66 / 2021-07-19 11:20:35

Dispersion compensation strategy based on sparse Bayesian learning in terahertz nondestructive evaluation

Special Session > SS2 - NDT&E and Intelligent Inspection System

Terahertz time-of-flight tomography (THz TOFT), as a potential nondestructive evaluation (NDE), has attracted great attention for the characterization of various non-metallic materials due to its superior temporal and spatial resolution and high penetration. However, when THz wave penetrates the material, the attenuation and dispersion will inevitably appear, especially for the thickness and high loss materials, which will degrade the minimum resolvable performance of THz wave and limit its wide application in super resolution characterization. Therefore, in this work, a novel strategy based on the sparse Bayesian learning is proposed to address the dispersion problem in THz NDE. First, the THz sparse dispersion model is established based on the sparse prior of THz echo signal. Second the double overcomplete dictionaries including a parametric dispersion dictionary and a parametric non-dispersion dictionary are constructed specifically based on the double Gaussian mixture model (DGMM). Then, the sparse Bayesian learning (SBL) method is applied to solve the sparse inverse problem. Finally, numerical simulations and experiments validate the effectiveness and applicability of the proposed strategy for suppressing the dispersion of THz wave in THz NDE.