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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.

  1. Innovative methods for detecting and characterizing electrical grid topologies and induced electrical power transient events from lights

    SBC: Systems & Technology Research LLC            Topic: NGA191010

    STR is proposing to implement monitoring of power grid state via high-speed, wide-field optical photometry. We will design, test, and implement algorithms on commercially available hardware with the intent of deriving grid topology in addition to detecting, characterizing, and geolocating anomalous events. We will also evaluate fusing the photometry-derived data with other data sources available t ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
  2. 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
  3. Gamified Analysis Tasks for Heightened Engagement across Repetitions (GATHER)

    SBC: Charles River Analytics, Inc.            Topic: NGA191007

    At the National Geospatial-Intelligence Agency (NGA), the ability to serve and analyze data is crucial to the success of efforts ranging from disaster relief to strategic military support. NGA recently created the Office of Automation, Augmentation, and Artificial Intelligence (AAA) which has a goal “to automate routine tasks to give crucial time back to employees.” These automated systems mus ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
  4. ALARM: Adversarially-learned Labels using Activity and Reward Models

    SBC: Aptima, Inc.            Topic: NGA191006

    Technological advances in navigation and positioning, along with expanding wireless infrastructure and remote sensing technologies, have resulted in an explosive growth of available trajectory data from a variety of moving objects, such as people, cars, ships, or animals. Traditional trajectory mining algorithms do not explain how and why the motion was generated, limiting their utility in GEOINT ...

    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. Faster Optical Modem for Underwater Data Acquisition

    SBC: SA Photonics, Inc.            Topic: NGA182001

    SA Photonics’ Optical Navigation and Ranging (ONAR) system is an interrogative system that operate underwater in wavelength range of blue/green (450-540 nm) and enables navigational correction to IMU based dead reckoning navigation. The location based beacons are battery operated and have operational life span of over one year. The system is designed to operate in on demand burst mode so that no ...

    SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency
  7. 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
  8. GEOFF: Geo-location from Edges, Objects, Foundational data, and a Filter

    SBC: Scientific Systems Company 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. Blending Ground View and Overhead Models

    SBC: Arete Associates            Topic: NGA181008

    We propose to build ARGON, the ARet Ground-to-Overhead Network. The network will ingest analyst-supplied ground-level imagery ofobjects and retrieve instances of those objects in overhead collections, providing tips back to the analysts. A proprietary method of trainingthe network, leveraging in-house capabilities, data sources, and tools, will be critical to its success. During Phase I, we will p ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
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