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Efficiently Computing and/or Compensating for Object Variability in Automatic…

Award Information

Agency:
Department of Defense
Branch:
Air Force
Award ID:
82205
Program Year/Program:
2008 / SBIR
Agency Tracking Number:
F071-233-2359
Solicitation Year:
N/A
Solicitation Topic Code:
N/A
Solicitation Number:
N/A
Small Business Information
Signal Innovations Group, Inc.
4721 Emperor Blvd. Suite 330 Durham, NC 27703-
View profile »
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
 
Phase 2
Fiscal Year: 2008
Title: Efficiently Computing and/or Compensating for Object Variability in Automatic Target Recognition (ATR) Applications
Agency / Branch: DOD / USAF
Contract: FA8650-08-C-1408
Award Amount: $741,212.00
 

Abstract:

A research program is proposed on the integration of signal processing and electromagnetic modeling, to address the problem of performing ATR with targets possessing a high degree of variability. Sparseness is employed from two perspectives. First, in the signal processing component, sparse classifiers are developed, based on principled Bayesian techniques, which infer the scattering physics most relevant for ATR applications. This relevant scattering phenomenology is linked to the physical components of the target, to focus computational resources. By defining the sparse set of key scattering features, one implicitly infers which relatively small set of observables are most robust to target variability, while also providing discriminative power. The second area in which sparseness is employed is within the computational electromagnetic model. Compressive sensing employs the fact that the angle-frequency dependent scattered fields are typically sparsely rendered in an orthonormal basis (wavelets or DCT), and based upon this one need only perform a relatively small number of computations, from which the remaining computations may be inferred. By exploiting the joint information across multiple similar but distinct targets in a database, the number of compressive computations may be further reduced. The proposed research seeks to optimally integrate these processing and computational tools.

Principal Investigator:

Felix Lu
Vice-President
9196605156
lu@appliedquantumtechnologies.com

Business Contact:

Felix Lu
Vice-President
9196605156
lu@appliedquantumtechnologies.com
Small Business Information at Submission:

SIGNAL INNOVATIONS
1009 Slater Road Suite 200 Durham, NC 27703

EIN/Tax ID: 201104360
DUNS: N/A
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No