Wavelet Compression for Improved Synthetic Aperture Radar Quality

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$749,964.00
Award Year:
2004
Program:
SBIR
Phase:
Phase II
Contract:
FA8650-04-C-4203
Award Id:
62316
Agency Tracking Number:
F031-1537
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
4027 Colonel Glenn Highway, Suite 210, Dayton, OH, 45431
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
023956415
Principal Investigator:
CarrollDay
(937) 429-9008
cday@systranfederal.com
Business Contact:
KennethBaker
(937) 429-9008
kbaker@systranfederal.com
Research Institute:
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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