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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: PARRY LABS, LLC            Topic: SOCOM23B001

    Existing airborne defense systems integrate a wide variety of sensors necessary to provide operators with situational awareness across the visual, thermal, signals, and electromagnetic spectrums. To date, individual sensor systems have been largely stove-piped, as have Artificial Intelligence/Machine Learning (AI/ML) and advanced, Size, Weight, and Power (SWaP)-optimized data processing systems. T ...

    STTR Phase I 2023 Department of DefenseSpecial Operations Command
  3. Low Cost Imaging In The mm Wave Region Using Plasma Waves in High Mobility Transistor

    SBC: BRIMROSE TECHNOLOGY CORP            Topic: CBD22BT001

    In this work, we propose to develop low-cost, high sensitivity high electron mobility transistor-based W-band millimeter wave focal plane array/camera based on mature ternary III-V epitaxial materials of InAlAs on top of InP substrate. The plasma-wave detector uses well established mature technology of high electron mobility transistors which allows future integration and reduces cost. The detecto ...

    STTR Phase I 2023 Department of DefenseOffice for Chemical and Biological Defense
  4. Low Cost W-Band Imaging Array

    SBC: MILLIMETER WAVE SYSTEMS LLC            Topic: CBD22BT001

    Low-cost systems operating at video rates within W-band have remained elusive – especially for stand-off and remote applications. Real time video rate imaging requires parallel detection modalities that traditionally led to high costs and calibration challenges. Substantial advances in low-cost packaging and chip-level integration driven by commercial millimeter-wave applications can now be appl ...

    STTR Phase I 2023 Department of DefenseOffice for Chemical and Biological Defense
  5. 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
  6. 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
  7. Self-Supervised Training in Geospatial Applications with a Robust Hierarchical Vision Transformer (STAR)

    SBC: UNIVERSITY TECHNICAL SERVICES, INC.            Topic: OSD22A001

    Satellite Imagery in Geospatial Intelligence (GEOINT), in conjunction with imagery intelligence (IMINT), geospatial information, and other means of gaining intelligence, has greatly improved the potential of the warfighter and decision makers enabling them to gain a more comprehensive perspective, an in-depth understanding, and a cross-functional awareness of the operational environment. The Artif ...

    STTR Phase I 2022 Department of DefenseNational Geospatial-Intelligence Agency
  8. sUAS Munition Teaming for Advanced Precision Strike

    SBC: CHARLES RIVER ANALYTICS, INC.            Topic: SOCOM21C001

    Precision-guided munitions have demonstrated dramatic effects with minimal collateral damage. New technology developed specifically to deny them accurate guidance information is now feasible, even for non-traditional adversaries. Further, digital communications are flooding the air with signals that interfere with communications many guidance methods rely on. Swarms of small, covert small Uncrewed ...

    STTR Phase I 2022 Department of DefenseSpecial Operations Command
  9. 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
  10. Bounding generalization risk for Deep Neural Networks

    SBC: Euler Scientific            Topic: NGA20A001

    Deep Convolutional Neural Networks (DCNNs) have become ubiquitous in the analysis of large datasets with geometric symmetries. These datasets are common in medicine, science, intelligence, autonomous driving and industry. While analysis based on DCNNs have proven powerful, uncertainty estimation for such analyses has required sophisticated empirical studies. This has negatively impacted the effect ...

    STTR Phase II 2022 Department of DefenseNational Geospatial-Intelligence Agency
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