Research On Optimization Method Of Unequally Spaced Tightly Coupled Array Antenna

Tightly coupled array is a kind of tightly arranged array,which plays an indispensable role in radar system and wireless communication systems.It uses the coupling between units to expand the bandwidth.In practical engineering,it is generally expected to use as few antenna elements as possible,such as sparse array,to achieve the performance index that the array needs to meet.However,the optimization of tight coupled arrays is not reported in the literature in practical applications,which is facing great challenges.In this paper,we do the following research on the optimization of tightly coupled array antenna layout:This paper presents a novel design method of irregular tightly coupled arrays.The sparsity technique is introduced to optimize the distribution of tightly coupled array elements by adjusting the width of the radiation wall at the edge of the element.The irregular sparse array makes the array element have more freedom of element position and improves the performance.In addition,due to the coupling between tightly coupled array elements,the traditional optimization method based on array factor formula is no longer applicable.In order to quickly and accurately analyze the advantages of unequally spaced tightly coupled arrays,two optimization algorithms are proposed:In the first method,the characteristic mode method(CM)is used to calculate the fitness function of genetic algorithm(GA).The fitness function of GA is calculated by full wave simulation.As an accurate full wave simulation analysis method,the moment of method is often used to analyze the antenna radiation problem.In order to save time and memory in the optimization process,this chapter adopts the CM based on the method of moment(Mo M),which takes the characteristic current as the global basis function.This method can not only reduce the unknown quantity and save time and memory,but also ensure the solution accuracy.In addition,in order to accelerate the optimization to a greater extent,message passing interface(MPI)parallel strategy is adopted in this chapter.The second algorithm introduces space mapping(SM),which is an improvement of the first algorithm.The first method needs a lot of time to calculate the fitness function.Therefore,in order to speed up the optimization and further analyze the advantages of the larger scale unequal spacing tightly coupled array,the space mapping algorithm is introduced.The fitness function in the first method is used as the fine model of the space mapping algorithm.In this paper,we proposed an improved method of pattern extraction for active cells as the fitness function of the GA in the coarse model optimization.The mapping relationship between the fine models can reduce the optimization times of the fine models.In addition,in order to accelerate the optimization,the MPI parallel strategy is adopted for the fine model.The fourth chapter introduces the algorithm in detail.In this paper,examples are given to verify that the tightly coupled array with unequal spacing can reduce the number of array elements without much gain loss,so as to simplify the feed network and save the processing cost.In addition,the unequally spaced tightly coupled array has the advantages of reducing the peak sidelobe level and increasing the gain.