Novel dual compressive sensing method for solving the monostatic scattering problems of three-dimensional target

ABSTRACT

According to the conventional dual compressive sensing method is limited to solving the electromagnetic scattering problems for two-dimensional targets, the Characteristic Basis Functions (CBFs) combined with the Compressive Sensing (CS) technique are proposed to solve the monostatic scattering problems for the three-dimensional (3D) target in this paper. The approach considered in this paper consists of successfully transforming the discrete surface-induced currents of Rao-Wilton-Glisson (RWG) basis functions using CBFs, which successfully enhance the sparsity of the induced currents. Then, the novel dual compressive sensing framework is obtained by applying the CBFs and discrete cosine transform matrices as sparse bases, respectively. The corresponding numerical calculations demonstrate that the proposed method can be used as an effective technical route to solve the monostatic scattering problem effectively.

KEYWORDS:

• Characteristic basis functions
• dual compressive sensing
• singular value decomposition
• sparse basis

Acknowledgments

This work was supported in part by the Anhui Provincial Natural Science Foundation under Grant no.2108085MF200, in part by the National Natural Science Foundation of China under Grant no.62071004, in part by the Natural Science Research Project of AnhuiEducational Committee under Grant no.2022AH051583, and the Graduate Innovation Fund of Anhui University of Science and Technology under grant no.2022CX2083

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the Anhui Provincial Natural Science Foundation under Grant, National Natural Science Foundation of China under Grant, Natural Science Research Project of Anhui Educational Committee under Grant [no.2108085MF200, no.62071004, no.2022AH051583]; Graduate Innovation Fund of Anhui University of Science and Technology under grant [no.2022CX2083].