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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. Tactical Sensor Data Processing, Exploitation, and Dissemination

    SBC: TOYON RESEARCH CORPORATION            Topic: SOCOM163008

    Toyon Research Corp. proposes the development, testing, and integration of an in-house automated 3D reconstruction and wide-area reconnaissance pipeline that provides Special Operations Forces (SOF) mission planners with the capability of automated high-resolution 3D model/scene generation from tactical sensor data and imagery. The proposed development will incorporate algorithms identied in the P ...

    SBIR Phase II 2018 Department of DefenseSpecial Operations Command
  2. Integrated Color Night-Vision Camera

    SBC: Physical Optics Corporation            Topic: SOCOM163006

    To address the USSOCOM need for a color night-vision sensor, in Phase II, Physical Optics Corporation (POC) proposes to advance development of the new Integrated Color Night-Vision Camera (ICONIC) proven feasible in Phase I. The innovation in the proposed color night-vision sensor is the compact long effective focal length lens (CLOFL) optics that allows for a compact camera with the required rang ...

    SBIR Phase II 2018 Department of DefenseSpecial Operations Command
  3. Cloud Data Synchronization with Limited Bandwidth Communications

    SBC: BLACK RIVER SYSTEMS COMPANY, INC.            Topic: SOCOM163005

    As the DoD continues to develop applications backed by cloud-based data and enterprise infrastructures, remote-sites will require on-site data caches to permit operation during periods of disconnect from the enterprise.To address this challenge for SOCOMs Chimera system, Black River will develop a stand-alone Chimera system that is independent of Amazon cloud software; develop bi-directional data ...

    SBIR Phase II 2018 Department of DefenseSpecial Operations Command
  4. Solid Propulsion Engineered for CubeSat Kinetic Operations (SPECK-Ops)

    SBC: EXQUADRUM INC            Topic: SOCOM17003

    During the proposed Phase II research and development effort, the project team will develop and demonstrate the Solid Propellant Engine for Enhanced Delta-V (SPECK-Ops) to provide a responsive, high-thrust orbital maneuvering, propulsion capability to CubeSats. The system will be tested simulated space environments and subject to various space qualification tests. The resulting propulsion system w ...

    SBIR Phase II 2018 Department of DefenseSpecial Operations Command
  5. Untethered LowerBody Actuated Exoskeleton Motion Prediction and Sensing

    SBC: Roam Robotics, Inc            Topic: NSF1621489

    Despite years of development, exoskeletons have yet to demonstrate dynamic benefits where they can improve the performance of able-bodied individuals. This proposal aims to use novel fabric centered exoskeleton technology to develop a lightweight, high power density exoskeleton to improve dynamic human performance for the first time.

    SBIR Phase II 2018 Department of DefenseSpecial Operations Command
  6. SHELLE- Swift Heavy Lift Long Endurance UAV

    SBC: SWIFT ENGINEERING INC.            Topic: SOCOM172004

    SHELLE brings a paradigm shifting level of performance to the US military for electric field deployable UAS in range, endurance and payload capability. It bridges the capability gap between existing hand launchable electric UAS and larger liquid fueled UAS. The SHELLE V1 even outperforms many liquid fuel aircraft and can match the payload of aircraft twice its weight at a fraction of the cost. Com ...

    SBIR Phase II 2018 Department of DefenseSpecial Operations Command
  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. NGAflix: a cloud-based adaptive bitrate video processing and distribution system

    SBC: KITWARE INC            Topic: NGA181002

    The large volume of full motion video from unmanned aerial vehicles, along with other data from various sensors creates resource andengineering challenges in managing, processing and distributing that data. Law enforcement and intelligence work require videos in locationsfar from where they were recorded, and need multiple sensor streams to be synchronized for search, filtering, and transformation ...

    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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