Compressive Sensing for DCGS-N

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$79,999.00
Award Year:
2012
Program:
STTR
Phase:
Phase I
Contract:
N00014-12-M-0375
Agency Tracking Number:
N12A-026-0199
Solicitation Year:
2012
Solicitation Topic Code:
N12A-T026
Solicitation Number:
2012.A
Small Business Information
Scientific Systems Company, Inc
500 West Cummings Park - Ste 3000, Woburn, MA, -
Hubzone Owned:
N
Socially and Economically Disadvantaged:
Y
Woman Owned:
N
Duns:
859244204
Principal Investigator:
Les Novak
Principal Investigator
(781) 933-5355
lnovak@ssci.com
Business Contact:
Jay Miselis
Corporate Controller
(781) 933-5355
contracts@ssci.com
Research Institution:
Wright State University
Brian D Rigling
Office of Research
3640 Colonel Glenn Hwy.
Dayton, OH, 45435-0001
(937) 775-5100
Nonprofit college or university
Abstract
Compressive sensing (CS) is a relatively new form of data sampling that shows promise to greatly reduce the amount of information required to acquire and reconstruct information from sources such as synthetic aperture radar (SAR) imagery, electro-optical (EO) imagery, and RF data. CS has interesting practical applications in processing/exploitation of imagery, signals, and other structured data. SSCI has applied CS-based processing to the formation of SAR imagery from phase-history data that has been degraded by interruptions in the SAR data collect. SSCI"s CS-based image formation algorithm provides imagery having nearly optimum image quality (IQ) from a significantly reduced amount of data. SSCI has also demonstrated CS-based image formation of EO data, obtaining excellent EO imagery from highly compressed data. The IQ of CS-compressed SAR and EO imagery is sufficient for exploitation by DCGS-N image analysts. SAR exploitation modes include coherent/non-coherent change detection, automatic and analyst assisted target recognition, target tracking, etc.; EO exploitation modes include Wide Area Motion Imaging (WAMI), visual target ID, target tracking, EO change detection, etc. CS-based processing of the imagery permits the detection, classification and estimation functions with reduced dimensionality, providing increased operational rates over the original sources.

* information listed above is at the time of submission.

Agency Micro-sites

US Flag An Official Website of the United States Government