A Sparse Bayesian Learning Approach for Vortex Electromagnetic Wave Three-dimensional Imaging in the Terahertz Band

Abstract

In the Inverse Synthetic Aperture Radar (ISAR) imaging system, when the terahertz radar transmits the wide bandwidth signal and vortex electromagnetic wave, Three-Dimensional (3D) high-resolution imaging can be achieved through information decoupling based on the differential radiation field formed by the vortex electromagnetic wave and the synthetic aperture formed by the relative movement of the radar and the target. Accordingly, a 3D imaging model based on the terahertz vortex electromagnetic wave ISAR is established. A new image reconstruction method is proposed based on the Sparse Bayesian Learning (SBL) and subregion amplitude threshold setting methods. The proposed method can significantly simplify the imaging procedure and reduce the computational load. The simulation results indicate that the proposed SBL method can achieve a higher resolution than the conventional fast Fourier transform-based method, and its reconstruction performance increases with an increase in the signal-to-noise ratio.

Keywords

• inverse synthetic aperture radar (isar)
• three-dimensional imaging
• terahertz radar
• sparse bayesian learning (sbl)
• vortex electromagnetic wave