Compressive Sensing for DCGS-N

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-12-M-0375
Agency Tracking Number: N12A-026-0199
Amount: $79,999.00
Phase: Phase I
Program: STTR
Awards Year: 2012
Solicitation Year: 2012
Solicitation Topic Code: N12A-T026
Solicitation Number: 2012.A
Small Business Information
500 West Cummings Park - Ste 3000, Woburn, MA, -
DUNS: 859244204
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: Y
Principal Investigator
 Les Novak
 Principal Investigator
 (781) 933-5355
Business Contact
 Jay Miselis
Title: Corporate Controller
Phone: (781) 933-5355
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
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. *

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