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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. AI/ML Aided Aviation Sensors for Cognitive and Decision Optimization

    SBC: KRTKL INC.            Topic: SOCOM23B001

    krtkl (“critical”) will conduct a Phase I Feasibility Study to identify the best approach for reducing aviator cognitive load by optimizing information delivery and decision-making based on a thorough analysis of existing platforms, sensors, data sources, and onboard compute resources. This information will be used to identify Artificial Intelligence and Machine Learning based algorithms for p ...

    STTR Phase I 2023 Department of DefenseSpecial Operations Command
  2. AI/ML Aided Aviation Sensors for Cognitive and Decision Optimization

    SBC: Mente Systems Inc.            Topic: SOCOM23B001

    Sensor systems aboard aircrafts address unique problems and are siloed in their objectives. A data silo is a term used to describe a data system that is insulated from other data systems. While keeping information categorized may lead to easier organization, the costs often outweigh the benefits. In aviation systems, data silos often lead to miscommunication, cognitive overload, and waste. These d ...

    STTR Phase I 2023 Department of DefenseSpecial Operations Command
  3. AI/ML Aided Aviation Sensors for Cognitive and Decision Optimization

    SBC: XR 2 LEAD LLC            Topic: SOCOM23B001

    In manned aviation environments – both military and commercial – AI support is being developed and researched for ground-based planning and operational decision support. Using AI in real-time with crews poses additional questions and issues. This research will provide the means to understand the measures and requirements to architect potential AI-Agent solutions before implementation. This fea ...

    STTR Phase I 2023 Department of DefenseSpecial Operations Command
  4. Generative Modeling of Multispectral Satellite Imagery

    SBC: Applied Research In Acoustics LLC            Topic: DTRA22D001

    To address the challenge DTRA faces in identifying rare objects of interest to defeat improvised threat networks using multispectral imagery, small business ARiA and research institution Michigan Technological University (MTU) will develop and demonstrate the feasibility of the Generative Augmentation Process (GAP). The Phase I effort will (1) conduct a proof-of-concept study for GAP by developing ...

    STTR Phase I 2023 Department of DefenseDefense Threat Reduction Agency
  5. Generative Modeling of Multispectral Satellite Imagery

    SBC: NOVATEUR RESEARCH SOLUTIONS LLC            Topic: DTRA22D001

    This STTR Phase I project proposes novel deep learning models for generating realistic multi-spectral remote sensing imagery, specifically in the infrared (IR) and near-infrared (NIR) bands. The proposed system enables synthesis of semantically realistic imagery and provides parametric control of synthesizing objects-of-interest, type of terrain and land cover, time or season, weather, cloud cover ...

    STTR Phase I 2023 Department of DefenseDefense Threat Reduction Agency
  6. Wide Area Distributed Algorithms for Cooperative Source Identification, Characterization, and Localization

    SBC: THE PROBITAS PROJECT, INC.            Topic: DTRA21B003

    Current radiation detection algorithms are based on the concept that each detector operates independently. The Probitas Project, Inc. (Probitas) and the Lawrence Berkeley National Laboratory (LBNL) propose to show the benefits of data fusion to improve the identification, localization, and characterization of a radioactive source in a complex scene as compared to a singular detector algorithm. We ...

    STTR Phase I 2022 Department of DefenseDefense Threat Reduction Agency
  7. Multi-Dimensional Event Sourcing & Correlation- Publicly Available Information (PAI) (MDESC-P)

    SBC: PROGRAMS MANAGEMENT ANALYTICS & TECHNOLOGIES INC            Topic: SOCOM22DST01

    Multi-Dimensional Event Sourcing & Correlation - Publicly Available Information (PAI) (MDESC-P) will support collection jointly across disparate PAI sources with coordinated cueing of more constrained intelligence, surveillance, target acquisition, and reconnaissance (ISTAR) sources. The primary objective for MDESC-P is to deliver a scalable and automated PAI collection management solution using a ...

    STTR Phase I 2022 Department of DefenseSpecial Operations Command
  8. Population Behavioral Analysis at Scale, AOR Modeling

    SBC: DEEP LABS INC            Topic: SOCOM22DST01

    Deep Labs recognizes USSOCOM’s challenge to process multiple data and communications inputs for optimized decision making, and to support rapid on-the-move abilities to learn and communicate knowledge to enhance tactically relevant situational awareness in peer/near peer environments. Deep Labs has proven this capability across complex challenges in the world’s largest commercial enterprises a ...

    STTR Phase I 2022 Department of DefenseSpecial Operations Command
  9. RAZORFISH: Real-time Augmented ZYX-aligned Operator RF/EM Integrated Scene for Hypercognition

    SBC: KNOWMADICS, INC.            Topic: SOCOM22DST01

    As electronic warfare (EW) permeates down to the small unit operations (e.g., with the proliferation of IoT devices, 5G, demand for multidomain spectrum management, and adversaries who can leverage or attack these components with malice), there is a need to bolster Operator's shared situation awareness (SSA) of the EW space that overlays the physical battlefield to enable a small unit multidomain ...

    STTR Phase I 2022 Department of DefenseSpecial Operations Command
  10. Numerics-Informed Neural Networks (NINNs)

    SBC: KARAGOZIAN & CASE, INC.            Topic: DTRA21B002

    The overall goal is to develop numerics-informed neural networks (NINNs) and DeepOnets for chemical reactions and for PDEs with spatial derivatives improve the computational efficiency of the chemical kinetics models for chemical weapon agents and simulants. Based on the first NINN developed by the Karniadakis’s group in 2018, which blends the multi-step time-stepping with deep neural networks, ...

    STTR Phase I 2022 Department of DefenseDefense Threat Reduction Agency
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