Interface identification using a GPR signal: a Monte Carlo Markov chain approach

Author

Coatanhay, Arnaud ; Szkolnik, Jean Jacques

Author_Institution

Lab. en Extraction et Exploitation d´´Infomation en Environement Incertain, ENSIETA, Brest, France

fYear

2002

fDate

2002

Firstpage

58

Lastpage

62

Abstract

This paper presents a new signal processing method to improve the identification of interface between different layered media, using a ground penetrating radar (GPR) recording. Our methodological approach is based on Monte Carlo Markov chain (MCMC) model. The deconvolution of the GPR signal is obtained in considering a stochastic estimation related to a maximum a posteriori criterion. The only known elements are the signal recorded from the GPR backscattering (one dimension approximation), and the order of the ARMA signal model for the emitted pulse.

Keywords

Markov processes; Monte Carlo methods; autoregressive moving average processes; backscatter; deconvolution; identification; maximum likelihood estimation; radar detection; radar signal processing; GPR signal; MCMC model; Monte Carlo Markov chain approach; Monte Carlo Markov chain model; backscattering; deconvolution; emitted pulse; ground penetrating radar; identification; interface identification; layered media; maximum a posteriori criterion; one dimension approximation; signal processing method; stochastic estimation; Backscatter; Deconvolution; Electromagnetic propagation; Ground penetrating radar; Integrated circuit modeling; Monte Carlo methods; Nonhomogeneous media; Reflectivity; Signal processing; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Radar Conference, 2002. Proceedings of the IEEE

Print_ISBN

0-7803-7357-X

Type

conf

DOI

10.1109/NRC.2002.999693

Filename

999693