Research On Sparse Array Antenna Optimization Technology Based On Arithmetic Optimization Algorithm

As one of the key components of systems such as wireless communication and radar,the performance of antennas directly affects the performance of these systems.In many scenarios,a single antenna cannot meet the requirements,while a sparse array antenna,with high degree of freedom of its elements,can achieve high gain,high directivity,and low sidelobe radiation characteristics with fewer elements.Therefore,it has been widely used in various radio devices.This thesis focuses on optimizing the element placement of sparse array antennas to meet the given electromagnetic radiation characteristics requirements.The main contributions of this thesis are as follows:(1)An improved arithmetic optimization algorithm is proposed to address the problem of easily getting stuck in local optima and low convergence accuracy in current algorithms.Based on the arithmetic optimization algorithm,the improved algorithm reconstructs the arithmetic optimization accelerator,improves the exploration and development update model,and introduces the elite mutation strategy.The effectiveness of the improved arithmetic optimization algorithm is verified by applying the standard test function,and the improved algorithm is then used for sparse array synthesis to prove its effectiveness in sparse array synthesis.(2)A fast calculation method for sparse array pattern based on Chirp Z transform is proposed to address the slow speed of current methods for computing the pattern of sparse array antennas.When optimizing sparse array antennas using meta-heuristic algorithms such as the improved arithmetic optimization algorithm,a significant amount of time is spent on the repetitive calculation of the array pattern.The proposed method converts the non-uniform sparse linear array into a uniform array by using the concept of virtual elements,and then uses Chirp Z transform to quickly calculate the pattern of the sparse array.The proposed method can be applied not only to sparse linear arrays but also to sparse planar arrays.Simulation results demonstrate that the proposed method can significantly speed up the pattern calculation of both sparse linear and planar arrays.(3)An adaptive matrix mapping rule is proposed to address the limitation of the existing matrix mapping rules in optimizing sparse planar arrays using improved arithmetic optimization algorithms.The minimum element spacing constraint severely restricts the degree of freedom of the array elements,resulting in the inability to search for many feasible solutions.The proposed rule overcomes this limitation by adapting the matrix mapping rules to the specific problem at hand.The proposed adaptive matrix mapping rule evaluates the current element distribution and adaptively adjusts only the element distribution that does not satisfy the minimum element spacing constraint,ensuring both element freedom and optimization accuracy while avoiding infeasible solutions.Based on the improved arithmetic optimization algorithm,the proposed adaptive matrix mapping rule is used in the sidelobe level suppression and null synthesis experiments of sparse planar arrays,and the results demonstrate the effectiveness of the proposed method.