An efficient synthesis method for large unequally spaced sparse linear arrays based on surrogate models of subarrays

Abstract

The synthesis of large arrays with optimization of element locations including mutual coupling effects is always known as a computationally cost task. In this article, an efficient synthesis method for large unequally spaced sparse linear arrays in the presence of coupling effects based on artificial neural network (ANN) surrogate models of subarrays is presented. High-quality ANN surrogate models of arbitrary large linear arrays are built by grouping the surrogate models of subarrays together to tackle the “curse of dimensionality” of ANNs caused by the large number of array elements. The surrogate models of a complete group of subarrays with the given physical structures are built by several independent ANNs which are used to characterize the effects of mutual coupling on the active element patterns (AEPs) with variable element location distributions. After training, the surrogate models of subarrays can be called by an alternating convex optimization (ACO) method, so that arbitrary large arrays can be quickly synthesized including mutual coupling effects. Numerical experiments are conducted to determine the suitable parameters of subarray partition and build surrogate models of subarrays. The results of four examples for synthesizing unequally spaced linear arrays are demonstrated to validate the effectiveness of the proposed method.