Electromagnetic Target Detection in Uncertain Media: Time-Reversal and Minimum-Variance Algorithms

Dehong Liu, Jeffrey Krolik, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

60 Scopus citations


An experimental study is performed on imaging targets that are situated in a highly scattering environment, employing electromagnetic time-reversal methods. A particular focus is placed on performance when the electrical properties of the background environment (medium) are uncertain. It is assumed that the (unknown) medium characteristic of the scattered fields represents one sample from an underlying random process, with this random process representing our uncertainty in the media properties associated with the scattering measurement. While the specific Green's function associated with the scattered fields is unknown, we assume access to an ensemble of Green's functions sampled from the aforementioned distribution. This ensemble of Green's functions may be used in several ways to mitigate uncertainty in the true Green's function. Specifically, when performing time-reversal imaging, we consider a Green's function as a representative of the average of the ensemble, as well as Green's functions based on a principal components analysis of the ensemble. We also develop a wideband minimum-variance beamformer with environment perturbation constraints, in which the unknown Green's function is constrained to reside in a subspace spanned by the Green's function ensemble. These algorithms are examined using electromagnetic scattering data measured in a canonical set of laboratory experiments. The qualitative performance of the different techniques is presented in the form of images, with quantitative results presented in the form of receiver operating characteristic performance. © 2007, IEEE. All rights reserved.
Original languageEnglish (US)
Pages (from-to)934-944
Number of pages11
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number4
StatePublished - Apr 7 2007
Externally publishedYes

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