Concepedia

Thin-Pavement Thickness Estimation Using GPR With High-Resolution and Superresolution Methods

Cédric Le Bastard, Vincent Baltazart, Yide Wang, J. Saillard

IEEE Transactions on Geoscience and Remote Sensing · 2007 · 153 citations · 29 references

TL;DR

Ground‑penetrating radar is traditionally used to determine pavement layer thickness by measuring backscatter time delays and dielectric constants, but its resolution is limited by bandwidth. This study aims to enhance the time resolution of GPR for pavement thickness estimation through superresolution and high‑resolution techniques. The authors implement a parametric approach and five subspace algorithms—ESPRIT, MUSIC, Min‑Norm, root‑MUSIC, and root‑Min‑Norm—and compare their resolution power and root‑mean‑square error in thickness estimation. Computer simulations and far‑field radar measurements demonstrate the effectiveness of these methods, showing improved resolution and accuracy over conventional GPR.

Abstract

In the field of civil engineering, sounding the top layer of carriageways, i.e., the pavement layer, is classically performed using standard ground-penetrating radar (GPR), whose resolution is bandwidth dependent. The layer thickness is deduced from both the time delays of backscattered echoes and the known dielectric constant of the medium. This paper focuses on superresolution and high-resolution techniques, which serve to improve the time resolution of GPR signals, and presents a parametric technique and five subspace methods, namely, estimation of signal parameters via rotational invariance techniques (ESPRIT), multiple-signal classification (MUSIC) algorithm, Min-Norm, and their polynomial versions root-MUSIC and root-Min-Norm. The performance of these algorithms will be compared in terms of resolution power as well as root-mean-square error on the estimated thickness. The paper also presents the results of computer tests and radar measurements in the far field.