Feature-aided tracking of targets in synthetic aperture radar is a topic of increasing interest. The aperture synthesized through the combination of target and platform motion facilitates the application of two-dimensional target recognition algorithms through noncooperative imaging of the target in question. Many non-parametric inverse synthetic aperture radar imaging techniques maximize image sharpness by estimating the phase error imposed by the unknown target motion. The resultant images can suffer from small unresolved phase errors and ambiguous cross range resolution. Downstream image exploitation algorithms must be robust to these effects. A set of civilian vehicles is investigated, which exacerbates image quality based ISAR algorithms due to their comparatively small radar cross section. This paper addresses the feasibility of peak-based classifcation of civilian targets moving through challenging tracking scenarios using ISAR images. Classifier performance is evaluated over a set of sensor, target, and environmental operating conditions through use of synthetically generated data.

Date Published: 3 June 2013
PDF: 10 pages
Proc. SPIE 8746, Algorithms for Synthetic Aperture Radar Imagery XX, 87460H (3 June 2013); doi: 10.1117/12.2018186

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