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The Award database is continually updated throughout the year. As a result, data for FY21 is not expected to be complete until September, 2022.

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. Enhanced Canine Performance, Protection and Survivability

    SBC: Metro International Biotech, LLC            Topic: SOCOM172002

    Given the benefits of exercise on general health, orally active compounds that can mimic or potentiate the effects of exercise have been of keen interest as therapeutic and dietary ingredients. Our proposed approach for enhancement of USSOCOM Multi-Purpose Canine (MPC) endurance and sensory performance using an exercise mimetic is based on the observation that age-related decline of nicotinamide ...

    SBIR Phase I 2018 Department of DefenseSpecial Operations Command
  2. Group 2 (<55lbs) Unmanned Aerial System for Special Operations Forces Tactical-Level Intelligence, Surveillance, and Reconnaissance Operations

    SBC: Sealandaire Technologies, Inc.            Topic: SOCOM172005

    SeaLandAire Technologies, Inc. (SLA) proposes to meet this Group 2 need with an electric drive concept that will be capable of a 20-30 lbs. modular payload, with comparable range and endurance to current grp 2 ISR UAS, while still maintaining sufficient ISR capabilities. This UAS concept is able to covertly and safely deliver payload to within 1 meter accuracy, then extract a different payload an ...

    SBIR Phase I 2018 Department of DefenseSpecial Operations Command
  3. Handheld Hidden Chamber Detection

    SBC: TIALINX, INC.            Topic: SOCOM173004

    SOCOM requires rapid implementation and delivery of a handheld, automated hidden chamber sensor system to detect, locate, and discriminate hidden compartments. TiaLinxs Eagle-NC is a wall imager that operates as an ultra-wideband (UWB) radio frequency (RF) sensor integrated with advanced computer vision algorithms, state-of-the-art computer processor technologies, and has a fully integrated displa ...

    SBIR Phase I 2018 Department of DefenseSpecial Operations Command
  4. SUAS Killer, Identifier, and Tracker

    SBC: Physical Optics Corporation            Topic: SOCOM173005

    To address USSOCOMs need for a system to detect, locate, track, and either disable and/or destroy a small unmanned aerial system (SUAS) from a distance, Physical Optics Corporation (POC) proposes to develop the new SUAS Killer, Identifier, and Tracker (SKEET) system. The SKEET system is based on a new design that combines state-of-the-art airspace monitoring technology utilizing compact scanning r ...

    SBIR Phase I 2018 Department of DefenseSpecial Operations Command
  5. 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
  6. Low-Shot Detection in Remote Sensing Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: NGA172002

    Toyon Research Corporation proposes to research and develop algorithms for low-shot object detection, adapting popular techniques to address the complexities inherent in ATR for remote sensing. Traditional object detection algorithms rely on large corpora of data which may not be available for more exotic targets (such as foreign military assets), and therefore, traditional Convolutional Neural Ne ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  7. 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
  8. 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
  9. Improving Uncertainty Estimation with Neural Graphical Models

    SBC: MAYACHITRA, INC.            Topic: NGA181005

    Building interpretable, composable autonomous systems requires consideration of uncertainties in the decisions and detections theygenerate. Human analysts need accurate absolute measures of probability to determine how to interpret and use the sometimes noisy resultsof machine learning systems; and composable autonomous systems need to be able to propagate uncertainties so that later reasoningsyst ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  10. Bayesian Urban Degradation Assessment

    SBC: Intellisense Systems, Inc.            Topic: NGA181004

    To address the NGA need for algorithms that fuse observables from over-flight operations and from ground sources to automatically estimatethe degradation of urban environments due to battle damage or natural disasters, Intellisense Systems, Inc. (ISS) proposes to develop a newBayesian Urban Degradation Assessment (BUDA) software system. It is based on the integration of multiple damage assessment ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
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