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IN-DEPTH ENGINEERING CORPORATION SBIR Phase I Award, August 2021

A SBIR Phase I contract was awarded to IN-DEPTH ENGINEERING CORPORATION in August, 2021 for $140,000.0 USD from the U.S. Department of Defense and United States Navy.

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Contents

sbir.gov/node/2176051
Is a
SBIR/STTR Awards
SBIR/STTR Awards

SBIR/STTR Award attributes

SBIR/STTR Award Recipient
IN-DEPTH ENGINEERING CORPORATION
IN-DEPTH ENGINEERING CORPORATION
0
Government Agency
U.S. Department of Defense
U.S. Department of Defense
0
Government Branch
United States Navy
United States Navy
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Award Type
SBIR0
Contract Number (US Government)
N68335-21-C-07030
Award Phase
Phase I0
Award Amount (USD)
140,0000
Date Awarded
August 17, 2021
0
End Date
February 22, 2022
0
Abstract

The Advanced Processor Build (APB) and Advanced Capabilities Build (ACB) processes are critical mechanisms to allow the Navy to pace the threat by continually introducing improved ASW acoustic sensor and signal processing solutions. These new solutions require adequate operator training in order to be effective – training that leverages the latest advances in learning science, such as adaptive training and dynamic feedback tools. More efficient training tools address the Sailor 2025 Ready Relevant Leaning initiative, the CNO’s request for “high velocity learning,” and the recent STRG Advanced Development Prioritized Focus Area letter call for adaptive training solutions that use real-world data. An area in particular need of improved training technology is narrowband signal detection and analysis for sonar operators. Existing trainers tend to be “static,” i.e., every trainee receives the same material at the same pace, with limited connection to real tactical displays, and are difficult to update with new threats and their associated unique narrowband signatures as they are discovered. In-Depth Engineering Corporation proposes the Sonar Narrowband Adaptive Proficiency (SNAP) solution, an adaptive narrowband signal detection and analysis trainer that: (1) uses Item Response Theory combined with AI/ML to adaptively move the trainee through the course material, (2) implements the real tactical displays that are stimulated with real-world acoustic data maintained in an easily-updateable database, and (3) provides both dynamic and post-exercise, process-based, multi-modality feedback, which has been proven to improve learning efficiency and knowledge retention.

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