High Efficiency SIGINT Collection

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$79,987.00
Award Year:
2013
Program:
SBIR
Phase:
Phase I
Contract:
N68335-13-C-0096
Award Id:
n/a
Agency Tracking Number:
N122-121-0614
Solicitation Year:
2012
Solicitation Topic Code:
N122-121
Solicitation Number:
2012.2
Small Business Information
8201 Greensboro Drive, Suite 800, McLean, VA, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
075132543
Principal Investigator:
BreckenUhl
Principal Investigator
(575) 646-9316
buhl@invertix.com
Business Contact:
BryanJudd
EVP, Chief Financial Offi
(703) 813-2131
bjudd@invertix.com
Research Institute:
Stub




Abstract
Signals intelligence systems use the established Shannon-Nyquist approach of signal acquisition, setting a sampling requirement based on data rate. This sampling approach is generating operationally impractical amounts of data, most of which goes unused or arrives late. The conventional rate of data-to-information (DTI) conversion is low, and there exists high demand for a solution. A new approach to signal sampling achieves efficient DTI conversion. Our approach exploits mathematical sparsity to produce high accuracy information sensing (ISENSE) with reduced sampling rates. This technique employs the relatively new science of compressive sensing (CS) and compressive processing (CP), which extract information with fewer samples compared than when sampling at the Nyquist rate. As an extension to known CS techniques, ISENSE continuously validates sparsity patterns, taking CS from theory to a deployable technology. We apply ISENSE to the intercept, detection, characterization, classification and identification of emitters. We will illustrate, by qualitative and quantitative measures, how the ISENSE capability maintains DTI accuracy and detection probabilities despite reduced sampling rates, thus improving Naval airborne SIGINT data collection, storage, transport, and processing efficiency.

* information listed above is at the time of submission.

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