Optimal Design of Low Sidelobe Sparse Linear Arrays

Abstract

To optimize the antenna performance in a sparse linear array (SLA) subject to specific constraints of the antenna aperture, number of elements and element spacing, this paper proposes an improved Differential Evolution (DE) algorithm integrating the Cauchy-Gauss mutation strategy. The initial value of the algorithm is generated through the chaotic Piecewise map, while the scaling factor is adjusted dynamically in line with the iterations and fitness values. When the algorithm indicates signs of premature convergence, the Cauchy-Gauss mutation strategy is applied to the population to escape local optima, so as to produce the global optimal solution. Standard function tests validate the effectiveness of the algorithm, proving its excellent accuracy and global search performance. Three diverse antenna-based simulation instances show that the improved algorithm can effectively reduce the peak sidelobe level (PSLL), thus elevating the antenna performance.