Electromagnetic Imaging of Dielectric Objects Using a Multi-Directional Search-Based Simulated Annealing

Low-cost quasi-global optimization of expensive electromagnetic simulation models by inverse surrogates and response features

Conceptual design of contemporary high-frequency structures is typically followed by a careful tuning of their parameters, predominantly the geometry ones. The process aims at improving the relevant performance figures, and may be quite expensive. The reason is that conventional design methods, e.g., based on analytical or equivalent network models, often only yield rough initial designs. This is especially the case for miniaturized components featuring considerable electromagnetic (EM) cross couplings, or antenna systems with non-negligible radiator coupling (e.g., MIMO, closely-spaced arrays). For reliability reasons, parametric optimization is carried out using EM simulation tools, which is a time-consuming task. In many cases, designer needs to resort to a global search, especially when handling several objectives and constraints is necessary, or the high-frequency structure under design is overly complex. Combination of both aforementioned factors makes it no longer possible to rely on engineering insight, even to detect a promising region of the design space. Unfortunately, nature-inspired algorithms, commonly employed for solving these tasks typically exhibit significant computational expenditures. This paper proposes a simple yet efficient method for globalized search using a response feature approach and inverse regression surrogates. Owing to less nonlinear dependence of the feature point coordinates on the system variables (as compared to the original responses, e.g., S-parameter frequency characteristics), our methodology permits a rapid identification of the most appropriate regions of the parametric space, and further design tuning by means of local routines. At the same time, the overall optimization cost is comparable to the cost of local procedures. The proposed approach is validated using several high-frequency structures (a dual-band antenna, a microstrip coupler, an impedance matching transformer) optimized under different design scenarios. Global search capability and computational efficiency are demonstrated through comprehensive comparisons with multiple-start local search, as well as particle swarm optimizer, a representative nature-inspired algorithm.

GNSS-R Soil Moisture Retrieval for Flat Vegetated Surfaces Using a Physics-Based Bistatic Scattering Model and Hybrid Global/Local Optimization

This paper presents a soil moisture retrieval scheme from Cyclone Global Navigation Satellite System (CYGNSS) delay-Doppler maps (DDMs) over land. The proposed inversion method consists of a hybrid global and local optimization method and a physics-based bistatic scattering forward model. The forward model was developed for bare-to-densely vegetated terrains, and it predicts the circularly polarized bistatic radar cross section DDM of the land surface. This method was tested on both simulated DDMs and CYGNSS DDMs over the Soil Moisture Active Passive (SMAP) Yanco core validation site in Australia. About 250 CYGNSS DDMs from 2019 and 2020 over the Yanco site were used for validation. The simulated DDMs were for grassland and forest vegetation types. The vegetation type of the Yanco validation site was grassland. The vegetation water content (VWC) was 0.19 kgm−2 and 4.89 kgm−2 for the grassland and forest terrains, respectively. For the case when the surface roughness is known to the algorithm, the unbiased root mean square error (ubRMSE) of soil moisture estimates was less than 0.03 m3m−3 while it was approximately 0.06 m3m−3 and 0.09 m3m−3 for the validation results from 2019 and 2020, respectively. The retrieval algorithm generally had enhanced performance for smaller values of soil moisture. For the case when both the soil moisture and surface roughness are unknown to the algorithm and only a single DDM is used for retrieval, the validation results showed an expected reduced performance, with an an ubRMSE of less than 0.12 m3m−3.

Fast optimal design of optical components using the cultural algorithm

Design of the guided-mode resonance (GMR) grating filter, as one of the most important optical components, using the cultural algorithm (CA) is presented, for the first time. CA is an evolutionary algorithm (EA) which is easy-to-implement, flexible, inspired by the human cultural evolution, upon using the domain knowledge for reducing the search space as a metaheuristic optimization method. Reflection spectra of the designed GMR filter based on the CA is in good agreement with the previous simulation results. CA has both acceptable accuracy and enough high speed to optimize the complicated structures; therefore, a novel double-line asymmetrical transmitter (DLAT) is introduced and optimized as a complex grating-based optical component using the mentioned algorithm. The results show the transmittance at two different communication wavelengths (1.5039 and 1.6113 µm) using the combination of binary diffraction grating and customized photonic crystal (PhC) structure. Also, the DLAT shows the characteristics of a perfect transverse magnetic (TM) polarizer. Furthermore, we demonstrated the Talbot effect at the DLAT output which is so applicable in the optical usage, especially for the integrated optics.