An Improved Synthesis of Sparse Planar Arrays Using Density-Weighted Method and Chaos Sparrow Search Algorithm

Abstract

This paper presents an improved approach for the synthesis of sparse planar arrays using density-weighted method and chaos sparrow search algorithm (CSSA) with multiple constraints. The approach consists of two stages and has an efficient way in dealing with multiple constraints, including the array aperture, number of elements, and minimum spacing between adjacent elements. In the first step, a density-weighted method is implemented to generate a thinned array structure by introducing the weighted coefficient based on the distribution function of a uniform full array to the elements, which can reduce the time and improve the efficiency of the optimization of sparse matrices. On the basis of a density-weighted array, tent chaotic mapping is introduced to initialize the population, then sparrow search algorithm (SSA) is used to further optimize the location of each element of the array antenna to search for the global optimal solution. As a consequence, the proposed method exhibits improved flexibility with respect to other analytical techniques, and reduces the computational cost of the optimization algorithm. Numerical validation results demonstrate the effectiveness and reliability of the proposed method, showing that it can achieve better results than existing methods in the design of sparse planar array arrangements.