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The Award database is continually updated throughout the year. As a result, data for FY22 is not expected to be complete until September, 2023.
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A data dictionary and additional information is located on the Data Resource Page. Files are refreshed monthly.
Gold-Contaminated Solder-Joint Characterization for Quantifying Risk Associated with Gold EmbrittlementSBC: Enig Associates, Inc. Topic: MDA15T005
Circuit card assembly (CCA) reliability is dependent on solder joints, which join components to printed circuit boards (PCBs). Board users strive to mitigate risks associated with gold-embrittled solder joints. Enig Associates, Inc. (ENIG), in collaboration with Sandia National Laboratories, proposes to develop a risk-forecasting tool for quantifying the risks associated with gold-embrittled sold ...STTR Phase I 2016 Department of DefenseMissile Defense Agency
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
Robust Classification through Deep Learning and Dynamic Multi-Entity Bayesian ReasoningSBC: 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
Interactive Sensor Fusion for Context-Aware DiscriminationSBC: 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