ANN Algorithm-Based Conformal Conical Antenna Array Synthesis and Optimization: Beamforming application

Abstract

In this paper, a new conformal array antenna conical design has been proposed. It can electronically steer the main beam in azimuth and elevation to cover a hemispherical mesh surrounding the given antennas configuration. To achieve the desired beam configuration, it is absolutely essential to select the appropriate antenna control parameters. To this aim, the optimization of the neural technique is adapted to the optimal set of matching parameters. For more details, this work discusses the use of an efficiently designed artificial neural network (ANN) to model and synthesize the radiation pattern of an adaptive uniform cone lattice beam. Let's assume, then, that the learning database of the network has a finite number of target selections at certain feasible angles. Utilized a feedforward artificial neural network model called a multilayer perceptron (MLP), generates a set of appropriate outputs based on sets of input data. The obtained systems are performed in Matlab and the suggested conforming conical array shows a significant enhancement versus the existent structures in terms of 3-D scanning, size, directivity, HPBW and SLL reducing. The employed neural technique revealed its effectiveness in improving performance using the conical isotropic antenna array. To approve this work, several examples are shown. The proposed method can also be utilized for other kinds of conformal phased arrays such as spherical or cylindrical arrangements.