Enhancing CBFM with Adaptive Frequency Sampling for Wide-Band Scattering from Objects Buried in Layered Media
An adaptive frequency sampling (AFS) strategy is proposed in conjunction with characteristic basis function method (CBFM) to investigate the problem of wide-band scattering from objects buried in layered media. Conventionally, the CBFM is implemented in the method of moments (MoM) formulation to reduce the solution time at a single frequency. However, wide-band analysis of the above problem is still time consuming when a large number of frequency samples are employed, together with uniform sampling. To mitigate this issue and speed up the process, we propose the AFS algorithm, which selects the frequency samples via an iterative process involving the error-estimates and in turn guarantees the convergence of the wide-band solution process. These samples are used as inputs to the vector fitting (VF) algorithm, which obtains a rational model and subsequently derives the scattered fields in the frequency range of interest efficiently. Numerical results are included to demonstrate that the number of required samples is significantly reduced without compromising the accuracy of the solution.