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Award Data

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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 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 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.

Displaying 202161 - 202170 of 207728 results
  1. Software and Sensor System for Autonomous Rendezvous Adaptive Docking Solution “ARADS”


    ExLabs is developing its Arachne system as the next generation of spacecraft for RPO capture and redirection mission profiles for LEO debris mitigation, defense purposes, and station keeping, with long term pursuit of NEO Asteroid capture for return to Lu

    SBIR Phase I 2023 Department of DefenseAir Force
  2. Rapid, High Throughput Production of SiC Yarn from Discontinuous Fibers

    SBC: TEXAS RESEARCH INSTITUTE , AUSTIN, INC.            Topic: N231072

    Texas Research Institute Austin, Inc. will demonstrate that the coupling of centrifugal spinning and microwave irradiation followed by mechanical sonication will yield short fibers of sufficient diameter, length, and thermomechanical performance to be spun into a high quality yarn in the Phase I Effort. The quality and performance of the proposed beta silicon carbide (ß-SiC) yarns will be compare ...

    SBIR Phase I 2023 Department of DefenseNavy
  3. CBM+ and RBM with an Energy Management Approach

    SBC: MAPLEWELL INC            Topic: N211074

    By adapting energy models from the utility industry, NAVSEA can create scalable energy models and meet CBM and RBM objectives. Maplewell’s Energy Management Control System (EMCS) monitors energy consumption and production, and energy models capture mechanical, electrical, material, and chemical problems and diagnose the condition of an asset or system. The EMCS harnesses energy models to monitor ...

    SBIR Phase II 2023 Department of DefenseNavy
  4. Nondestructive Detection of Flaws through Thick Polymers using Electromagnetic Imaging Technologies

    SBC: TEXAS RESEARCH INSTITUTE , AUSTIN, INC.            Topic: N211062

    TRI Austin, Iowa State University, and AVID R&D propose to develop a real-time, high resolution, microwave frequency imaging system for the inspection of the surface of a metallic substrate under thick polymer coatings. The imaging system will use synthetic aperture radar (SAR) imaging algorithms to generate high resolution images from raw microwave inspection data. Polymer specimens and metal sub ...

    SBIR Phase II 2023 Department of DefenseNavy
  5. Tough, Ultra-Rapid Restructuring Marine Coatings with Broad-Spectrum Anti-Fouling Behavior

    SBC: TEXAS RESEARCH INSTITUTE , AUSTIN, INC.            Topic: N212120

    The Navy currently has a need for passive solutions to mitigate biofouling on ship hulls that are not externally activated and do not leach harmful biocides into the environment. The fouling of ship hulls is a serious and persistent problem that increases drag, which leads to increased fuel consumption. This necessitates periodic maintenance whereby the ships are docked and their hulls are scrubbe ...

    SBIR Phase II 2023 Department of DefenseNavy
  6. Digital Engineering For Nuclear, Environmental, and Signal Integrity Performance of the Next Generation Hardened Connector

    SBC: Electro Magnetic Applications, Inc.            Topic: N211096

    Navy boost vehicles require hardening and operate-through capability to nuclear electromagnetic pulse (EMP) effects. The Navy requires a new generation of cable harness Electrical Wiring Interconnect System (EWIS) connectors and cables designed to perform in the EMP environment. The requirements include protection from System Generated Electromagnetic pulse (SGEMP) and High-altitude Electromagneti ...

    SBIR Phase II 2023 Department of DefenseNavy
  7. Electromagnetic Interactions Between Cables, Antennas, and Their Environments

    SBC: Electro Magnetic Applications, Inc.            Topic: N221015

    Navy aircraft use complex cable harnesses to transfer data and provide power to avionics, weapons, sensors, control surfaces, and landing gear. The cable harnesses and the aircraft must be designed so that interference to an electronics unit does not occur. Design approaches must address the cable harness packing, separation, shielding, and layout to avoid crosstalk between conductors within an in ...

    SBIR Phase II 2023 Department of DefenseNavy
  8. CHATMAN Phase II

    SBC: Stratagem Group, Inc., The            Topic: OSD221001

    Reducing the False Alarm Rate (FAR) of Automated Target Recognition (ATR) algorithms for Synthetic Aperture Radar (SAR) imagery is crucial for Intelligence, Surveillance, Reconnaissance (ISR) and precision target engagement missions. While modern Deep Learning (DL) ATR networks have demonstrated advanced predictive capabilities and generalization for SAR imagery, they lack spatial awareness, resul ...

    SBIR Phase II 2023 Department of DefenseNational Geospatial-Intelligence Agency
  9. Development of EMI Enhanced Concrete for EMP and Blast Protection


    In this work, Texas Research Institute (TRI) Austin, Inc, South Dakota School of Mines and Technology, and Missouri University of Science and Technology will demonstrate the use of graphene and spiral steel embedment as strength reinforcement and electromagnetic interference/pulse protection for concrete.  Research will be performed as to the best method(s) for embedment of the materials of inter ...

    SBIR Phase I 2023 Department of DefenseDefense Threat Reduction Agency
  10. X DRLSGT

    SBC: Cynnovative, LLC            Topic: MDA22T004

    Cynnovative proposes Explainable Deep Reinforcement Learning with Symbolically Guided Transitions (X DRLSGT) to improve the transparency and, thus, the explainability of deep reinforcement learning (DRL) algorithms. The inability to understand the reasoning behind an Artificial Intelligence’s (AI) decision is a major limiting factor that prevents AI-enabled physical systems from being deployed a ...

    STTR Phase I 2023 Department of DefenseMissile Defense Agency
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