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Proceedings Paper

Quadratic lifting inversion applied to buried object radar imaging
Author(s): Joseph B. Lindgren; Joseph Burns; Brian Thelen; Ismael Xique
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Paper Abstract

Since radar imaging of buried objects involves propagation through media that are at best partially known, there is mismatch between the forward model used in the inversion and the propagation behavior actually observed in the measured data. The mismatch can cause degradation and/or reduce resolution in the imagery, which limit automatic target recognition features that can be extracted from the imagery. Recently, several research groups have advocated backpropagation of interferometric measurements as a more statistically stable estimator of targets in the presence of forward model errors and in the presence of clutter. Specifically, the lifting approach to inverse problems [Demanet and Jugnon, 2017]1 has been proposed as a robust approach to inversion in the presence of forward model mismatch that can produce reconstructions with fidelity comparable to direct inversion with the matched model. We apply this technique to radar imaging of buried targets to determine if it can produce enhanced imagery in the presence of limited knowledge of the surrounding ground geometry and/or material properties. In this paper we describe the algorithm implementation and present results for both simulated and measured data. The results show that the approach has significant potential for enhancing images of buried objects from scenarios with realistic forward model mismatch. However, we have observed significant sensitivity to surrounding clutter and to the choice of regularization. Mitigating these sensitivities is a topic of ongoing research.

Paper Details

Date Published: 10 May 2019
PDF: 11 pages
Proc. SPIE 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 1101217 (10 May 2019); doi: 10.1117/12.2519016
Show Author Affiliations
Joseph B. Lindgren, Michigan Tech Research Institute (United States)
Joseph Burns, Michigan Tech Research Institute (United States)
Brian Thelen, Michigan Tech Research Institute (United States)
Ismael Xique, Michigan Tech Research Institute (United States)

Published in SPIE Proceedings Vol. 11012:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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