TY - JOUR
T1 - Parameter Inversion by a Modified Reflected Signal Reconstruction Method for Thin-Layered Media
AU - Li, Yixuan
AU - Yang, Xiaopeng
AU - Lan, Tian
AU - Liu, Renjie
AU - Qu, Xiaodong
N1 - Publisher Copyright:
© 2002-2011 IEEE.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - Parameter estimation of layered media is an important application of ground penetrating radar (GPR). However, in the case of thin-layer detection, the limitation of GPR vertical resolution can lead to overlapping reflected signals, which seriously deteriorates the detection results. To solve this problem, a modified reflected signal reconstruction method based on reflected signal reconstruction is proposed in this letter. First, a forward model of the GPR reflected signal is constructed using the generalized reflection coefficient definition. Then, the parameter inversion is transformed into an optimization problem. The residue cost function is defined as the error square between the model and the actual reflected signal, which is minimized by using genetic algorithm. Furthermore, the generalized reflection coefficient spectrum is feasible to estimate the parameters of each layer as the initial values of the parameters required by the algorithm, thus improving the accuracy and convergence speed of the algorithm. Numerical, experimental, and field tests demonstrate that the method has a high time resolution and antinoise capability in the inversion of layered media parameters.
AB - Parameter estimation of layered media is an important application of ground penetrating radar (GPR). However, in the case of thin-layer detection, the limitation of GPR vertical resolution can lead to overlapping reflected signals, which seriously deteriorates the detection results. To solve this problem, a modified reflected signal reconstruction method based on reflected signal reconstruction is proposed in this letter. First, a forward model of the GPR reflected signal is constructed using the generalized reflection coefficient definition. Then, the parameter inversion is transformed into an optimization problem. The residue cost function is defined as the error square between the model and the actual reflected signal, which is minimized by using genetic algorithm. Furthermore, the generalized reflection coefficient spectrum is feasible to estimate the parameters of each layer as the initial values of the parameters required by the algorithm, thus improving the accuracy and convergence speed of the algorithm. Numerical, experimental, and field tests demonstrate that the method has a high time resolution and antinoise capability in the inversion of layered media parameters.
KW - Generalized reflection coefficient
KW - genetic algorithm (GA)
KW - ground penetrating radar (GPR)
KW - thin-layered media
UR - http://www.scopus.com/inward/record.url?scp=85125300937&partnerID=8YFLogxK
U2 - 10.1109/LAWP.2022.3152843
DO - 10.1109/LAWP.2022.3152843
M3 - Article
AN - SCOPUS:85125300937
SN - 1536-1225
VL - 21
SP - 958
EP - 962
JO - IEEE Antennas and Wireless Propagation Letters
JF - IEEE Antennas and Wireless Propagation Letters
IS - 5
ER -