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.

Date Published: 15 April 2008
PDF: 11 pages
Proc. SPIE 6970, Algorithms for Synthetic Aperture Radar Imagery XV, 697006 (15 April 2008);

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