Multiple-Measurement-Vectors Compressive Sensing Enhanced CBFM for Analyzing Wide-Angle Scattering Problems of Three-Dimensional Objects

In this letter, the multiple-measurement-vectors (MMV) compressive sensing (CS) model is presented to accelerate the singular value decomposition (SVD)-based characteristic basis function method (CBFM) for the solution of wide-angle scattering problems of three-dimensional objects. On one hand, the MMV-CS model is constructed to accelerate the generation of characteristic basis functions (CBFs), through which the whole CBFs can be simultaneously recovered from only a small percentage of them. Such obtained CBFs are more computationally efficient in the SVD procedure, with fewer CBFs retained compared to the traditional SVD-CBFM. On the other hand, the MMV-CS model is also constructed to accelerate the solution of induced currents, through which the mutual impedance matrices only need to be partially filled, and the reduced matrix equation is replaced by an overdetermined CS matrix equation, which can be efficiently solved. Numerical simulation results indicate that the computational efficiency is significantly improved by the MMV-CS model with sufficient accuracy compared to the traditional SVD-CBFM.