LAUSR

In the scenario where the echo hyperbolas of different targets intersect or the opening structure of the hyperbola is destroyed, the current B-scan image interpretation model has the problems of missing detection and unable to make full use of the hyperbolic information. The hyperbola needs to be identified manually or using neural network methods. The recognition accuracy is difficult to guarantee. To solve the above problem, a new ground-penetrating radar (GPR) B-scan interpretation model based on the hyperbolic trend is proposed in this article. The model consists of four parts, including preprocessing method, hyperbolic trend clustering algorithm (HTCA), restricted algebraic distance fitting and cluster center (RADFCC) algorithm, and hierarchical filtered back-projection (HFBP). First, the GPR B-scan image is converted into a binarized image based on the ratio of the main lobe and sidelobe of the echo. Second, the points in the binarized image are classified into hyperbolic column segment clusters based on the hyperbolic trend, and further denoised according to the main lobe width of the Ricker wave. Third, by analyzing the characteristics of the GPR in the ground coupling mode, the restricted hyperbola fitting algorithm is used to fit the classified hyperbola column segment clusters to speed up the convergence of the fit and avoid the result from falling into a local optimum. Fourth, the target’s location is preliminarily estimated using the parameters of the hyperbola and the cluster center is established through the location and radius of the target to obtain the accurate estimation of the target and the dielectric constant of the underground. Finally, an imaging algorithm is applied to invert the subsurface image based on the dielectric constant of the subsurface medium. The effectiveness of the proposed model has been demonstrated on both the simulated and real datasets.