    Due to the lack of consistency in existing automated methods for assigning VNIIRS levels to motion imagery, and the overwhelming human resources required to manually assign levels, a new method of automated/semi-automated VNIIRS assessment is needed. In recent years, advancements in deep learning have provided solutions to previously intractable computer vision problems. In many cases, automated d ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
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