Submarine Sensor Environmental Inference

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-18-C-0806
Agency Tracking Number: N182-135-0400
Amount: $124,974.00
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
5416 Tortuga Trail, Austin, TX, 78731
DUNS: 833058634
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 David Knobles
 (512) 796-2090
 dpknobles@yahoo.com
Business Contact
 David Knobles
Phone: (512) 796-2090
Email: dpknobles@yahoo.com
Research Institution
N/A
Abstract
The Base and Option propose applying machine learning (ML) deep learning (DL) methods to estimate the Waveguide Invariant (WGI). The ML and DL application also provide prior parameterization of the waveguide. Data fusion methods generate Energy Spectral Density (ESD) and Empirical Orthogonal Functions (EOF) to characterize the uncertainty of the sound speed profile in the water column from which to develop a Likelihood function. These quantities then become input into a multi-step Bayesian maximum entropy (BME) method which determines the frequency-dependent source levels (SL) which in turn allows for the vertical Time axis in surface ship spectrograms to be replaced by Range, and for Received Levels (RL) to be replaced by transmission loss (TL). The resulting maximum a priori (MAP) for seabed parameter values then provide point estimates for the waveguide parameter values. With the MAP point estimates, the TL versus range to the SSN array for a shallow source depth can be established. The in situ determined TL can then be used as a measure to test and evaluate TL determined with the Submarine STDA.

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

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