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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. 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
  2. Guidance System for Autonomous Medium Caliber Munition

    SBC: OPTO-KNOWLEDGE SYSTEMS INC            Topic: SOCOM182001

    OKSI will demonstrate autonomous operations concepts for self-guided munition with Fire-Before-Lock-On-Target capabilities and with obstacle avoidance and anti-defilade targets.We will assess the feasibility of implementing the technology in medium caliber munitions.

    SBIR Phase I 2018 Department of DefenseSpecial Operations Command
  3. Munition Guidance Package for Use in a GPS and Laser Denied Environment

    SBC: TOYON RESEARCH CORPORATION            Topic: SOCOM182001

    Traditional munition guidance packages rely on the Global Positioning System (GPS) or laser designation technologies to target an operator defined location. However, if either of these systems cannot operate, whether they are being actively denied by the enemy or from harsh environmental conditions, the guidance package will fail to function as designed. Toyon Research Corporation proposes to crea ...

    SBIR Phase I 2018 Department of DefenseSpecial Operations Command
  4. Dynamic Diver Breathing Gas Heat Recycler

    SBC: Tanner Research, Inc.            Topic: SOCOM182003

    Tanner proposes to develop a dynamic heat pump system with self-contained, rechargeable battery and driving circuit that operates as a Combination Thermal Recycler (CTR). By utilizing thermal reservoirs coupled with the rebreather mask, heat can be efficiently recycled in proportion to the temperature difference between the exhaled air stream and a target inhaled air temperature. No power would be ...

    SBIR Phase I 2018 Department of DefenseSpecial Operations Command
  5. Diver Breathing Gas Re-heater for Closed Circuit Underwater Breathing Apparatus

    SBC: Sea Star, LLC            Topic: SOCOM182003

    The objective of this proposal is to develop a robust, lightweight and compact device for re-heating and maintaining the temperature of a divers inspired breathing gas when diving on a closed circuit underwater breathing apparatus for the duration of the dive. As a solution, Simuleer proposes to develop the Hybrid Energy Abiotic Thermoelectric (HEAT) system based on a high-power hybrid (i.e. passi ...

    SBIR Phase I 2018 Department of DefenseSpecial Operations Command
  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. 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
  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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