Towards real‐time through‐obstacle imaging based on compressed sensing for sparse objects

Abstract

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.