An ANN-Based Synthesis Method for Nonuniform Linear Arrays Including Mutual Coupling Effects

Abstract

This paper proposes an artificial neural network (ANN)-based synthesis method for nonuniform linear arrays with mutual coupling effects. The proposed method can simultaneously optimize the location distributions and excitations of the elements. As is well known, for nonuniform linear arrays, the mutual coupling effects on the active element patterns (AEPs) and passive S parameters vary with the location distribution of the elements. However, few papers have focused on how to describe the relationship between the effects of mutual coupling and the location distribution of the elements. In this paper, we use several parallel and independent ANNs to characterize the effects of mutual coupling on the AEPs and passive S parameters in nonuniform linear arrays with variable location distributions. At the same time, to relieve the curse of dimensionality, we make use of the idea of subarrays, which makes it possible for the proposed method to model nonuniform linear arrays with a large number of elements. After building accurate ANN models, we make use of the adaptive differential evolution algorithm JADE to search for the optimal location distribution and the excitations of the elements, to satisfy the requirements for the radiation pattern and the active S parameters of the antenna array. In view of the fact that the mutual coupling effects are accounted for in the synthesis process, the proposed method probably exhibits more practical value in antenna array designs than in other synthesis methods. The validity and efficiency of the proposed method are confirmed based on three examples.

Keywords

• Artificial neural networks
• mutual coupling effects
• nonuniform linear antenna arrays
• subarray