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Submarine Sensor Environmental Inference

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-18-C-0808
Agency Tracking Number: N182-135-0176
Amount: $124,651.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N182-135
Solicitation Number: 2018.2
Timeline
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2018-10-15
Award End Date (Contract End Date): 2019-04-18
Small Business Information
67 Beacon Ave, Jamestown, RI, 02835
DUNS: 129791401
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Michael Hughes
 (571) 282-8437
 mhughes@highrezconsulting.com
Business Contact
 Dennis Quelch
Phone: (401) 423-0348
Email: dquelch@highrezconsulting.com
Research Institution
N/A
Abstract
Within the increasingly contested undersea operational arena, the Navy needs a tactical and competitive advantage in undersea sensing and detection through improved situational awareness with respect to environmental parameters, such as sound speed profile (SSP) and bottom properties, that affect overall submarine sonar sensor performance. Current approaches for enhancing environmental situational awareness rely heavily on historical databases and remote numerical ocean models to provide predictions of the acoustic environment and sensor performance within that environment. To gain this competitive advantage and enhance the tactical decisions and warfighting posture of submarines, the Navy needs to develop advanced environmental inference capabilities to provide in-situ characterizations of the speed and attenuation of sound in the seabed and water column.
For the purposes of this research , HRC proposes to create a Deep Learning Environmental Inference Framework that will utilize platform/sensor/environmental geometry, canonical representations of available passive and active sensors, information derived from these available submarine sonar systems, inference and deep learning algorithms, and standard Navy ocean acoustic models coupled with advanced 3-D acoustic propagation model capabilities to fuse the traditional sources of data with local measurements to improve the currency of the environmental picture and provide measures of uncertainty for derived parameters.

* Information listed above is at the time of submission. *

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