Optimizing Performance of Antenna Arrays with Clustered Fractal Shapes for Multiband Applications

Abstract

Fractal antennas are mainly used in multiband applications. However, these types of arrays suffer from numerous disadvantages, such as high sidelobe levels, low directivity, poor taper efficiency, and high design computational complexity. In this paper, the conventional fractal procedures are redesigned and efficient clustered subarrays are deployed, such that their multiband properties are maintained while simultaneously achieving significant improvements in radiation characteristics. A genetic optimization algorithm is used to find the optimal clustered fractal shapes and their associated amplitude distributions, such that the sidelobe levels are minimized at the narrower beam width, i.e. maximum feasible directivity. Since the optimization process is carried out at the clustered level, it can be represented by merely a few variables, which solves the problem of time intensity. Simulation results confirm the superiority of the proposed clustered fractal array, where the sidelobe level has been reduced to more than -10 dB over a wide range of frequencies. Directivity and taper efficiency have been improved by more than 6 dB and 50%, respectively, in comparison to the parameters of conventional, original fractal arrays. Moreover, the proposed fractal array pattern offers an additional advantage, as it is capable of wide sidelobe nulling at some undesired directions.