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Award Data
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.
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Development of powder bed printing (3DP) for rapid and flexible fabrication of energetic material payloads and munitions
SBC: MAKEL ENGINEERING, INC. Topic: DTRA16A001This program will demonstrate how additive manufacturing technologies can be used with reactive and high energy materials to create rapid and flexible fabrication of payload and munitions. Our primary approach to this problem will be to use powder bed binder printing techniques to print reactive structures. The anticipated feedstock will consist of composite particles containing all reactant spe ...
STTR Phase I 2016 Department of DefenseDefense Threat Reduction Agency -
Modular Pulse Charger and Laser Triggering System for Large-Scale EMP and HPM Applications
SBC: SCIENTIFIC APPLICATIONS & RESEARCH ASSOCIATES, INC. Topic: DTRA16A004For effective protection against EMP and HPM threats, it is important to understand the physics of the threats, and also to quantify the effects they have on electrical systems. EMP and HPM vulnerability testing requires delivery of high peak power and electric fields to distant targets. The most practical solution to simulate such environments is to develop a modular, optically-isolated MV-antenn ...
STTR Phase I 2016 Department of DefenseDefense Threat Reduction Agency -
Interactive Sensor Fusion for Context-Aware Discrimination
SBC: OPTO-KNOWLEDGE SYSTEMS INC Topic: MDA15T001We 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 -
Robust Classification through Deep Learning and Dynamic Multi-Entity Bayesian Reasoning
SBC: EXOANALYTIC SOLUTIONS INC Topic: MDA15T001Missile 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