An efficient position optimization method based on improved genetic algorithm and machine learning for sparse array

Abstract

Sparse array antennas are significant in communication and radar systems due to the advantages of high resolution, low complexity, and immunity to interference. In this letter, a novel array synthesis method based on an improved genetic algorithm (IGA) is proposed to optimize the antenna position in the sparse array. Different from the traditional generic methods with high computational complexity, a method combined with Gaussian process regression (GPR) prediction is proposed to optimize the antenna position with low computational complexity. The proposed method can minimize the peak sidelobe level (PSLL) during the beam scanning procedures. Moreover, the minimum antenna spacing constraint is also considered to ensure a physically achievable solution. Simulation results show that the proposed method can achieve a lower PSLL with fewer iterations and provide a global optimization solution for large array synthesis compared with existing methods. Furthermore, a prototype of the sparse array principle optimized by IGA is designed. Measurement results indicate that the optimized sparse array has good synthesis performance in high frequency.