Sensing and imaging using multi-dimensionality coded compressive material

Abstract

Reducing the cost of electromagnetic sensing and imaging systems is a necessity before they can be far and widely established as a part of an extensive network of radars. Conventional imaging systems seek to reconstruct a target in the imaging domain by employing many transmitting and receiving antenna elements. These systems are suboptimal, due to the often large mutual information existing between successive measurements. This thesis describes a new sensing technique, which is based on the use of a novel compressive reflector antenna (CRA), that is capable of providing high sensing capacity in different sensing applications. The high sensing capacity provided by the CRA enhances the information transfer efficiency from the sensing system to the imaging domain and vice versa. Thus, complexity and cost of the hardware architecture can be drastically reduced. The CRA generates spatial codes in the imaging domain, which are dynamically changed through the excitation of Multiple-Input-Multiple-Output (MIMO) feeding arrays. In order to increase the sensing capacity of the CRA even further, frequency dispersive metamaterials can be designed to coat the surface of the CRA, which ultimately produce spectral codes in near- and far- field of the reflector. This thesis describes different concepts of operation, in which a multi-dimensionality coded compressive system can be used to perform sensing and imaging. The proposed sensing technique, which may be used for imaging applications, is based on norm-1 regularized iterative Compressive Sensing (CS) algorithms. In this thesis, we also present the mathematical formulation that describes the properties of the spatial and spectral codes produced by the CRA that will be used to perform quasi-real-time imaging. The content outlined in this thesis leverages advances from multi-scale wave propagation, sparse data signal processing, information coding and distributed computing. The result will enhance the efficiency and reliability of the current beamforming systems by using novel Compressive Sensors made of traditional metallic and dielectric structures, as well as novel metamaterials and metasurfaces.