The following work discusses IAA’s approach to tackling the wide angle, circular spotlight, synthetic aperture radar (SAR) problem from the 2008 Gotcha wide angle SAR data set, which is publicly released, with unlimited distribution. This data set comes with a MATLAB image formation routine and attendant graphical user inter- face (GUI). We begin by introducing a simple approach to focusing the collected phase history data that utilizes point targets (quadrahedral targets) present in the scene. Two SAR imaging algorithms are then presented, namely, the data-independent backprojection (BP) algorithm and the data-adaptive sparse learning via itera- tive minimization (SLIM) algorithm. These imaging approaches are compared using the 2008 Gotcha wide angle SAR data to perform both a clutter discrimination experiment, as well as an automatic target recognition (ATR) experiment. The ATR system is composed of a target pose and target center estimation preprocessing system, and includes a novel target feature for the final classification stage. Empirical results obtained by applying the focusing approach and imaging algorithms to the 2008 Gotcha wide angle SAR data set are presented and described. The results presented highlight the benefit of applying the SLIM algorithm over its data-independent counterpart, as well as the utility of the novel target feature.

Date Published: 23 May 2013
PDF: 8 pages
Proc. SPIE 8746, Algorithms for Synthetic Aperture Radar Imagery XX, 87460N (23 May 2013); doi: 10.1117/12.2015773

Published in SPIE Proceedings Vol. 8746:
Algorithms for Synthetic Aperture Radar Imagery XX
Edmund Zelnio; Frederick D. Garber, Editor(s)