Enhancing ASED Basis Function Method With Compressive Sensing for Fast Analysis of Large-Scale Finite Periodic Arrays

Abstract

The accurate subentire-domain (ASED) basis function method has been demonstrated to be effective in analyzing large-scale finite periodic array problems. However, as the array size increases, both the full calculation of the impedance matrix and the solution of the reduced matrix becomes increasingly time-consuming. To address this issue, a novel compressive sensing (CS) model employing the ASED basis functions as a sparse base is presented in this letter. On one hand, the reduced matrix equation is replaced by the CS equation, which can be more efficiently constructed and solved. On the other hand, only very few rows of impedance matrix need to be filled for constructing the measurement matrix. Numerical examples are calculated to verify the computational efficiency and accuracy of the proposed method.