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Award Data
The Award database is continually updated throughout the year. As a result, data for FY23 is not expected to be complete until September, 2024.
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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Faster Optical Modem for Underwater Data Acquisition
SBC: SONALYSTS INC Topic: NGA182001To address NGA’s requirements, Sonalysts’ team of world-class experts in underwater optical communication proposes development and implementation of the Precision Optical Navigation Transceiver for Undersea Systems (PONTUS). PONTUS will transfer navigation information from an Underwater Navigation Beacon (UNB) to an Unmanned Undersea Vehicle (UUV) in an electromagnetic-spectrum-denied (e.g., G ...
SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency -
Faster Optical Modem for Underwater Data Acquisition
SBC: SA PHOTONICS, LLC Topic: NGA182001SA Photonics’ Optical Navigation and Ranging (ONAR) system is an interrogative system that operate underwater in wavelength range of blue/green (450-540 nm) and enables navigational correction to IMU based dead reckoning navigation. The location based beacons are battery operated and have operational life span of over one year. The system is designed to operate in on demand burst mode so that no ...
SBIR Phase I 2019 Department of DefenseNational Geospatial-Intelligence Agency -
Resonant voltage regulator architecture eliminates 30-50% energy consumption of digital ICs
SBC: Empower Semiconductor Incorporated Topic: DEFOA0001736Empower Semiconductor has developed a resonant integrated voltage regulator (IVR) technology that can dramatically improve the energy efficiency of any digital CMOS IC. As Moore’s law has advanced, the plexity and power needs of digital ICs has grown dramatically. The voltages required by these ICs have lowered and the resulting voltage accuracy needs have become more stringent. Existing voltage ...
SBIR Phase I 2018 Department of EnergyARPA-E -
A stable, low cost, low power CO2 sensor for demand controlled ventilation
SBC: Matrix Sensors, Inc. Topic: DEFOA0001738"We propose to develop a stable, low-cost, low power CO2 sensor module that meets the requirements of the ARPA-E SENSORS FOA, namely, 30 ppm precision over a dynamic range of 400 ppm to 2000 ppm with 10 ppm drift per year. Existing optical non-dispersive infrared (NDIR) CO2 sensors simply cannot scale to the cost and power requirements of the FOA. We therefore propose a solid state architecture ...
SBIR Phase I 2018 Department of EnergyARPA-E -
Low-Shot Detection in Remote Sensing Imagery
SBC: TOYON RESEARCH CORPORATION Topic: NGA172002Toyon Research Corporation proposes to research and develop algorithms for low-shot object detection, adapting popular techniques to address the complexities inherent in ATR for remote sensing. Traditional object detection algorithms rely on large corpora of data which may not be available for more exotic targets (such as foreign military assets), and therefore, traditional Convolutional Neural Ne ...
SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency -
Blending Ground View and Overhead Models
SBC: Arete Associates Topic: NGA181008We propose to build ARGON, the ARet Ground-to-Overhead Network. The network will ingest analyst-supplied ground-level imagery ofobjects and retrieve instances of those objects in overhead collections, providing tips back to the analysts. A proprietary method of trainingthe network, leveraging in-house capabilities, data sources, and tools, will be critical to its success. During Phase I, we will p ...
SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency -
Improving Uncertainty Estimation with Neural Graphical Models
SBC: MAYACHITRA, INC. Topic: NGA181005Building interpretable, composable autonomous systems requires consideration of uncertainties in the decisions and detections theygenerate. Human analysts need accurate absolute measures of probability to determine how to interpret and use the sometimes noisy resultsof machine learning systems; and composable autonomous systems need to be able to propagate uncertainties so that later reasoningsyst ...
SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency -
Low-Shot Detection in Remote Sensing Imagery
SBC: TOYON RESEARCH CORPORATION Topic: NGA181010The National Geospatial-Intelligence Agency (NGA) ingests and analyzes raw imagery from multiple sources to form actionable intelligenceproducts that can be disseminated across the intelligence community (IC). To effectively meet these demands NGA must continue to improveits automated and semi-automated methods for target detection and classification. Of particular concern is furthering NGA's abil ...
SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency -
Automated Assessment of Urban Environment Degradation for Disaster Relief andReconstruction
SBC: TOYON RESEARCH CORPORATION Topic: NGA181004Toyon Research Corp. proposes development of a system that automates disaster assessment based on fusion of overhead and ground-basedimages, video, and other data. In Phase I, we will investigate various possible data sources and the benefits of fusing the data in automatedanalysis. We will select and curate data for processing in a Phase I feasibility study. Damage assessment will be performed in ...
SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency -
Generalized Change Detection to Cue Regions of Interest
SBC: TOYON RESEARCH CORPORATION Topic: NGA181006Toyon Research Corporation proposes to research and develop algorithms for generalized change detection, by leveraging and exploringexisting and proven effective traditional and deep learning methods, with a unique 3D reconstruction component. The vast majority of themassive amounts of imagery data will have small pixel level differences due to a multitude of unimportant changes: minor misregistra ...
SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency