Rotary detection using ground penetrating radar based on a B-Scan compressed sensing imaging algorithm

Abstract

In this work, we propose an imaging algorithm applicable to the Ground Penetrating Radar (GPR) rotary detection scenario. The constructed sparse matrix is interpretable, strictly following the actual physical model, and the convex optimization objective function is not the conventional L1 norm but the inverse Huber norm. A solver with a primal-dual interior point method is used to solve the convex optimization problem.Moreover, the study proposes a new metric for evaluating imaging performance. Firstly, we compared different algorithms on simulation data, demonstrating the superiority of the proposed algorithm in multi-target imaging;subsequently, a reasonable interpretation of the weight values involved in the proposed metric was provided;then, to test the robustness of the algorithm, it was proven on data with different Peak Signal-to-Noise Ratio (PSNR) and different sampling rates;next, sensitivity analysis was conducted on the three parameters involved in the inverse Huber norm and beam width, and empirical parameter values were proposed;finally,the study selected appropriate parameters within the optimal range of the proposed algorithm to perform imaging comparisons on experimental data.We look forward to this work promoting the development of the GPR detection field.