A Bayesian approach to simultaneous autofocus and superresolution

Richard O Lane; Keith D Copsey; Andrew R Webb

doi:10.1117/12.541504

2 September 2004 A Bayesian approach to simultaneous autofocus and superresolution

Richard O Lane, Keith D Copsey, Andrew R Webb

Proceedings Volume 5427, Algorithms for Synthetic Aperture Radar Imagery XI; (2004) https://doi.org/10.1117/12.541504
Event: Defense and Security, 2004, Orlando, Florida, United States

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.

Citation Download Citation

Richard O Lane, Keith D Copsey, and Andrew R Webb "A Bayesian approach to simultaneous autofocus and superresolution", Proc. SPIE 5427, Algorithms for Synthetic Aperture Radar Imagery XI, (2 September 2004); https://doi.org/10.1117/12.541504

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