As is well known, in through-the wall imaging one needs to estimate the wall electromagnetic parameters in order to get properly focused images. Even under the simplest case in which the wall can be assimilated as a single homogeneous slab, this problem puts some difficulties since the wall dielectric permittivity and thickness are nonlinearly linked to the reflected field data. The usual way to go on that problem is through some optimization iterative procedure which can be time consuming and can suffer from false solutions. In this contribution we propose a method that avoids the previously mentioned drawbacks by leveraging on a MIMO configuration. The main idea is to estimate the wall transmission coefficient rather than its electromagnetic properties. This way, one estimates the kernel of the relevant (for imaging) scattering operator instead of constructing it after wall parameters have been estimated. More in detail, it is shown that the characterization stage is cast as a linear inverse problem which is solved by a Truncated-Singular Value Decomposition method. The proposed method avoids optimization but in principle can be applied only for lossless walls. However, multi layered walls can be dealt with as well. In this contribution we focus only on the wall transmission coefficient estimation; once it has been obtained imaging can be achieved by standard back-propagation algorithms. In particular, the study is developed for a single wall and 3D vector case and some numerical examples are reported to check the theory.
High-resolution through-the-wall radar imaging (TWRI) systems can provide a high degree of situational awareness in
urban sensing applications. However, such systems generate huge amounts of data, owing to the use of wideband signals
and large arrays to achieve high resolutions in range and crossrange. This makes both data acquisition and processing
challenging. In this paper, we present fast data acquisition and processing schemes for TWRI. We use compressive
sensing and novel concepts of microwave tomography to establish a reduced-redundancy spatial and frequency
measurement configuration, which provides clear advantages in terms of measurement time and algorithm complexity.
Performance validation of the proposed strategy is provided using laboratory experiments.
Experimental validation of a tomographic technique for radar imaging of 3-D scenes behind walls is presented. The
imaging technique is based on a linear inverse scattering algorithm combined with a 2-D sliced approach, which ensures
fast data processing and quick investigation of very large spatial regions. Further, we investigate the possibility of
achieving 3-D reconstructions using a limited set of data with the objective of reduction in data acquisition time, while
maintaining a reasonable image quality. Performance of the limited data schemes is evaluated using experimental data
collected in a semi-controlled environment.
The information content of radiated fields and the achievable resolution limits in the reconstruction of a bounded current distribution are dealt with. The analysis refers to the scalar and one dimensional case of a rectilinear and bounded electric current distribution when data are collected over a segment location in the Fresnel or near zone, orthogonal and centered with respect to the source. In the Fresnel zone, the investigation is carried out by means of the analytical Singular Value Decomposition (SVD) of the radiation operator providing the unknown-data mapping. This has been made possible thanks to the introduction of suitable weighted scalar products both in the unknown and data spaces. In the near zone, a numerical approach based on the SVD of the radiation operator has been followed. The effect of the geometrical parameters of the measurement configuration on depth resolving power is also discussed.