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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. 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
  3. 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
  4. Edge Quantum Processor

    SBC: STREAMLINE AUTOMATION LLC            Topic: SOCOM22DST01

    Quantum technology will become a key enabler of future Air Force superiority. Topological insulator (TI) qubits are inherently stable and fault-tolerant because they exploit local topological symmetries and global boundary conditions of chalcogenide materials to yield unique, emergent quantum states. Wake Forest University and Streamline Automation have been working collaboratively for the last se ...

    STTR Phase I 2022 Department of DefenseSpecial Operations Command
  5. 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
  6. SAR AI Training dataset generated using Reification

    SBC: Arete Associates            Topic: DTRA21B001

    The Synthetic Aperture Radar (SAR) Image Generation Data Augmentation (SIGDA) system is achieved using SAR simulators and the Arete’s Reification approach. Large, realistic datasets will be generated using the Arete Reification capability. These large Reified datasets are then used to train machine learning or Artificial Intelligence (AI), Automatic Target Recognition (ATR) classification algori ...

    STTR Phase I 2022 Department of DefenseDefense Threat Reduction Agency
  7. sUAS Munition Teaming for Advanced Precision Strike

    SBC: OPTO-KNOWLEDGE SYSTEMS INC            Topic: SOCOM21C001

    The US requires standoff precision strike capabilities in GPS-denied and high threat environments. This includes fire-and-forget lock-after-launch vision-based guidance for SOPGM. Due to emerging threats, a paradigm shift is occurring in the way we gather intelligence, maintain surveillance, and perform reconnaissance. ISR platforms are evolving, and artificial intelligence is at the forefront of ...

    STTR Phase I 2022 Department of DefenseSpecial Operations Command
  8. sUAS Munition Teaming for Advanced Precision Strike

    SBC: INVARIANT CORPORATION            Topic: SOCOM21C001

    This task seeks to develop advanced teaming via machine learning between small unmanned air systems and Non Line-of-Sight (NLOS) munitions in GPS denied Environments. Current precision targeting capabilities are robust to state errors from ISR targeting platforms and weapons systems seeking to passively acquire a target. Visual Based Navigation (VBN) provides required state information in GPS deni ...

    STTR Phase I 2022 Department of DefenseSpecial Operations Command
  9. Algorithm Performance Evaluation with Low Sample Size

    SBC: SIGNATURE RESEARCH, INC.            Topic: NGA20C001

    The team of Signature Research, Inc. and Michigan Technological University will develop and demonstrate methods and metrics to evaluate the performance of machine learning-based computer vision algorithms with low numbers of samples of labeled EO imagery. We will use the existing xView panchromatic dataset to demonstrate a proof-of-concept set of tools. If successful, in Phase II, we will extend t ...

    STTR Phase I 2021 Department of DefenseNational Geospatial-Intelligence Agency
  10. General Purpose Radiation Detector Front End and Digital Processor

    SBC: H3D INC            Topic: DTRA20B002

    This project aims to create a general-purpose readout architecture that will allow the rapid deployment of next generation detection systems. The system will be based on the development of a programmable general-purpose integrated circuit (GPIC) that has the front-end electronics required to read out signals from a variety of radiation detectors, especially next generation scintillators and semico ...

    STTR Phase I 2021 Department of DefenseDefense Threat Reduction Agency
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