Enhancing CBFM With Adaptive Frequency Sampling for Wideband 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 wideband scattering from objects buried in layered media. Conventionally, the CBFM is implemented 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 (FSs) are employed with uniform sampling. To mitigate this issue and improve efficiency, the AFS algorithm is proposed to select FSs with a reduced number via an iterative process based on error-estimators. The vector-fitting (VF) algorithm is used to derive the rational model, which allows to generate the scattered fields in the band efficiently. The key feature of the proposed AFS is it minimizes the number of the FSs and guarantees the convergence of the solution simultaneously. Numerical results demonstrate that the number of required samples is significantly reduced without compromising the accuracy of the solution.

Automated summary

An adaptive frequency sampling (AFS) strategy is proposed to reduce the solution time for wideband scattering from objects buried in layered media. The AFS algorithm selects frequency samples (FSs) based on error-estimators, minimizing the number of required samples while guaranteeing solution convergence. Numerical results show significant reduction in sample requirements without compromising accuracy.