Signature Prediction and Uncertainty Analysis for Recognition Applications

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$99,998.00
Award Year:
2006
Program:
SBIR
Phase:
Phase I
Contract:
FA8650-06-M-1054
Agency Tracking Number:
F061-217-0800
Solicitation Year:
2006
Solicitation Topic Code:
AF06-217
Solicitation Number:
2006.1
Small Business Information
HYPERCOMP, INC.
31255 Cedar Valley Drive, Suite 327, Westlake Village, CA, 91362
Hubzone Owned:
N
Socially and Economically Disadvantaged:
Y
Woman Owned:
N
Duns:
005100560
Principal Investigator:
vijaya shankar
Vice President
(818) 865-3713
vshankar@hypercomp.net
Business Contact:
Vijaya Shankar
Vice President
(818) 865-3713
vshankar@hypercomp.net
Research Institution:
n/a
Abstract
Development of target recognition algorithms require 1) predicting accurate physics-based signatures, and 2) characterizing signature sensitivity to various field parameters in order to identify robust and stable signature features. HyPerComp Inc. in collaboration with SAIC-DEMACO proposes to build a computational platform using its highly accurate Maxwell’s equations solver TEMPUS (Time-Domain EM Parallel Unstructured Simulator) a procedure to model uncertainty that enables quantifying the sensitivity of solutions and derived quantities (RCS, SAR, etc.) to key parameters affecting signatures observed in the field. The uncertainty model is based on the recent work of Prof. Hesthaven of Brown University and his colleagues that uses a so-called chaos expansions to represent the functional dependence of the solution on parameters that can only be described in a statistical sense. This approach is far more efficient than the traditional method of Monte Carlo sampling and it naturally provides a probability distribution for the solution space.

* information listed above is at the time of submission.

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