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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.

Displaying 205071 - 205080 of 207726 results
  1. Smart Patient Monitoring Algorithms for Ruggedized Autonomous Combat Casualty Care Capability

    SBC: ARETE ASSOCIATES            Topic: A18065

    Areté Associates’ Patient Handling via Audio, Recording, and Observation System (PHAROS) is the monitoring subsystem for a future, ruggedized, low-SWaP autonomous casualty care system capable of operating in real-time in a forward operating environment. Medics are trained to use communication and observation to gauge a patient’s status within the first few seconds of interaction as well as du ...

    SBIR Phase II 2019 Department of DefenseArmy
  2. Lidar for Mapping Dense Aerosols

    SBC: ARETE ASSOCIATES            Topic: A14041

    Arete proposes to continue development of its Cloud Lidar product, in a sequential Phase II contract.  The Cloud Lidar is designed to be a field diagnostic tool to characterize clouds of new obscurants and the effectiveness of aerosol dissemination systems.  The Cloud Lidar sensor enables quantitative measurement and visualization of the formation and temporal evolution of obscurants deep into t ...

    SBIR Phase II 2020 Department of DefenseArmy
  3. USV Celestial Navigation

    SBC: ARETE ASSOCIATES            Topic: N201060

    Arete proposes to develop and deliver HyperionPro - a modular, passive, electro-optic payload for Medium Unmanned Surface Vehicle (MUSV) and Large Unmanned Surface Vehicle (LUSV) enabling safe, autonomous navigation in GPS denied or degraded environments.  HyperionPro leverages Areté’s Celestial Navigation technology developed and demonstrated for submarines using onboard periscopes and proces ...

    SBIR Phase I 2020 Department of DefenseNavy
  4. Mine Countermeasures Unmanned Surface Vehicle Common Deploy and Retrieve System

    SBC: ARETE ASSOCIATES            Topic: N201061

    The Mine Countermeasures (MCM) Mission Package (MP) has a requirement to conduct minehunting (MH), which is being executed through a capability to tow either the AN/AQS-20C or AN/AQS-24B sonar systems. Currently, a common deploy and retrieve system does not exist for these towed systems given that they have different tow point locations, tow cables, handling requirements, hydrodynamic requirements ...

    SBIR Phase I 2020 Department of DefenseNavy
  5. Transfer Learning and Deep Transfer Learning for Military Applications

    SBC: ARETE ASSOCIATES            Topic: AF19CT004

    Aided Target Recognition (AiTR) algorithms augment human decision-making, but require a large database of labeled targets for training. Applied to new domains, these algorithms fail to transfer knowledge and require substantial retraining. Areté and University of Alabama Huntsville (UAH) propose the development of the Domain Extracted Feature Transfer (DEFT) Network to transfer knowledge from a ...

    STTR Phase I 2020 Department of DefenseAir Force
  6. Open Call for Innovative Defense-Related Dual-Purpose Technologies/Solutions with a Clear Air Force Stakeholder Need

    SBC: ARETE ASSOCIATES            Topic: AF192001

    Areté Associates introduces Photon Sleuth, an advanced deployable ground-based ultra-wide field optical sensor and advanced track-before-detect processing system, to accomplish the objective of rapidly detecting small orbital space debris objects that threaten the safety of military and commercial satellite traffic, in cooperation with the Air Force Research Laboratory and the Space Security an ...

    SBIR Phase II 2020 Department of DefenseAir Force
  7. Advanced UAV and Mortar Target Detection and Tracking Algorithms for Low Signal-to-Noise Ratio and Cluttered Environments

    SBC: ARETE ASSOCIATES            Topic: A17102

    In this work, Areté Associates proposes to extend its long history of tracking and detection solutions to meet the challenge of tracking small and dim targets in high-clutter environments using a mid-wave infrared (MWIR) sensor coupled with innovative detection algorithms and processing. We present a track-before-detect, signal-integration approach that merges the functionality of Areté’s prov ...

    SBIR Phase II 2019 Department of DefenseArmy
  8. CAMDEN

    SBC: ARETE ASSOCIATES            Topic: AF161087

    The domain of battle for missile defense, once focused around launch preparation and engaging targets after launch, must now extend temporally far before launch to ascertain the intent and actions of an adversary. While spatial data is becoming increasingly more available, there is no efficient way to look broadly and consistently across large spatial areas for structures/activities of interest fo ...

    SBIR Phase II 2020 Department of DefenseMissile Defense Agency
  9. Maritime Target Classification from Inverse Synthetic Aperture Radar (ISAR) Using Machine Learning/Arete Associates

    SBC: ARETE ASSOCIATES            Topic: SCO182008

    The Strategic Capabilities Office of OSD is seeking to use machine learning to produce a robust classification capability using ISAR for a relevant radar sensor system. Areté Associates proposes an advanced machine learning approach to perform robust classification with a path towards real-time classification performance. The proposed approach employs a robust method to generate a valid, balanc ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  10. Machine Learning applied to Measurement Assessment

    SBC: ARETE ASSOCIATES            Topic: AF182019

    Areté’s SQUIRE (Signal Quality Utility for Identification and Removal of Errors) leverages the latest in machine and deep learning to automatically detect, classify, and localize sources of contamination in radar returns. Currently, test engineers at NRTF use the SABER software tool to perform analyses to manually identify and localize contamination in radar data that needs to be removed ...

    SBIR Phase II 2020 Department of DefenseAir Force
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