Multilevel Characteristic Basis Function Method Combined with Compressive Sensing for Analyzing Electrically Large Scattering Problems

For the pursuit of solving the electrically large scattering problems quickly and efficiently, an algorithm combining multilevel characteristic basis function method (MLCBFM) and compressive sensing (CS) is proposed for calculating the radar cross section of electrically large objects. According to MLCBFM, the surface of the object is first divided into large subdomains. Similarly, more levels of small subdomains are obtained by multilevel division, and the characteristic basis functions (CBFs) of each large subdomain can be obtained by those of small subdomains. Then, the obtained CBFs are taken as the sparse basis functions of compressive sensing theory, and the total impedance matrix which includes all large subdomains is evenly extracted as a measurement matrix. Finally, the coefficient of the unknown current can be obtained by implementing a recovery algorithm. The results of numerical simulations show that the algorithm of MLCBFM combined with CS has the same level of accuracy but higher efficiency than the traditional MLCBFM.