Wavelet Compression for Improved Synthetic Aperture Radar Quality

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-04-C-4203
Agency Tracking Number: F031-1537
Amount: $749,964.00
Phase: Phase II
Program: SBIR
Awards Year: 2004
Solicitation Year: 2003
Solicitation Topic Code: AF03-089
Solicitation Number: 2003.1
Small Business Information
4027 Colonel Glenn Highway, Suite 210, Dayton, OH, 45431
DUNS: 023956415
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Carroll Day
 (937) 429-9008
 cday@systranfederal.com
Business Contact
 Kenneth Baker
Phone: (937) 429-9008
Email: kbaker@systranfederal.com
Research Institution
N/A
Abstract
Current and future intelligence, surveillance, and reconnaissance platforms exploit advanced Synthetic Aperture Radar (SAR) systems to identify critical targets. These systems generate enormous amounts of data requiring a large storage capacity and high transmission bandwidth. Advanced compression techniques are necessary to reduce the demands on storage and transmission while maintaining high data quality for human and machine analysis. Systran Federal Corp. (SFC), the Ohio State University (OSU), and Lockheed-Martin Corp. (LMCO) propose to develop, demonstrate, and commercialize a set of wavelet-based compression techniques adapted for SAR images and raw SAR data. As with other types of data, compression of SAR images must strike a balance between maximum density and minimum information degradation. Current techniques, such as those using a Discrete Fourier Transform or Discrete Cosine Transform, do not provide acceptable results due to the generation of artifacts. The research conducted in Phase I shows that wavelet-based techniques provide low loss of information at high compression ratios. The SFC/OSU/LMCO team will develop wavelet-based algorithms for compressing magnitude-only images and complex SAR data, and implement these algorithms in field programmable gate arrays for efficient and reconfigurable processing. Extensive testing will ensure that information vital to human and automated analysis is not degraded.

* Information listed above is at the time of submission. *

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