E. Vafiadis

Sparse Linear Array Synthesis With Multiple Constraints Using Differential Evolution With Strategy Adaptation

This letter addresses the problem of designing sparse linear arrays with multiple constraints. The constraints could include the minimum and maximum distance between two adjacent elements, the total array length, the sidelobe level suppression in specified angular intervals, the main-lobe beamwidth, and the predefined number of elements. Our design method is based on differential evolution (DE) with strategy adaptation. We apply a DE algorithm (SaDE) that uses previous experience in both trial vector generation strategies and control parameter tuning. Design cases found in the literature are compared to those found by SaDE and other DE algorithms. The results show that fewer objective-function evaluations are required than those reported in the literature to obtain better designs. SaDE also outperforms the other DE algorithms in terms of statistical results.

A Multi-Objective Approach to Subarrayed Linear Antenna Arrays Design Based on Memetic Differential Evolution

In this paper we present a multi-objective optimization approach to subarrayed linear antenna arrays design. We define this problem as a bi-objective one. We consider two objective functions for directivity maximization and sidelobe level minimization. Memetic algorithms (MAs) are hybrid algorithms that combine the benefits of a global search Evolutionary Algorithm (EA) with a local search method. In this paper, we introduce a new memetic multi-objective evolutionary algorithm namely the memetic generalized differential evolution (MGDE3). This algorithm is a memetic extension of the popular generalized differential evolution (GDE3) algorithm. Another popular MOEA is the nondominated sorting genetic algorithm-II (NSGA-II). MGDE3, GDE3 and NSGA-II are applied to the synthesis of uniform and nonuniform subarrayed linear arrays, providing an extensive set of solutions for each design case. Depending on the desired array characteristics, the designer can select the most suitable solution. The results of the proposed method are compared with those reported in the literature, indicating the advantages and applicability of the multi-objective approach.

Pareto Optimal Yagi-Uda Antenna Design Using Multi-Objective Differential Evolution

Antenna design problems often require the optimization of several con∞icting objectives such as gain maximization, sidelobe level (SLL) reduction and input impedance matching. Multi- objective Evolutionary Algorithms (MOEAs) are suitable optimization techniques for solving such problems. An e-cient algorithm is Generalized Difierential Evolution (GDE3), which is a multi-objective extension of Difierential Evolution (DE). The GDE3 algorithm can be applied to global optimization of any engineering problem with an arbitrary number of objective and constraint functions. Another popular MOEA is Nondominated Sorting Genetic Algorithm- II (NSGA-II). Both GDE3 and NSGA-II are applied to Yagi-Uda antenna design under specifled constraints. The numerical solver used for antenna parameters calculations is SuperNEC, an object-oriented version of the numerical electromagnetic code (NEC-2). Three difierent Yagi-Uda antenna designs are considered and optimized. Pareto fronts are produced for both algorithms. The results indicate the advantages of this approach and the applicability of this design method.

On the design of a dual-band base station wire antenna

A dual-band base station wire-antenna design is presented. The MoM and the simulated-annealing technique, for optimizing the antenna geometry, are used. The procedure starts from the design of a primary radiator, consisting of a dielectric covered driven dipole and a parasitic element. A Chebyshev antenna array is checked in both the 900 and 1800 MHz frequency bands, with and without the appropriate feed network. All the electrical characteristics, as well as the patterns of the antenna, are presented.

Aperiodic Array Layout Optimization by the Constraint Relaxation Approach

An optimization procedure for the layout assessment of electrically large but finite planar arrays is presented. The synthesis takes into account the desired directivity pattern that is prescribed employing bound constraints. Moreover, the size of the radiators is taken into account, which results in a hard nonoverlapping, between the elements, constraint. The latter should not be violated if we want the attained solution not only to obey the far-field mask, but also to be physically realizable. As stated, the optimization problem is twofold. An antenna design is associated with a packing problem. In order to take the constraints on the layout into account and solve the whole problem, we propose the constraint relaxation approach, which is equipped with a packing algorithm. Our study is applied to various initial geometries, and the resulting arrays appear to comply with the desired pattern and the nonoverlapping constraint. Several examples for different cases including symmetric arrays and a study on maximally sparse arrays are presented, which show the applicability and merit of the method.

On the RLSA antenna optimum design for DBS reception

An optimal design for the RLSA (radial line slot array) antenna useful for DBS reception is presented. Classical geometries and structures given in the literature are first used. It is found that in some cases these are not suitable. So, optimization techniques by using the right objective functions have been applied. Two different methods were developed and three antennas were designed. Numerical results and comparisons with other similar antennas give the advantages of our design.

Monte Carlo simulation for the prediction of the emission level from multiple sources inside shielded enclosures

The emission level from multiple sources inside shielded enclosures is presented. Fields are expressed through the dyadic Greens function. Integrated circuits are modeled as electric and magnetic dipoles. Induced electric current magnitudes on the walls of the enclosures are analytically calculated. Probabilistic models based on measurement data for various source configurations are derived and Monte Carlo simulations are produced. The results are used for the prediction of possible emission margins. Applications in PCB design are discussed and suggestions are made.

EM field measurements in the vicinity of an antenna park for radiation hazard purposes

An extensive set of measurements folr the purpose of mapping of the EM pollution in the vicinity of an Antenna Park is given. The EM power density is compared with three international standard limits. Special attention has been paid to residential areas. An increased EM pollution was found in areas where construction of buildings is planned for the future. In view of these plans, reformation of the park and spreadmg of the antennas have to be done.

Analysis of semi-infinite elliptic conical scatterer

The electromagnetic field scattered by a perfectly conducting elliptic cone is discussed. The problem is three-dimensional and is presented by the dyadic Green function, expressed by the help of Lame periodic and nonperiodic functions. Numerical results for the field and the current distribution are presented for some special cases.

A Stochastic Study of Large Arrays Related to the Number of Electrically Large Aperture Radiators

A study of the maximally sparse large planar arrays with electrically large elements is presented. The conditional probabilities of the element placements and their resulting auxiliary radiation integrals are derived. Through them the average and the directivity pattern formulas are also derived. Employing these formulas, we present a convex method that provides a solution to the maximally sparse problem when main lobe constraints are imposed on the directivity pattern. In particular, by taking the possible types of elements into account, we manage to obtain the lower bound of the directivity that an array should exhibit. This lower bound is analytically derived in the form of a Pareto (sub-) set that classifies the possible arrays into feasible and nonfeasible ones. This Pareto set can also enable tradeoff studies to be conducted without the need to consider the full range of every parameter. From the procedure, several acceptable combinations of elements are obtained. Simulation results, which confirm the methodology, are presented.