Synthesis of Sparse Arrays Based On CIGA (Convex Improved Genetic Algorithm)

Abstract

In this paper, a novel hybrid algorithm on beam pattern synthesis of sparse arrays is proposed，which aims at minimizing the peak sidelobe level (PSLL). Sparse arrays is significant technology, which possessing many advantages and wide application prospect. Sparse arrays can provide higher spatial resolution and relatively lower sidelobe than general arrays, but it is necessary to solve the multi-constraint problem of nonconvex nonlinear. Thus, we introduce the Convex Improved Genetic Algorithm (CIGA) that can adjust the positions and the excitation coefficients of arrays to achieve the minimum PSLL. Improved Genetic Algorithm, which is suitable for beam pattern synthesis of sparse arrays, is adopted to adjust the positons of arrays to achieve the local optimum PSLL at first, then convex optimization method is used to set the excitation coefficients in expectation of reaching the minimum PSLL. Simulation results verify that the minimum PSLL has been successfully achieved by using the CIGA and prove that the CIGA is superior to the published methods.