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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. Hybrid Machine Learning Approaches for Radiation Signature Identification

    SBC: Radiation Monitoring Devices, Inc.            Topic: NGA192002

    To improve the identification and detection of radio-logical materials, we propose a hybrid supervised learning and unsupervised machine learning approach to reduce the false positive rate, increase the accuracy and throughput, and augment the capabilities of the human operators. At the end of the Phase I, we will have a machine learning algorithm that is trained to recognize a variety of nuclear ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  2. Novel Algorithms for Remote Nuclear Detection and Classification

    SBC: APPLIED RESEARCH LLC            Topic: NGA192002

    We propose a novel software system for remote nuclear detection and classification. First, we propose to apply GADRAS or GEANT4 to generate training data for different combinations of nuclear materials and detectors. The training data contain spectral shapes of different detector responses. Second, we propose to use a Continuous Wavelet Transform (CWT) based technique for peak detection in spectr ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  3. Radioactive Anomaly Detection and Identification Algorithm Suite (RADIAS) for Enhanced Radiological Search

    SBC: Physical Sciences Inc.            Topic: NGA192002

    Physical Sciences Inc. (PSI) proposes to develop an advanced machine learning (ML) algorithm to detect threat-based anomalies in gamma-ray spectra in real-time. If a network of distributed R/N sensors is employed, the algorithms will also be capable of tracking such anomalies through the network. The Radiation Anomaly Detector (RAD) will be packaged with PSI’s award winning Poisson Clutter Spli ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  4. High Efficiency Semiconductors for Nuclear Detection

    SBC: Radiation Monitoring Devices, Inc.            Topic: NGA192003

    There is a need for low cost, high performance gamma-ray detectors for national and homeland security applications for detection, identification and localization of special nuclear materials. Common detectors used in this application include scintillators coupled to photomultiplier tubes or silicon photodiodes, and semiconductor detectors like cadmium zinc telluride. Semiconductor detector offer ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  5. Perovskite-based Multi-modal Detector and Imaging System

    SBC: CapeSym, Inc            Topic: NGA192003

    Perovskites are rapidly emerging as attractive radiation detectors. The goal of this program is to develop a multi-modal detection sensor based on perovskite materials. The developed detector will be integrated with a high resolution active pixel array and the pixel signals will be processed by high speed electronics to create scene images of different radiation types.

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  6. Deep Reinforcement Learning for High-fidelity Vehicle Motion Simulation

    SBC: INTELLIGENT AUTOMATION, INC.            Topic: NGA192004

    NGA seeks to incorporate Artificial Intelligence (AI) and Machine Learning (ML) to Intelligence, Surveillance and Reconnaissance (ISR) missions in the aim to capture fleeting targets, thus a large amount of dynamic scenes with accurate target motions and behaviors will be needed for training and performance evaluation. Traditional microscopic model based approach for vehicle activity simulation i ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  7. Long-term Patterns of Life from Sporadic Observations

    SBC: Novateur Research Solutions, LLC            Topic: NGA192005

    This SBIR Phase-I project will develop novel approaches for unsupervised life-long learning architecture to extract long-term patterns of life from sporadic observations. The proposed architecture models longterm dependencies, learns associations between features from sensory observations and the contexts in which these features occur, identifies sudden changes in the patterns for anomaly detecti ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  8. GLANCE: Graph Learned Anomalies in Changing Environments

    SBC: Expedition Technology, Inc.            Topic: NGA192005

    While Wide Area Motion Imagery (WAMI) activity analysis has considerably advanced for continuous tracking—particular through adoption of improved machine learning (ML) methods, less research has been applied to using similar quality image sequences over sporadic intervals to characterize site activity. While pure still-image remote sensing collection can characterize basic change detection usin ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  9. Automated Assessment of Urban Environment Degradation for Disaster Relief and Reconstruction

    SBC: TOYON RESEARCH CORPORATION            Topic: NGA181004

    Toyon Research Corp. proposes development of a software prototype for automated 3D damage assessment in urban environments. We will document the requirements for the algorithms and software, and optimize or extend the algorithms as needed. We will implement the software prototype, leveraging and integrating existing components where possible and implementing new components as needed. We will qua ...

    SBIR Phase II 2020 Department of DefenseNational Geospatial-Intelligence Agency
  10. Low-shot Using Contextual Knowledge and Ephemeral Search Hierarchy for One-shot Targets (LUCK E SHOT) in Remote Sensing Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: NGA181010

    At a high level, the primary technical objective of the proposed effort is to Identify, obtain, evaluate, and improve datasets; Investigate the use of Meta-Learning to improve the low-shot detection performance of the feed forward network;   Extend research in multi-branch multi-domain embedding networks and semantic layouts for low-shot detection in remote sensing imagery; and Develop and implem ...

    SBIR Phase II 2020 Department of DefenseNational Geospatial-Intelligence Agency
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