Spectral coding is of special interest in high-sensing-capacity imaging applications. This letter presents the design, fabrication, and experimental validation of a lightweight and robust 3-D-printed compressive horn antenna, capable of performing spectral coding for such applications. First, a pyramidal horn antenna is designed to operate within the E-band. Then, a dielectric piece with a pseudorandom geometric pattern is inserted inside the horn to distort the radiation pattern of the antenna in both near- and far-field regions. The performance of both antennas is evaluated by their return loss and radiation patterns.

This letter presents the first experimental results of our three-dimensional (3D) millimeter-wave (mm-wave) Compressive-Reflector-Antenna (CRA) imaging system. In this prototype, the CRA is 3D-printed and coated with a metallic spray to easily introduce pseudo-random scatterers on the surface of a traditional reflector antenna (TRA). The CRA performs a pseudo-random coding of the incident wavefront, thus adding spatial diversity in the imaging region and enabling the effective use of compressive sensing (CS) and imaging techniques. The CRA is fed with a multiple-input-multiple-output (MIMO) radar, which consists of four transmitting and four receiving ports. Consequently, the mechanical scanning parts and phase shifters, which are necessary in conventional physical or synthetic aperture arrays, are not needed in this system. Experimental results show the effectiveness of the prototype to perform a successful 3D detection of a T-shaped metallic target.

The use of solid cavities around electromagnetic sources has been recently reported as a mechanism to provide enhanced images at microwave frequencies. These cavities are used as measurement randomizers; and they compress the wave fields at the physical layer. As a result of this compression, the amount of information collected by the sensing array through the different excited modes inside the resonant cavity is increased when compared to that obtained by no-cavity approaches. In this work, a two-dimensional cavity, having multiple openings, is used to perform such a compression for ultrasound imaging. Moreover, compressive sensing techniques are used for sparse signal retrieval with a limited number of operating transceivers. As a proof-of-concept of this theoretical investigation, two point-like targets located in a uniform background medium are imaged in the presence and the absence of the cavity. In addition, an analysis of the sensing capacity and the shape of the point spread function is also carried out for the aforementioned cases. The cavity is designed to have the maximum sensing capacity given different materials and opening sizes. It is demonstrated that the use of a cavity, whether it is made of plastic or metal, can significantly enhance the sensing capacity and the point spread function of a focused beam. The imaging performance is also improved in terms cross-range resolution when compared to the no-cavity case.

This paper presents three distributed techniques to find a sparse solution of the under-determined linear problem ${\bf g}={\bf Hu}$ with a norm-1 regularization, based on the Alternating Direction Method of Multipliers (ADMM). Each one of these techniques divide the matrix ${\bf H}$ into submatrices by rows, columns, or both rows and columns, leading to the so-called consensus -based ADMM, sectioning -based ADMM, and consensus and sectioning -based ADMM, respectively. They are validated for a particular millimeter-wave imaging problem based on the use of a Compressive Reflector Antenna (CRA). The CRA is a hardware designed to increase the sensing capacity of an imaging system and reduce the mutual information among measurements, allowing an effective imaging of sparse targets with the use of Compressive Sensing (CS) techniques. In previous works, the consensus -based ADMM has been proved to accelerate the imaging process, and the sectioning -based ADMM has shown the ability to potentially reduce the amount of information to be exchanged among the computational nodes, based on the system configuration. In this paper, the mathematical formulation and graphical interpretation of these two techniques, together with the consensus and sectioning -based ADMM approach, are presented. The imaging quality, the imaging time, the convergence, the communication efficiency among the computational nodes, and the computational complexity are analyzed and compared. The distributed capabilities of the ADMM-based approaches, together with the high sensing capacity of the CRA, allow the imaging of metallic targets in a 3D domain in quasi-real time with a reduced amount of information exchanged among the nodes.

Acoustic metamaterials have been proposed for numerous applications including subwavelength imaging, impedance matching, and lensing. Yet, their application in compressive sensing and imaging has not been fully investigated. When metamaterials are used as resonators at certain frequencies, they can generate random radiation patterns in the transmitted waves from the transducers and received waves from a target. Compressive sensing favors such randomness inasmuch as it can increase incoherence by decreasing the amount of mutual information between any two different measurements. This study aims at assessing whether the use of resonating metamaterial unit cells in a single-layered non-optimized array between a number of ultrasound transceivers and targets can improve the sensing capacity, point-spread function of the sensing array (their beam focusing ability), and imaging performance in point-like target detection. The theoretical results are promising and can open the way for more efficient metamaterial designs with the aim of enhancing ultrasound imaging with lower number of transceivers compared to the regular systems.

In this paper, a novel norm-1 regularized imaging technique, based on the Alternating Direction Method of Multipliers (ADMM) algorithm, is presented. This technique divides the sensing matrix of the imaging system in submatrices by columns. This method is capable of imaging metallic targets with a reduced amount of measurements, by sectioning the imaging domain into several regions and optimizing them individually in different nodes. This technique reduces the amount of information to be shared among the nodes, compared to the consensus ADMM that divides the sensing matrix into submatrices by rows. This makes the communication among the nodes faster for large imaging domains that need to be distributed into several computational nodes of a cluster. A Compressive Reflector Antenna (CRA) has been recently proposed as a low cost hardware to provide high sensing capacity. The use of the column-wise division ADMM algorithm in combination with the imaging capabilities of the CRA allows a distributed and quasi real-time imaging, while reducing the communications among the computational nodes.