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The Award database is continually updated throughout the year. As a result, data for FY21 is not expected to be complete until September, 2022.

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.

  1. Lightweight, Stable Optical Bench with Integrated Vibration Attenuation

    SBC: SAN DIEGO COMPOSITES, INC.            Topic: MDA13T007

    The goal of this program is to design a lightweight optical bench capable of remaining stable under temperature and moisture changes, while isolating the precision optical array from vibrations such as engine noise and air turbulence. By integrating a customizable periodic stack in the bench, vibrations are attenuated more effectively than commercially available mounts. Additionally, the periodic ...

    STTR Phase II 2016 Department of DefenseMissile Defense Agency
  2. Using Magnetic Levitation for Non-Destructive Detection of Defective and Counterfeit Materiel

    SBC: Nano Terra, Inc.            Topic: DLA15C001

    The introduction of substandard or counterfeit materials into the DoD supply chain can have extremely expensive, and potentially life threatening, consequences. Current techniques used to detect nonconforming materiel can be destructive (e.g., manual sectioning and inspection of a part), time consuming and expensive (e.g., micro-computed tomography, ultrasound), or provide only limited informatio ...

    STTR Phase I 2016 Department of DefenseDefense Logistics Agency
  3. Deep Inference and Fusion Framework Utilizing Supporting Evidence (DIFFUSE)

    SBC: Boston Fusion Corp.            Topic: MDA15T001

    Combining information from disparate sensors can lead to better situational awareness and improved inference performance; unfortunately, traditional multi-sensor fusion cannot capture complex dependencies among different objects in a scene, nor can it exploit context to further boost performance. Integrating context information within a fusion architecture to reason cohesively about scenes of inte ...

    STTR Phase I 2016 Department of DefenseMissile Defense Agency
  4. Robust Classification through Deep Learning and Dynamic Multi-Entity Bayesian Reasoning

    SBC: ExoAnalytic Solutions, Inc.            Topic: MDA15T001

    Missile defense faces the challenges of rapidly maturing and evolving complex threats, possessing capabilities which require the use of all available resources to successfully detect, track and identify the lethal objects. Future performance will rely on multiple sensors such as ground and sea based radars and electro-optical and infrared sensors for target recognition. It is crucial to develop a ...

    STTR Phase I 2016 Department of DefenseMissile Defense Agency
  5. Decision Making under Uncertainty

    SBC: GCAS, Inc.            Topic: MDA13T001

    Our proposed second order uncertainty (SOU) product is a decision making software solution that addresses the problem of providing accurate and precisely defined decision courses of action (COAs) of complex, time-constrained problems in a fraction of the time required by alternative methods striving to achieve the same level of precision. Complex decision situations can deal with large volume of ...

    STTR Phase II 2016 Department of DefenseMissile Defense Agency
  6. Real-Time Health Management Portable Sensor for Solid Rocket Motors

    SBC: Physical Sciences Inc.            Topic: MDA14T004

    Physical Sciences Inc. (PSI) proposes to design, develop, and demonstrate a portable, non-invasive, real-time sensor to assess the chemical and physical health of solid rocket motors as a function of age without affecting the motors integrity nor having direct contact with the propellant. In Phase II, a sensor to monitor specific gas species that are markers of the chemical and mechanical aging pr ...

    STTR Phase II 2016 Department of DefenseMissile Defense Agency
  7. Interactive Sensor Fusion for Context-Aware Discrimination

    SBC: Opto-knowledge Systems, Inc.            Topic: MDA15T001

    We propose a novel computational framework for discrimination that incorporates sensor data from observations of the engagement and from kill assessment (KA) that such sensors can provide. The KA information is combined with data from other sensors to improve the discrimination decision and to reduce the probability of correlated shots. Approved for Public Release 16-MDA-8620 (1 April 16)

    STTR Phase I 2016 Department of DefenseMissile Defense Agency
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