Compressive Sensing for DCGS-N

Period of Performance: 06/25/2012 - 04/26/2013

$80K

Phase 1 STTR

Recipient Firm

Scientific Systems Company, Inc.
500 West Cummings Park Array
Woburn, MA 01801
Principal Investigator
Firm POC

Research Institution

Wright State University
134 Oelman Hall 3640 Colonel Glenn Highway
Dayton, OH 45435
Institution POC

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.