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

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Batu, Özge and Çetin, Müjdat (2008) Hyper-parameter selection in non-quadratic regularization-based radar image formation. In: SPIE Defense and Security Symposium, Algorithms for Synthetic Aperture Radar Imagery XV, Orlando, Florida, USA

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Official URL: http://dx.doi.org/10.1117/12.782341


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.

Item Type:Papers in Conference Proceedings
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
ID Code:10523
Deposited By:Müjdat Çetin
Deposited On:11 Nov 2008 15:08
Last Modified:22 Jul 2019 10:14

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