Synthesis and Failure Correction of Flattop and Cosecant Squared Beam Patterns in Linear Antenna Arrays

Abstract

Abstract-This paper deals with the synthesis of flattop and cosecant squared beam patterns using the firefly algorithm which is based on metaheuristics. This synthesis is followed by the correction of the radiation patterns when unfortunate malfunctioning of the individual elements in the array occurs. The necessary attention is given to the recovery process, with due emphasis on reduction of side lobe level, ripple and the reflection coefficient. Simulation in Matlab shows a successful employment of the firefly algorithm in producing voltage excitations of the good elements necessary for the recovered patterns. The performance of the firefly algorithm in failure correction is validated by duly comparing it with a standard benchmark.Keywords-cosecant squared pattern, failure correction, firefly algorithm, flattop beam pattern, linear array, side lobe level.

Failure Correction of a Linear Array of Coupled Parallel Vertical Dipole Antennas

“…The servicing of faulty elements is time and cost-intensive. The idea of replacing failed elements is substituted by modifying beam weights [2]- [9], i.e. amplitudes and/or phase excitations of the remaining non-failed elements to the extent that the corrected radiation pattern looks the same as the original (non-failed) pattern.…”

“…In this paper, correction of the antenna array performance was achieved by minimizing SLL with null steering, without considering the mutual coupling effect. For a linear antenna array, the shaped beam patterns and the failure corrections are described in [9]. Also, the synthesis of flattop and cosecant squared beam patterns, along with failure correction, is discussed to decrease SLL, as well as to deviate ripple in the shaped beam region.…”

Embryonics Based Phased Array Antenna Structure With Self-Repair Ability

“…Therefore, researchers have paid great attention to the excitation recalculation, and different calculating methods have been studied. Some intelligence optimization algorithm based schemes have been reported, including genetic algorithms (GA) [7], [12]- [14], adaptive genetic algorithm (AGA) [15], firefly algorithm (FA) [12], [16], [17], particle swarm optimization (PSO) [10], [18], quantum particle swarm optimization (QPSO) [19], bacteria foraging optimization (BFO) [10], cuckoo search algorithm [20], cuckoo search-chicken swarm optimization (CSCSO) [21], flower pollination algorithm [22], [23], recursive intelligent optimizer (RIO) [24], whale optimization algorithm (WOA) and chaotic whale optimization algorithm (CWOA) [25], grey wolf optimizer hybridized with an interior point algorithm [26], greedy sparseness constrained optimization (GSCO) technique [27], differential evolution (DE) algorithm [28], Taguchi algorithm [29]. Iterative Fourier transform (IFT) [5], [30], [31] and quantized IFT (QIFT) [32] is another method for calculating the excitation of remaining elements.…”