Radiation Pattern Synthesis of the Coupled almost Periodic Antenna Arrays Using an Artificial Neural Network Model

Hamdi Bilel,Aguili Taoufik

doi:10.3390/electronics11050703

Abstract

This paper proposes radiation pattern synthesis of almost periodic antenna arrays including mutual coupling effects (extracted by Floquet analysis according to our previous work), which in principal has high directivity and a large bandwidth. For modeling the given structures, the moment method combined with the generalized equivalent circuit (MoM-GEC) is proposed. The artificial neural network (ANN), as a powerful computational model, has been successfully applied to antenna array pattern synthesis. Our results showed that multilayer feedforward neural networks are rugged and can successfully and efficiently resolve various distinctive, complex almost periodic antenna patterns (with different source amplitudes) (in particular, both periodic and randomly aperiodic structures are taken into account). An ANN is capable of quickly producing the synthesis results using generalization with the early stopping (ES) method. Significant advantages in speed and memory consumption are achieved by using this method to improve the generalization (called early stopping). To justify this work, several examples are shown and discussed.

Highlights

IntroductionSmart antenna arrays involve intelligent systems, including genetic algorithms and neural networks, to synthesize the radiation pattern [6,7,8,9,10]
Today, the synthesis of the radiation patterns of almost periodic planar structures is the subject of research, mostly in space and defense applications, communication systems and electronic devices such as phased array radar systems, frequency selective surface (FSS) applications [1], millimeter waves, and optical wave regions [2,3,4,5].In general, smart antenna arrays involve intelligent systems, including genetic algorithms and neural networks, to synthesize the radiation pattern [6,7,8,9,10]
This study focused essentially on creating the radiation pattern associated with the planar almost periodic structures [37,38,39]

Summary

Introduction

Smart antenna arrays involve intelligent systems, including genetic algorithms and neural networks, to synthesize the radiation pattern [6,7,8,9,10]. The genetic algorithm (GA) is used basically for sidelobe reduction in antenna pattern synthesis [11,12,13]. Artificial neural networks (ANNs) have been employed for various purposes, e.g., as pattern recognition systems, and have been put to use for input–output mapping, system identification, adaptive prediction, etc. Several other methods of synthesis of coupled periodic and aperiodic arrays have shown their reliability in electromagnetic calculations [17,18], one of which is MoM GeC, which we use as a comparison method The present work is centered on a neural network technique for the synthesis of almost periodic network models, and we point out their most prominent features and distinctive characteristics [14,15,16].

Objectives

Methods

Results

Conclusion