Title :
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
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;
Conference_Titel :
Radar Conference, 2002. Proceedings of the IEEE
Print_ISBN :
0-7803-7357-X
DOI :
10.1109/NRC.2002.999693
