In this paper, we propose and compare two novel reconstruction strategies for near-field electromagnetic imaging of regions in 3D surrounded by walls or shields. Our focus is on the estimation of electrical conductivity profiles inside regions which are roughly equivalent in size to small rooms or medium-sized containers, from electromagnetic data obtained at one given frequency. This setup has interesting applications in the surveillance of activities behind walls, the screening of boxes or containers at ports or airports, or the monitoring of processes inside regions which might contain hazardous materials. Moreover, the techniques proposed here can easily be adjusted to imaging situations at larger or smaller scale; as often found in geophysical or non-destructive testing applications. The two novel regularization techniques proposed here are based on a sparsity promoting regularization scheme on the one hand, and a level set based shape evolution technique on the other. In our numerical simulations, we perform 3D reconstructions from noisy simulated data and compare the results with those obtained from a standard L 2 -type reconstruction approach. Our results suggest, in the applications considered here, that the two proposed novel schemes are potentially able to yield significantly improved reconstructions compared to more traditional techniques.
In this paper, we propose a novel reconstruction scheme for the low-frequency near-field electromagnetic imaging of high-contrast conductivity distributions inside shielded regions using the system of Maxwell's equations in 3D. In our novel scheme, we focus on estimating the shape characteristics of the electrical conductivity profile inside these regions from low-frequency electromagnetic data measured at external locations for a single frequency. We introduce a color level set regularization scheme which is a shape-based approach focusing on the simultaneous reconstruction of several shape-like distributions of different conductivity values in the same region of interest. We also introduce a topological perturbation scheme alongside the color level set regularization that is used to avoid a certain type of local minima which is characteristic for this simultaneous multi-value shape-based reconstruction. Using two numerical experiments focusing on a three-value reconstruction problem related to the imaging of shielded boxes or cargo containers, we compare this novel approach with results obtained from standard voxel-based reconstruction schemes on the one hand and the more established two-value shape based approach on the other hand. We demonstrate that, depending on the particular situation of the imaging setup, this three-value (or in general multiple-value) shape-based reconstruction technique has the potential to provide superior reconstruction results in many situations, in particular regarding reconstruction of the correct shapes. We also discuss particular challenges of this novel methodology.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.