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Proceedings Paper

Three-dimensional sparse-aperture moving-target imaging
Author(s): Matthew Ferrara; Julie Jackson; Mark Stuff
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Paper Abstract

If a target's motion can be determined, the problem of reconstructing a 3D target image becomes a sparse-aperture imaging problem. That is, the data lies on a random trajectory in k-space, which constitutes a sparse data collection that yields very low-resolution images if backprojection or other standard imaging techniques are used. This paper investigates two moving-target imaging algorithms: the first is a greedy algorithm based on the CLEAN technique, and the second is a version of Basis Pursuit Denoising. The two imaging algorithms are compared for a realistic moving-target motion history applied to a Xpatch-generated backhoe data set.

Paper Details

Date Published: 15 April 2008
PDF: 11 pages
Proc. SPIE 6970, Algorithms for Synthetic Aperture Radar Imagery XV, 697006 (15 April 2008); doi: 10.1117/12.786289
Show Author Affiliations
Matthew Ferrara, Air Force Research Lab. (United States)
Julie Jackson, The Ohio State Univ. (United States)
Mark Stuff, Michigan Tech Research Institute (United States)


Published in SPIE Proceedings Vol. 6970:
Algorithms for Synthetic Aperture Radar Imagery XV
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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