Sidelobe Level Reduction in Linear Array Pattern Synthesis Using Particle Swarm Optimization

Abstract

The design of nonuniformly spaced linear array antennas using Particle Swarm Optimization method is considered. The purpose is to match a desired radiation pattern and improve the performance of these arrays in terms of sidelobe level. This performance criterion determines how well the system is suitable for wireless communication applications and interference reduction. Two approaches are considered: in the first, the design of element placement with the constraint of array length being imposed is performed. The second is based on element position perturbation starting from a uniform element distribution. Many examples are treated to show the effectiveness of the designs and the effect some other parameters might have on the overall performance of the array.

A Review on Antenna Radiation Pattern Synthesis

“…The design of nonuniformly spaced linear array antennas is done using Particle Swarm Optimization method [5]. The purpose of this is to match a desired radiation pattern and improve the performance in terms of sidelobe level.…”

“…However, the first approach puts a further constraint on the array length which is a practical choice. The designs satisfy the requirements of wireless communications systems and present an optimal geometry of antenna system that provides minimal signal-of-non-interest (interferers) reduction capability and enhances the signals-of-interest (very high directivity) [5].…”

Pattern Synthesis Using Hybrid Fourier-Neural Networks for Ieee 802.11 Mimo Application

“…Different synthesis techniques, such as genetic algorithm [1] and particle swarm optimization algorithm [2] have been successfully used for reducing the sidelobe level. In [3] the authors present sidelobe level reduction in linear array pattern synthesis using particle swarm optimization (PSO). The synthesis of radiation patterns of linear arrays using the Schelkunoff method and genetic algorithms (GAs) is presented in [25,26].…”

Optimal Multiobjective Design of Digital Filters Using Taguchi Optimization Technique

“…These are referred to as global optimizers while the more familiar, traditional techniques such as conjugate gradient and the quasi-Newtonian methods are classified as local optimizers. The distinction between local and global search of optimization techniques is that the local techniques produce results that are highly dependent on the starting point or initial guess, while the global methods are totally independent of the initial conditions [42]. Though they possess the characteristic of being fast in convergence, local techniques, in particular the quasi-Newtonian techniques have direct dependence on the existence of at least the first derivative.…”

“…Though they possess the characteristic of being fast in convergence, local techniques, in particular the quasi-Newtonian techniques have direct dependence on the existence of at least the first derivative. In addition, they place constraints on the solution space such as differentiability and continuity; conditions that are hard or even impossible to satisfy in some situations [42].…”