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Proceedings Paper

A Bayesian approach to simultaneous autofocus and superresolution
Author(s): Richard O Lane; Keith D Copsey; Andrew R Webb
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Paper Abstract

This paper presents a numerical Bayesian approach to the autofocus and super-resolution of targets in radar imagery. An ill-posed inverse problem is studied in which the known linear imaging operator is subject to an unknown degree of distortion (defocusing). The goal is simultaneously to reconstruct a high-resolution representation of a target based on noisy lower resolution image measurements and to estimate the degree of defocus. We present a Markov chain Monte Carlo algorithm for parameter estimation, illustrate the approach on an explanatory example and compare our technique with a maximum likelihood approach. Given a model for the sensor measurement process, this technique may be applied to any type of radar image such as those produced by a synthetic aperture radar (SAR), inverse SAR (ISAR) or a real beam imaging radar. The proposed approach fits into a larger set of procedures aiming to exploit targeting information from different radar sensors.

Paper Details

Date Published: 2 September 2004
PDF: 10 pages
Proc. SPIE 5427, Algorithms for Synthetic Aperture Radar Imagery XI, (2 September 2004); doi: 10.1117/12.541504
Show Author Affiliations
Richard O Lane, QinetiQ Ltd. (United Kingdom)
Keith D Copsey, QinetiQ Ltd. (United Kingdom)
Andrew R Webb, QinetiQ Ltd. (United Kingdom)


Published in SPIE Proceedings Vol. 5427:
Algorithms for Synthetic Aperture Radar Imagery XI
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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