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Imaging in high clutter environments
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Burkholder, RJ, Volakis, JL. (2011). Imaging in high clutter environments .
10.1109/LAPC.2011.6114012
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Burkholder, RJ, Volakis, JL. (2011). Imaging in high clutter environments .
10.1109/LAPC.2011.6114012
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cited authors
Burkholder, RJ; Volakis, JL
authors
Volakis, John
abstract
Four microwave imaging methods are presented with the goal of suppressing natural clutter from such images. Unlike traditional clutter filtering approaches, based on a known clutter distribution, imaging algorithms aim to suppress any scattering mechanism not "stable" across all sensor locations. The four methods considered are (a) Coherence factor correction, (b) Model-based correction, (c) Adaptive sidelobe reduction (apodization), and (d) Image sparsity optimization (compressive sensing). In all cases, a clearer image is attained. However, image sparsity optimization leads to significantly sharper images. The images are actually super-resolved and are improved subject to available CPU time and/or data additions. Simulated and measured imaging examples are presented to demonstrate the stated conclusions. © 2011 IEEE.
publication date
December 1, 2011
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Digital Object Identifier (DOI)
https://doi.org/10.1109/lapc.2011.6114012