Uncertainty Estimation for Atmospheric Acoustic Propagation Prediction

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$69,993.00
Award Year:
2008
Program:
SBIR
Phase:
Phase I
Contract:
N66001-08-M-1075
Agency Tracking Number:
N081-096-0544
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
APPLIED PHYSICAL SCIENCES CORP.
475 Bridge Street, Suite 100, Groton, CT, 06340
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
112716357
Principal Investigator:
Joshua Wilson
Scientist
(860) 448-3253
jwilson@aphysci.com
Business Contact:
David Horne
Sr. Vice President/CFO
(860) 448-3253
dhorne@aphysci.com
Research Institution:
n/a
Abstract
The propagation of acoustic signatures from vehicles, aircraft, boats and artillery can have a significant impact on military operations. Specifically combatants need to reliably detect enemy acoustic signatures while at the same time concealing their own. The physical propagation environment, including surface and atmospheric parameters, may drastically alter the range at which an emitting object may be detected. There are currently several models for predicting these acoustic propagation effects based on atmospheric profiles and/or ground topography. Unfortunately these models are sensitive to errors in environmental inputs. We propose a method for estimating the uncertainty (variance) of the acoustic propagation prediction given the uncertainty in the environmental inputs. Advantages to this new method are (1) it fits within current Parabolic Equation (PE) marching algorithms and does not require a second stand-alone code and (2) it is computationally efficient. This would allow our forces to simultaneously predict both the acoustic propagation and its uncertainty. The algorithm used to calculate the uncertainty may also be used to determine the sensitivity of the sound field to the various individual environmental parameters which could aid (1) in acoustic environmental parameter inversion and (2) in the intelligent allocation of meteorological measurement resources.

* information listed above is at the time of submission.

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