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Proceedings Paper

Hyper-parameter selection in non-quadratic regularization-based radar image formation
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Paper Abstract

We consider the problem of automatic parameter selection in regularization-based radar image formation techniques. It has previously been shown that non-quadratic regularization produces feature-enhanced radar images; can yield superresolution; is robust to uncertain or limited data; and can generate enhanced images in non-conventional data collection scenarios such as sparse aperture imaging. However, this regularized imaging framework involves some hyper-parameters, whose choice is crucial because that directly affects the characteristics of the reconstruction. Hence there is interest in developing methods for automatic parameter choice. We investigate Stein's unbiased risk estimator (SURE) and generalized cross-validation (GCV) for automatic selection of hyper-parameters in regularized radar imaging. We present experimental results based on the Air Force Research Laboratory (AFRL) "Backhoe Data Dome," to demonstrate and discuss the effectiveness of these methods.

Paper Details

Date Published: 15 April 2008
PDF: 12 pages
Proc. SPIE 6970, Algorithms for Synthetic Aperture Radar Imagery XV, 697009 (15 April 2008); doi: 10.1117/12.782341
Show Author Affiliations
Özge Batu, Sabanci Univ. (Turkey)
Müjdat Çetin, Sabanci Univ. (Turkey)
Massachusetts Institute of Technology (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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