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Vol. 175

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2022-07-27 PIER Vol. 175, 29-43, 2022. doi:10.2528/PIER22052806

Low Cost and High Performance 5-Bit Programmable Phased Array Antenna at Ku-Band

Xin Li, Han Qing Yang, Rui Wen Shao, Feng Zhai, Guo Biao Liu, Zheng Xing Wang, Hong Fei Gao, Ge Fan, Jun Wei Wu, Qiang Cheng, and Tie-Jun Cui

We present a low-cost and high-performance 5-bit programmable phased array antenna at Ku-band, which consists of 1-bit reconfigurable radiation structures, digital phase shifters, and coplanar waveguide feeding network. The 1-bit reconfigurable radiation structure utilizes symmetric geometries and PIN diodes to form stable 180° phase difference. The digital phase shifter provides 168.75° phase difference and together with the radiation structure form a 348.75° phase coverage. The antenna operates between 14.4 and 15.4 GHz, and the overall array contains 24×2 elements with each of them being individually addressable. By changing the states of the diodes and thus adjusting the phase coding sequences of the array, the antenna achieves 0°-60° precise beam scanning at 14.8 GHz, with the sidelobe level, cross-polarization, and gain fluctuation being less than -16 dB, -26 dB, and 2.4 dB, respectively. A prototype was fabricated to verify the design, and the measurement results agree well with simulations. Compared with traditional phased arrays composed of numerous phase shifters and T/R components, the proposed antenna features high performance, high flexibility, low profile, and low cost. The antenna provides a new and feasible solution of wavefront steering and will benefit the various application scenarios.

2022-07-21 PIER Vol. 175, 13-27, 2022. doi:10.2528/PIER22022204

An Elliptically Polarized Wave Injection Technique via TF/SF Boundary in Subdomain Level DGTD Method

Xiaobing Han, Hang Li, Yuanguo Zhou, Lin Wang, Shangqing Liang, and Fawad Javaid

This study presents an effective solution on the basis of Discontinuous-Galerkin Time-Domain (DGTD) scheme for the injection of elliptically polarized plane wave through total-field/scattered-field (TF/SF) boundary. Generally, the elliptically polarized wave can be resolved into two linearly polarized waves in phase quadrature with the polarization planes at right angles to each other, but the proposed methodology is focused to utilize the principle of wave field formation to induce left-handed or right-handed elliptically polarized waves by regulating the phase and amplitude of the incident waves. The outcome of the proposed technique is achieved by deriving the EB-scheme equations and employing the explicit fourth order Runge-Kutta (RK4) time integration scheme in the DGTD methodology. An anisotropic Riemann solver and non-conformal mesh schemes are introduced for domain decomposition to allow efficient spatial discretization. Additionally, the proposed work is extended from single frequency to broadband elliptical polarized plane wave injection in the DGTD method, and the significance of this study is observed in the results. The experimental outcomes reveal that the proposed method is consistent with the analytical solution in free space and expected to provide efficient numerical solutions for analyzing scattering characteristics generated by various elliptically polarized waves.

2022-07-14 PIER Vol. 175, 1-11, 2022. doi:10.2528/PIER22051601

Machine-Learning-Enabled Recovery of Prior Information from Experimental Breast Microwave Imaging Data

Keeley Edwards, Joe LoVetri, Colin Gilmore, and Ian Jeffrey

We demonstrate the recovery of simple geometric and permittivity information of breast models in an experimental microwave breast imaging system using a synthetically trained machine learning workflow. The recovered information consists of simple models of adipose and fibroglandular regions. The machine learning model is trained on a labelled synthetic dataset constructed over a range of possible adipose and fibroglandular regions and the trained neural network predicts the geometry and average permittivty of the adipose and fibroglandular regions from calibrated experimental data. The proposed workflow is tested on two different experimental models of the human breast. The first model is comprised of two simple, symmetric phantoms representing the adipose and fibroglandular regions of the breast that match the model used to train the neural network. The second, more realistic model replaces the symmetric fibroglandular phantom with an irregularly shaped, MRI-derived fibroglandular phantom. We demonstrate the ability of the machine learning workflow to accurately recover geometry and complex valued average permittivity of the fibroglandular region for the simple case, and to predict a symmetric convex hull that is a reasonable approximation to the proportions of the MRI-derived fibroglandular phantom.