Three-dimensional sparse-aperture moving-target imaging

Matthew Ferrara; Julie Jackson; Mark Stuff

doi:10.1117/12.786289

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15 April 2008 Three-dimensional sparse-aperture moving-target imaging

Matthew Ferrara, Julie Jackson, Mark Stuff

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Proceedings Volume 6970, Algorithms for Synthetic Aperture Radar Imagery XV; 697006 (2008) https://doi.org/10.1117/12.786289
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

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.

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Matthew Ferrara, Julie Jackson, and Mark Stuff "Three-dimensional sparse-aperture moving-target imaging", Proc. SPIE 6970, Algorithms for Synthetic Aperture Radar Imagery XV, 697006 (15 April 2008); https://doi.org/10.1117/12.786289

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