Sparse Synthesis And Optimization Of Array Antennas

The array antennas are of great significance in modern radar and wireless communication systems.In terms of engineering applications,reducing cost and complexity of the antenna system while maintaining high performance is a key consideration.It is desirable to use fewer elements to achieve the performance demands required by the antenna array with the same aperture,sparse array techniques are one of the ways to implement.The sparse arrays can achieve the desired radiation pattern with fewer number of elements,while at the same time reducing the mutual coupling effect.Therefore,how to synthesis and optimize the sparse arrays more effectively has become a research hotspot.In this paper,to achieve a better radiation pattern with fewer array elements,the matrix beam theory and gray wolf optimization algorithm are applied to the sparse synthesis and optimization of antenna arrays.The main contents of the thesis are as follows:In the first part,we introduce the basic theory of the array antenna in detail.Then analyze the structure of planar array and conformal array,at the end of that derive its mathematical optimization model.At the last,summarize the comprehensive problems of array antenna and classic optimization method.The second part studies the synthesis and optimization of antenna array based on the unitary matrix pencil method.First of all,from the perspective of signal processing,the matrix pencil method is introduced,and then used it to optimize the sparse array.The calculations involved in the matrix pencil method are in the complex domain,which will cause errors in the fitting results.Aiming at this defect,a unitary matrix pencil method based on the principle of unitary transformation is proposed to sparse antenna array.Some shaped-beam patterns of sparse array antennas verify the superiority of the algorithm's performance.In the third part,to realize the sparse synthesis and optimization of the conformal array,an optimization algorithm based on the hybrid of gray wolf algorithm and particle swarm algorithm(GWO-PSO)is proposed.Establish the mathematical models of uniform circular array,uniform cylindrical array and uniform hemispherical array with the constrain of array aperture,the number of array elements,and the minimum distance between adjacent array elements.To reduce the complexity of optimization,it is essential to reduce the search dimensional space of variables.The minimum peak sidelobe level is the sparse conformal array's optimization target.The opposition-based learning strategy is used to update the gray wolf algorithm initialization population,and the speed update method of the particle swarm algorithm is combined with the gray wolf optimization algorithm.The improved gray wolf algorithm can effectively solve the sparse optimization problem of conformal array which could simplify the computational complexity while achieving lower sidelobe.Simulation examples have proved its advantages.