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Proceedings Paper

Radar imaging using statistical orthogonality
Author(s): David G. Falconer
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Paper Abstract

Statistical orthogonality provides a mathematical basis for imaging scattering data with an inversion algorithm that is both robust and economic. The statistical technique is based on the approximate orthogonality of vectors whose elements are exponential functions with imaginary arguments and random phase angles. This orthogonality allows one to image radar data without first inverting a matrix whose dimensionality equals or exceeds the number of pixels or voxels in the algorithmic image. Additionally, statistical-based methods are applicable to data sets collected under a wide range of operational conditions, e.g., the random flight paths of the curvilinear SAR, the frequency-hopping emissions of ultra- wideband radar, or the narrowband data collected with a bistatic radar. The statistical approach also avoids the often-challenging and computationally intensive task of converting the collected measurements to a data format that is appropriate for imaging with a fast Fourier transform (FFT) or fast tomography algorithm (FTA), e.g., interpolating from polar to rectangular coordinates, or conversely.

Paper Details

Date Published: 24 August 2000
PDF: 10 pages
Proc. SPIE 4053, Algorithms for Synthetic Aperture Radar Imagery VII, (24 August 2000); doi: 10.1117/12.396333
Show Author Affiliations
David G. Falconer, SRI International (United States)


Published in SPIE Proceedings Vol. 4053:
Algorithms for Synthetic Aperture Radar Imagery VII
Edmund G. Zelnio, Editor(s)

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