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Detection Rate Improvements Through Understanding and Modeling Ocean Variability

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-18-C-0398
Agency Tracking Number: N18A-002-0056
Amount: $124,869.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N18A-T002
Solicitation Number: 18.A
Timeline
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-05-31
Award End Date (Contract End Date): 2019-10-11
Small Business Information
5 Militia Drive Suite 104
Lexington, MA 02421
United States
DUNS: 611628595
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Phil Abbot Phil Abbot
 President
 (781) 862-8339
 abbot@oasislex.com
Business Contact
 Philip Abbot
Phone: (781) 862-8339
Email: abbot@oasislex.com
Research Institution
 Massachusetts Institute of Technology
 Marissa Clarkson Marissa Clarkson
 
77 Massachusetts Avenue
Cambridge, MA 02139
United States

 (617) 715-2479
 Nonprofit college or university
Abstract

The littoral environment is especially demanding on tactical sonar systems, in large part because the spatial and temporal variability imposes sonar system operating conditions of a nature and with a scale heretofore not encountered in the open oceans. Recent Office of Naval Research (ONR) sponsored basic research as well as fleet exercises have shown that littoral environments tactically important to the US Navy have rapidly varying sound speed profiles (direction and time dependent) along with directional wind-wave conditions and horizontally anisotropic bottom properties that result in spatial and temporal anisotropies in transmission loss. Most existing TL models within fleet Tactical Development Aids (TDAs) and System Performance Prediction (SPP) capabilities are accurate when the inputs are appropriately modeled and accounted for. But often there are not enough sensors in the water to sample the temporal and spatial variability sufficiently and the model outputs are inaccurate. This proposal addresses these deficiencies in acoustic models by leveraging highly-sampled GFI measurements of oceanographic variability to inform sonar detection uncertainty estimates and predictions of sensor-level output, to aid asset placement, enhance test planning, and improve accuracy of post-test reconstruction.

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

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