Through-wall imaging (TWI) is a challenging topic in the area of inverse scattering problem based microwave imaging. A TWI solution based on non-linear inversion algorithms is experimentally investigated. Experiments are performed using a setup with the object surrounded by a closed wall. The inversion is based on a twofold subspace-based optimisation method, and the results show that the location, shape, size, and dielectric constant of the object can be successfully retrieved from the measured scattering data. This is the first experimental demonstration of TWI of objects behind a closed wall using a non-linear inversion algorithm. These results imply the potential of TWI for practical applications.
Abstract:Microwave imaging based on inverse scattering problem has been attracting many interests in the microwave society. Among some major technical challenges, the ill-posed, multi-dimensional inversion algorithm and the complicated measurement setup are critical ones that prevent it from practical applications. In this paper, we experimentally investigate the performance of the subspace-based optimization method (SOM) for two-dimensional objects when it was applied to a setup designed for oblique incidence. Analytical, simulation, and experimental results show that, for 2D objects, neglecting the cross-polarization scattering will not cause a notable loss of information. Our method can be potentially used in practical imaging applications for 2D-like objects, such as human limbs.
Although significant progress has been made in microwave imaging, real‐time imaging, especially for objects behind walls or closed obstacles, remains a technical challenge. In this work, highly efficient imaging for complex‐structured objects surrounded by a closed obstacle was experimentally demonstrated. The imaging equations are derived based on a combination of the inverse‐scattering problem and the concept of compressed sensing. Making use of the spatial sparsity of objects and obstacles, the compressed imaging can be implemented using a time‐division multi‐antenna setup with reduced transmitting antennas. Owing to the spatial compressed sensing applied to the sparse imaging region and objects, the imaging time can be reduced by two orders of magnitude compared with the conventional twofold subspace‐based optimisation method with a comparable imaging quality. Taking advantage of the sparsity of the entire imaging area, objects with larger relative permittivity can also be reconstructed. The proposed method can be potentially used in applications such as security examination through boxes. It also provides a new clue for solving the practicability difficulty faced by existing microwave imaging systems.
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