Metasurfaces allow the rapid development of compact and flat electromagnetic devices owing to their capability in manipulating the wavefront of electromagnetic waves. Particularly, with respect to the metasurface lenses, wide operational bandwidth and wide incident angle behavior are critically required for practical applications. Herein, a single-layer phase gradient metasurface lens is presented to achieve millimeter-wave focusing at a focal point of 13 mm regardless of the incident angle. The proposed metasurface lens is fabricated by constructing subwavelength-thick (< λ/10) phase elements composed of two metallic layers separated by a single dielectric substrate that exhibits low-Q resonance properties and a wide phase modulation range with satisfactory transmissivity. By controlling the spatial phase distribution, the proposed metasurface lens successfully realises effective wavefront manipulation properties and high-performance electromagnetic-wave-focusing characteristics over a wide operating frequency range from 35 to 40 GHz with incident angle independency up to 30°.

A broadband metasurface flat lens is proposed as a polarization-independent wideband superstrate for wave focusing and gain enhancement at Ka-band. The proposed metasurface structure consists of four metal layers and is designed with diagonally symmetric unit cells to accommodate both the vertical and horizontal polarizations. The focusing ability of the proposed metasurface flat lens is validated via simulation and measurement, where normally incident plane waves are shown to be enhanced by up to 11 dB as a result of wave focusing. Also, the radiation gain enhancement due to the proposed metasurface flat lens is demonstrated via simulation and measurement, where a gain enhancement of up to 10.5 dB is achieved. The results show that the proposed structure maintains the wave focusing and gain enhancement characteristics over a bandwidth of 28–32 GHz. Furthermore, to demonstrate the utility of the proposed metasurface for circular polarization (CP), the gain enhancement of a CP patch antenna as a result of implementing the proposed metasurface as a superstrate is demonstrated via simulation and measurement. It is shown that the proposed metasurface superstrate provides a CP gain enhancement of nearly 10 dB.

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In this paper, a method for nonlinear target recognition via machine learning is presented. The nonlinear radar environment used in this study was a frequency-modulated continuous-wave (FMCW) nonlinear radar with a transmit frequency band of 3.0∼3.2 GHz and received a frequency band of 6∼6.4 GHz corresponding to the second harmonics. Nonlinear radar measurements were performed using four types of electronic devices as nonlinear targets. Statistical parameters were extracted from the measured amplitude spectrum of the received harmonic responses for each target to successfully construct a classification algorithm. The extracted characteristic data were then used to construct and verify a support vector machine (SVM) classifier. The accuracy of the target classification by the trained SVM classifier was confirmed through verification data, and an accuracy of 85 % with 10-fold cross-validation was demonstrated.

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