You are here
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.
-
General Purpose Radiation Detector Front End and Digital Processor
SBC: H3D INC Topic: DTRA20B002This project aims to create a general-purpose readout architecture that will allow the rapid deployment of next generation detection systems. The system will be based on the development of a programmable general-purpose integrated circuit (GPIC) that has the front-end electronics required to read out signals from a variety of radiation detectors, especially next generation scintillators and semico ...
STTR Phase I 2021 Department of DefenseDefense Threat Reduction Agency -
Integrated Circuits
SBC: NU-TREK, INC. Topic: DTRA20B002The Nu-Trek team is proposing to develop µDet, a low Size, Weight, and Power (SWaP) read out integrated circuit (IC) for gamma and neuron detectors. µDet offers pulse shape digitization, which in turn enables gamma-neutron discrimination. This is a game changing capability that brings laboratory-level functionality to the field. In Phase I the Nu-Trek Team will develop a baseline design for the ...
STTR Phase I 2021 Department of DefenseDefense Threat Reduction Agency -
SAR AI Training dataset generated using Reification
SBC: Arete Associates Topic: DTRA21B001The Synthetic Aperture Radar (SAR) Image Generation Data Augmentation (SIGDA) system is achieved using SAR simulators and the Arete’s Reification approach. Large, realistic datasets will be generated using the Arete Reification capability. These large Reified datasets are then used to train machine learning or Artificial Intelligence (AI), Automatic Target Recognition (ATR) classification algori ...
STTR Phase I 2022 Department of DefenseDefense Threat Reduction Agency -
Multimode Organic Scintillators for Neutron/Gamma Detection
SBC: RADIATION MONITORING DEVICES, INC. Topic: DTRA19B003There is significant interest in multi-functional materials enabling gamma-ray spectroscopy, neutron/gamma pulse shape discrimination (PSD), ultra-fast response, and time-of-flight (TOF) neutron detection. These materials would be used in a variety of mission scenarios for the localization and monitoring of special nuclear materials. Commercial inorganic scintillators offer some of these character ...
STTR Phase I 2020 Department of DefenseDefense Threat Reduction Agency -
Wide Area Distributed Algorithms for Cooperative Source Identification, Characterization, and Localization
SBC: THE PROBITAS PROJECT, INC. Topic: DTRA21B003Current radiation detection algorithms are based on the concept that each detector operates independently. The Probitas Project, Inc. (Probitas) and the Lawrence Berkeley National Laboratory (LBNL) propose to show the benefits of data fusion to improve the identification, localization, and characterization of a radioactive source in a complex scene as compared to a singular detector algorithm. We ...
STTR Phase I 2022 Department of DefenseDefense Threat Reduction Agency -
Generative Modeling of Multispectral Satellite Imagery
SBC: NOVATEUR RESEARCH SOLUTIONS LLC Topic: DTRA22D001This STTR Phase I project proposes novel deep learning models for generating realistic multi-spectral remote sensing imagery, specifically in the infrared (IR) and near-infrared (NIR) bands. The proposed system enables synthesis of semantically realistic imagery and provides parametric control of synthesizing objects-of-interest, type of terrain and land cover, time or season, weather, cloud cover ...
STTR Phase I 2023 Department of DefenseDefense Threat Reduction Agency -
Numerics-Informed Neural Networks (NINNs)
SBC: KARAGOZIAN & CASE, INC. Topic: DTRA21B002The overall goal is to develop numerics-informed neural networks (NINNs) and DeepOnets for chemical reactions and for PDEs with spatial derivatives improve the computational efficiency of the chemical kinetics models for chemical weapon agents and simulants. Based on the first NINN developed by the Karniadakis’s group in 2018, which blends the multi-step time-stepping with deep neural networks, ...
STTR Phase I 2022 Department of DefenseDefense Threat Reduction Agency -
Generative Modeling of Multispectral Satellite Imagery
SBC: Applied Research In Acoustics LLC Topic: DTRA22D001To address the challenge DTRA faces in identifying rare objects of interest to defeat improvised threat networks using multispectral imagery, small business ARiA and research institution Michigan Technological University (MTU) will develop and demonstrate the feasibility of the Generative Augmentation Process (GAP). The Phase I effort will (1) conduct a proof-of-concept study for GAP by developing ...
STTR Phase I 2023 Department of DefenseDefense Threat Reduction Agency -
Rugged Ultrafast Radiation Hard Scintillators for Nuclear Battlefield
SBC: CAPESYM INC Topic: DTRA20B001Scintillator radiation detectors offer high sensitivity and relatively accurate radionuclide detection at a reasonable price. However, most of the available commercial scintillators have long decay times ranging from hundreds of nanoseconds to tens of microseconds. As a result, they have limited performance in high dose rate environments such as nuclear battlefields, robotic nuclear weapons test s ...
STTR Phase I 2021 Department of DefenseDefense Threat Reduction Agency -
Numerically Inspired Deep Neural Nets for Chemically Reacting Flows
SBC: APPLIED SIMULATIONS INC Topic: DTRA21B002The project will develop numerically inspired deep neural nets (NINNs) in order to replace the stiff ordinary differential equation (SODE) solvers currently being used to integrate chemical species in high-fidelity computational fluid dynamics simulations. Unlike traditional deep neural nets, the architectures and optimization strategies used to learn the physics of a problem will be based on the ...
STTR Phase I 2022 Department of DefenseDefense Threat Reduction Agency