Rafael G. Ayestarán

Synthesis of Passive-dipole Arrays with a Genetic-neural Hybrid Method

Neural networks and genetic algorithms are combined in a new hybrid technique able to perform synthesis tasks over passive-element antenna arrays, whose radiation pattern is defined by the coupling effects between the active and passive elements. The combination of both methods improves the results obtained using neural networks, also reducing the computation time required by genetic algorithms. Some significant results are presented.

Near field to far field transformation using neural networks and source reconstruction

Neural networks are proposed as an efficient tool able to perform near field to far field transformation, directly or through the application of the Theorem of Equivalence. A neural network can be trained to relate near field data with far field data or with an equivalent current distribution and then this one with the corresponding far field radiation pattern. Significant numerical examples are presented.

Support vector regression for the design of array antennas

The support vector regression (SVR) framework is proposed as the basis for a new antenna array synthesis technique suitable to solve practical problems with high accuracy using a more appropriate cost function for template-based specification handling. Two design examples are presented to show the efficiency of the proposed method.

Realistic Antenna Array Synthesis in Complex Environments Using a MOM-SVR Approach

The Support Vector theory combined with antenna array modeling based on the Method of Moments is proposed to provide an accurate synthesis method able to solve problems specified in terms of a template or a mask, taking into account all the real properties of the radiating structure and its environment, such as coupling effects or the presence of an obstacle, and without the need of the measurement of training patterns. Illustrative results are presented.

Synthesis of Phased Arrays in Complex Environments with the Multilevel Characteristic Basis Function Method

The aim of this paper is to present a method to carry out the synthesis of large phased arrays when they are afiected by complex environments which can in∞uence the radiation pattern. The synthesis is performed with the help of the Multilevel Characteristic Basis Function Method to calculate a matrix relating input voltages and the far fleld pattern samples. The method is illustrated with the synthesis of a Secondary Surveillance Radar antenna on a turret containing multiple obstacles.

High-accuracy neural-network-based array synthesis including element coupling

A novel neural-network (NN)-based technique for antenna array synthesis and a prototype of antenna array designed using it are presented. This technique is able to calculate the voltages that must be applied to the radiating elements considering all the physical properties of each element of the array and the coupling effects between all of them.

Systematic Framework for Reflectarray Synthesis Based on Phase Optimization

A new systematic synthesis framework for reflectarray antennas is discussed. Optimization based on the Levenberg-Marquardt algorithm is used to obtain the phase distribution of the reflection coefficients required on the reflectarray surface, in order to achieve the pattern specifications. A Local Multipoint Distribution System (LMDS) base station working in the 24.5–26.5 GHz frequency band has been proposed to evaluate the method. The 3D requirements are defined by the combination of the elevation and templates and considering a maximum acceptable ripple in the beam shaping. Some illustrative results are obtained.

Optimization framework on antenna arrays for near field multifocusing

This paper presents a new method for focusing on multiple targets simultaneously using antenna arrays. An optimization framework based on the Levenberg-Marquardt algorithm is used to concentrate the radiated field at certain positions. The proposed synthesis technique is a flexible method that can optimize different sets of parameters in the array, i.e., the weights (phase and/or magnitude) of the elements, their positions or all these features, in order to achieve this goal. Some illustrative examples using simulated and implemented antennas are addressed to evaluate this framework.

A MULTI-GPU SOURCES RECONSTRUCTION METHOD FOR IMAGING APPLICATIONS

A proflle reconstruction method using a surface inverse currents technique implemented on GPU is presented. The method makes use of the internal flelds radiated by an equivalent currents distribution retrieved from scattered fleld information that is collected from multiple incident flelds. Its main advantage over other inverse source-based techniques is the use of surface formulation for the inverse problem, which reduces the problem dimensionality thus decreasing the computational cost. In addition, the GPU implementation drastically reduces the calculation time, enabling the development of real time and accurate geometry reconstruction at a low cost.

Echo Characterization Based on Maximum-Likelihood Estimation for Antenna-Measurement Correction [Measurements Corner]

Antenna measurement systems could be affected by multipath effects resulting in reflected beams that might modify the measured values. In this paper, the use of the Maximum Likelihood estimator provides the basis of several post-processing methods able to estimate the echo parameters in imperfect antenna measurements. Once the reflections are characterized, these parameters are used to correct the measured values and to retrieve the actual radiation pattern corresponding to the antenna under test. Some different techniques are proposed in order to perform such cancellation. Experimental validation is also presented to show the accuracy of the described methods.