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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. Low Cost Carbon-Carbon Development for Hypersonic Flight Systems

    SBC: M4 ENGINEERING, INC.            Topic: MDA22T013

    The innovation proposed here is a novel carbon-carbon composite (CCC) manufacturing method based on polymer infusion and polymerization (PIP) using a novel precursor polymer with exceptionally high char yields. This results in a material that has the promise of excellent quality and mechanical properties, while offering the breakthrough advantages of (1) greatly reduced or eliminated need for back ...

    STTR Phase I 2023 Department of DefenseMissile Defense Agency
  2. 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
  3. Paratemporal Simulation with Uncertainty Quantification

    SBC: WARPIV TECHNOLOGIES, INC.            Topic: MDA22T001

    This topic identifies the need creating a Modeling and Simulation (M&S) development and execution environment that significantly decreases the time to execute statistically significant batches of stochastic simulation runs for the purpose of estimating scenario output and outcome distributions while improving statistical knowledge of the outcome distributions. The strategy sought by this topic is ...

    STTR Phase I 2023 Department of DefenseMissile Defense Agency
  4. Recommendation Technology for Digital Engineering Artifacts

    SBC: NOU SYSTEMS INC            Topic: MDA22T002

    Utilizing our current applications and data models supporting the MDS digital test planning and anlaysis ecosystem (currently deployed to MDA's EWS system), the nSI team will develop methods for artifact recommendations for system engineering data to aid MDS personnel in finding data relevant to their tasks. The team will extend our current data model (currently supporting the digital M&S) and tag ...

    STTR Phase I 2023 Department of DefenseMissile Defense Agency
  5. Deep Reinforcement Learning (DRL) Enabled Warfighter Assistant

    SBC: NOU SYSTEMS INC            Topic: MDA22T004

    State spaces are enormous, the operation is real time, the outcomes are uncertain, the consequences of suboptimal actions are dire. The BMDS is a carefully crafted system with many moving parts. As with all complicated systems, there are many tradeoffs to make when operating them. In a world with finite resources, decisions on how to allocate interceptors during a raid could be the difference betw ...

    STTR Phase I 2023 Department of DefenseMissile Defense Agency
  6. Reactive Jet Interactions with Multifidelity Turbulence and Tailored Finite-Rate Combustion Modeling

    SBC: ATA ENGINEERING, INC.            Topic: MDA22T005

    To advance simulation techniques, such as high-fidelity computational fluid dynamics (CFD), to accelerate maturation of DACS design through design-time trade studies, there is a need for new, test-validated models that improve both computational performance and the accuracy of the reacting jet in hypersonic crossflow simulations. ATA and CUBRC (a research institution with leading expertise in aero ...

    STTR Phase I 2023 Department of DefenseMissile Defense Agency
  7. 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
  8. 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
  9. Artificial Intelligence-based Recommender for Model-Based Systems Engineering (ARMS)

    SBC: NEXCEPTA INC            Topic: MDA22T002

    There is a critical need for innovative recommendation technology for digital data engineering artifacts and tools. To address this need, we propose design and implement the Artificial Intelligence-based Recommender System for Model-Based Systems Engineering (ARMS) software as a user-personalized context-aware content recommender system solution for Model Based Systems Engineering (MBSE) applicati ...

    STTR Phase I 2023 Department of DefenseMissile Defense Agency
  10. Deep Reinforcement Learning Enabled Warfighter Assistant (DICE)

    SBC: NEXCEPTA INC            Topic: MDA22T004

    Deep reinforcement learning (DRL) provides strong capabilities to make decisions under uncertain and complicated scenarios. However, there is a lack of explainability of DRL when applied to assist warfighters in training and operational scenarios. To address this critical need, we propose to develop Deep Reinforcement Learning Enabled Warfighter Assistant (DICE). The key innovation of DICE is util ...

    STTR Phase I 2023 Department of DefenseMissile Defense Agency
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