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This paper provides a simple yet effective approach to improve direction-of-arrival (DOA) estimation performance in extreme signal-to-noise-ratio (SNR) conditions. As an example, a multiple signal classification (MUSIC) algorithm with a deep learning (DL) approach is used. First, brief research into the existing DOA estimation techniques is provided, followed by a demonstration of a simulation environment created on the MATLAB platform to generate and resolve signals from a uniform rectangular array of antenna elements. Following that is an attempt to improve the estimation accuracy of these signals by training various DL approaches, including multi-layer perceptron and one- and two-dimensional convolutional neural networks, using the generated dataset. Key findings include the cases where the developed DL approach can resolve signals and provide accurate DOA estimations that the MUSIC algorithm cannot. [ABSTRACT FROM AUTHOR]

In this paper, we present the application of a resonant electric based metamaterial element and its two-dimensional metasurface implementation for a variety of emerging wireless applications. Metasurface apertures developed in this work are synthesized using sub-wavelength sampled resonant electric-based unit-cell structures and can achieve electromagnetic wave manipulation at microwave frequencies. The presented surfaces are implemented in a variety of forms, from absorption surfaces for energy harvesting and wireless power transfer to wave-chaotic surfaces for compressive sensing based single-pixel direction of arrival estimation and reflecting surfaces. It is shown that the resonant electric-synthesized metasurface concept offers a significant potential for these applications with high fidelity absorption, transmission and reflection characteristics within the microwave frequency spectrum. [ABSTRACT FROM AUTHOR]

A simple, nearfield transformation meta-structure is proposed to increase the directivity of resonant cavity antennas (RCA). The meta-structure is comprised of 14 × 14 meta-atoms or so called "unit-cells", adding localized phase delays in the aperture of the RCA and thus increasing its broadside directivity. A prototype of the meta-structure is additively manufactured using the stereolithograpy process and has a profile of 0.56 λ. With the meta-structure integrated with the RCA, it demonstrates a measured broadside directivity of 20.15 dBi without affecting its half-power directivity bandwidth. Benefiting from additive manufacturing, the proposed approach is a simple, light-weight, low-cost, and planar approach that can be tailored to achieve medium-to-high gains with RCAs. [ABSTRACT FROM AUTHOR]

Computational imaging using coded apertures offers all-electronic operation with a substantially reduced hardware complexity for data acquisition. At the core of this technique is the single-pixel coded aperture modality, which produces spatio-temporarily varying, quasi-random bases to encode the back-scattered radar data replacing the conventional pixel-by-pixel raster scanning requirement of conventional imaging techniques. For a frequency-diverse computational imaging radar, the coded aperture is of significant importance, governing key imaging metrics such as the orthogonality of the information encoded from the scene as the frequency is swept, and hence the conditioning of the imaging problem, directly impacting the fidelity of the reconstructed images. In this paper, we present dielectric lens loading of coded apertures as an effective way to increase the information coding capacity of frequency-diverse antennas for computational imaging problems. We show that by lens loading the coded aperture for the presented imaging problem, the number of effective measurement modes can be increased by 32% while the conditioning of the imaging problem is improved by a factor of greater than two times. [ABSTRACT FROM AUTHOR]