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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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3D Acoustic Model for Geometrically Constrained Environments
SBC: HEAT, LIGHT, AND SOUND RESEARCH, INC. Topic: N16AT018The goal of this work is to demonstrate and validate a 3D acoustic propagation model for use in constrained environments such as harbors. The 3D model will be used to model system performance for 1) passive sonar, 2) active sonar, and 3) acoustic communications networks. This latter application is of primary importance in this Phase II Extended proposal. To accomplish these goals are sequence of f ...
STTR Phase II 2023 Department of DefenseNavy -
Improved High-Frequency Bottom Loss Characterization
SBC: HEAT, LIGHT, AND SOUND RESEARCH, INC. Topic: N17AT026The existing HFBL (High-Frequency Bottom Loss) database has been recognized to be unsatisfactory due to its lack of physical underpinning and inability to provide consistent performance across frequency and space. The aim of the project is to replace the HFBL database with a geoacoustic model that leads to a smooth transition to the LFBL (Low-Frequency Bottom Loss) model at 1 kHz. To this end, thi ...
STTR Phase II 2023 Department of DefenseNavy -
Deep Reinforcement Learning for Collaborative Multi-Robot Systems with Low-Latency Wireless Networking
SBC: TIAMI LLC Topic: N23BT031In this Phase I effort, Tiami, LLC, aims to develop and demonstrate a hardware proof of concept for a collaborative multi-robot system (MRS) that leverages imitative augmented deep reinforcement learning (IADRL) amongst heterogeneous uncrewed systems (robots) to achieve a common task. Collaboration is based on low-latency machine-to-machine wireless links between robots that use both RF and optica ...
STTR Phase I 2023 Department of DefenseNavy -
Ad Hoc Swarm Modulation and Adaptation
SBC: IOTAI INC Topic: N23BT031Ad Hoc Swarm Modulation and Adaptation focuses on the ability to enable secure cyber communications, data, and distributed AI processing for any robotic swarm in any condition. The system incorporates a range of multi-robotic system functionality to allow for coordination, cooperation, and reconfigurable methods of robotic teams, flocks, and swarms. The system further includes methods for swar ...
STTR Phase I 2023 Department of DefenseNavy -
AI-Based Learning Environment (ABLE) for Undersea Warfare (USW) Training
SBC: PACIFIC SCIENCE & ENGINEERING GROUP, INC. Topic: N23AT014To compete on the world stage of undersea warfare (USW), the US Navy’s USW systems are frequently updated with advanced capabilities. As a result, modernization trainers need to perform the challenging tasks of updating training material to reflect the new (and obsolete) capabilities. This process requires comparing legacy to updated documentation, identifying changes to system capabilities, and ...
STTR Phase I 2023 Department of DefenseNavy -
UUV Sensor Transformation
SBC: Arete Associates Topic: N23AT013Areté and its teaming partner the University of Arizona (UofA) will develop a software tool that transforms sensor and metadata from a given sensor system into realistic synthetic data as if it were collected by a different sensor system. The exponential rise in available data from a multitude of sensor systems has driven commercial and academic entities to achieve significant innovations in arti ...
STTR Phase I 2023 Department of DefenseNavy -
Realistic UUV Data Transformation Tool
SBC: MAKAI OCEAN ENGINEERING INC Topic: N23AT013Undersea target recognition from sensor systems onboard unmanned underwater vehicles (UUVs) play a critical role in the US Naval strategies and mission capabilities. Machine Learning provides a game-changing opportunity for improved Automated Target Recognition (ATR), but current attempts remain limited due to a lack of adequate training data. ML-based ATR algorithms are statistics-based systems; ...
STTR Phase I 2023 Department of DefenseNavy -
Non-thermal Plasma for Deployable JP-10 Fuel Synthesis
SBC: MALACHITE TECHNOLOGIES INC Topic: N23AT015Our Phase I project will synthesize JP-10 jet fuel from CO2 feedstock using a multi-step process. CO2 will be converted to syngas (CO and H2) in a plasma reactor. The syngas will be used as the feedstock for a catalytic Fischer-Tropsch synthesis of JP-10. This carbon-neutral system will be easily deployable to synthesize jet fuel in remote locations, fit in a standard shipping container, and i ...
STTR Phase I 2023 Department of DefenseNavy -
Lightweight Turbogenerator for eVTOL Systems in Marine Environments
SBC: SCALED POWER INC Topic: N23AT016The proposed R&D project is to develop a lightweight integrated turbogenerator in a compact package intended for embedded integration into a Vertical Take-off and Landing Unmanned Aerial System (VTOL UAS). The turbogenerator will support short duration, high-power flight conditions, such as those encountered during takeoff and landing. The project will take an existing, innovative compact turbogen ...
STTR Phase I 2023 Department of DefenseNavy -
Distributed Consensus for Coherent Spectrum Sensing
SBC: TIAMI LLC Topic: N23AT017In this Phase I effort, Tiami, LLC, aims to develop and demonstrate a hardware proof of concept for a completely distributed spectrum sensing scheme that leverages consensus learning amongst radio frequency (RF) sensors. The algorithm is based on low-bandwidth message exchange between one-hop neighbors, spans multiple RF bands, is agnostic to the sensing modality, and is resilient to link disrupti ...
STTR Phase I 2023 Department of DefenseNavy