A Generalized Uncertainty Analysis for Physical Optics Based Radar Cross Section Prediction

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$100,000.00
Award Year:
2006
Program:
SBIR
Phase:
Phase I
Contract:
FA8650-06-M-1056
Agency Tracking Number:
F061-217-1468
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
ANALYTIC DESIGNS, INC.
245 East Gay Street, Columbus, OH, 43215
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
802771923
Principal Investigator:
Sean Gilmore
Principal Scientist
(614) 224-9078
swg@adinc.com
Business Contact:
Harry Shamansky
President
(614) 224-9078
hts@adinc.com
Research Institution:
n/a
Abstract
Automatic Target Recognition (ATR) for Combat Identification (CID) represents one of the most demanding disciplines in sensor technology facing the United States Air Force today. Central to this process is the development of validated synthetic. Unfortunately, the synthetic database generation process is hampered by uncertainty. Under a previous program, Analytic Designs, Incorporated developed a robust and extensible theoretical foundation uncertainty analysis for Physical Optics based Radar Cross Section (RCS) prediction. The goal of this Phase~I proposal is to extend this statistical theory to include two important areas of uncertainty: coupled regions and altered parameterizations. Coupled regions addresses the statistical dependence associated with vertices comprised by feature-based regions. During the target modeling process, a modeler may be unsure of the exact location of a specific feature (e.g., hatch, weapon, antenna, etc.), and this uncertainty may be larger than the geometry capture process. Altered parameterizations addresses the need to simulate RCS uncertainty in synthetic data due to errors in pose angle and/or frequency to facilitate comparisons to measured data and streamline the data validation process. The overall purpose of this Phase~I program is to improve the efficiency of the synthetic RCS database development process and thereby advance Automatic Target Recognition capabilities.

* information listed above is at the time of submission.

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