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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.

  1. (7) Navy Technology Acceleration- Integration of Automatic Dependent Surveillance

    SBC: SKYWARD, LTD.            Topic: N193A01

    Extracting patterns from Automatic Dependent Surveillance-Broadcast (ADS-B) data to identify air corridors and detect anomalous behaviors could provide crucial information for both commercial and military applications. Historically, pattern recognition and anomaly detection were dependent on statistical analysis. Patterns were defined as statistical models and anomalies were defined as outliers. A ...

    SBIR Phase I 2020 Department of DefenseNavy
  2. NAVY TECHNOLOGY ACCELERATION (2)-On-board Real-time Situational Awareness (ORSA)

    SBC: PATHFINDER SYSTEMS INC            Topic: N193A01

    Pathfinder Systems proposes a combination of deep and shallow neural nets to provide autonomous situation recognition and appropriate response by Unmanned Air Systems while in flight.

    SBIR Phase I 2020 Department of DefenseNavy
  3. (7) Aircraft Intent Inference based on Real-Time ADS-B Data Processing

    SBC: THE INNOVATION LABORATORY, INC.            Topic: N193A01

    In this Phase I SBIR effort, The Innovation Laboratory, Inc. (TIL) proposes to deliver Artificial Intelligence (AI)/Machine Learning (ML) capabilities to autonomously characterize aircraft intent based on real-time Automatic Dependent Surveillance – Broadcast (ADS-B) data. In Phase I, dozens of AI behavior models are developed to characterize nominal and anomalous behaviors for piloted aircraft. ...

    SBIR Phase I 2020 Department of DefenseNavy
  4. NAVY TECHNOLOGY ACCELERATION- (2): A Multi-Agent Machine Learning System for Cognitive Autonomous Sensor Processing (CASP)

    SBC: COLORADO ENGINEERING INC.            Topic: N193A01

    Complex sensor systems available on Naval Unmanned Air System (UAS) platforms requires advanced techniques to enhance their resiliency and survivability. This includes autonomous artificial intelligence (AI) architectures that can process, analyze, and provide actionable intelligence in terms of understanding and reporting on adversarial actions/events focused on damaging/crippling Navy UAS system ...

    SBIR Phase I 2020 Department of DefenseNavy
  5. (7)ADS-B Identified Routes and Corridors for Recognizing Anomalies From Training Artificial Intelligence (AIRCRAFT.AI)

    SBC: CALIOLA ENGINEERING, LLC            Topic: N193A01

    Air Traffic Management (ATM) modernization and the integration of unmanned aerial systems (UAS) on a global scale signals that the Navy will have to operate in an increasingly dynamic and loaded airspace. To satisfy air traffic authorities emerging performance requirements, airspace users necessarily have to periodically report their positions using Automatic Dependence Surveillance Broadcast (ADS ...

    SBIR Phase I 2020 Department of DefenseNavy
  6. NAVY TECHNOLOGY ACCELERATION- Machine Learning (ML) and Artificial Intelligence (AI) to Develop Capabilities and Impact Mission Success

    SBC: DATA FUSION & NEURAL NETWORKS, LLC            Topic: N193A01

    The DF&NN team proposes to demonstrate the feasibility of artificial intelligence (AI) deep neural net machine learning to predict required maintenance activities for Naval aircraft. We estimate the approach will automatically detect and predict anticipated maintenance activities and discover previously unknown required maintenance for aircraft. The system will continue to improve over time as new ...

    SBIR Phase I 2020 Department of DefenseNavy
  7. NAVY TECHNOLOGY ACCELERATION- Machine Learning (ML) and Artificial Intelligence (AI) to Develop Capabilities and Impact Mission Success

    SBC: PREDICTRONICS CORP.            Topic: N193A01

    Recent advances in artificial intelligence (AI) and machine learning have the potential to deliver significant value and impact throughout the Navy's operations and specifically in the area of predictive maintenance. In order to fully realize this potential, a framework is proposed to address the need for deployable predictive maintenance models that can maintain accuracy over time and can diagnos ...

    SBIR Phase I 2020 Department of DefenseNavy
  8. 2- AI Naval Inter-domain Tracking and Recognition Onboard Unmanned System (AI-NITROUS)

    SBC: ADVANCED SIMULATION RESEARCH INC            Topic: N193A01

    The Navy needs automated ways to be able to understand the air, land, surface and subsurface surroundings of its unmanned systems based on sensor data. ASRI’s goal for this research effort is to expand the capability for the Navy’s unmanned aerial systems (UAS) to monitor, detect, track and classify relevant targets simultaneously in land, surface, and subsurface domains. ASRI’s solution wil ...

    SBIR Phase I 2020 Department of DefenseNavy
  9. 2- AWARE-Perception

    SBC: SOAR TECHNOLOGY INC            Topic: N193A02

    Soar Technology, Inc. and Northrop Grumman propose AWARE – Perception, a self-aware architecture for improving underwater perception through motion. An unmanned underwater vehicle (UUV) equipped with AWARE-Perception learns from experience to accurately perceive its surroundings through judicious use of perception-oriented behavior. Leveraging prior episodic memory and behavioral adaptation deve ...

    SBIR Phase I 2020 Department of DefenseNavy
  10. NAVY TECHNOLOGY ACCELERATION- Unmanned Surface Vehicle (USV) and Unmanned Underwater Vehicle (UUV) Autonomous Behavior Development

    SBC: DATA FUSION & NEURAL NETWORKS, LLC            Topic: N193A02

    At sea commanders must maintain situational awareness that includes a wide range of surface and subsurface contacts with multiple acoustic (AC), radio frequency (RF), optical (VIS), and thermal (IR) signatures. Automatic detection and threat classification can dramatically improve their response time and course of action decisions. This proposal demonstrates the feasibility of artificial intellige ...

    SBIR Phase I 2020 Department of DefenseNavy
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