Time Delay and Permittivity Estimation by Ground-Penetrating Radar With Support Vector Regression
- 25 September 2013
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Geoscience and Remote Sensing Letters
- Vol. 11 (4) , 873-877
Abstract
In the field of civil engineering, sounding the pavement layers is classically performed using standard ground-penetrating radar, whose vertical resolution is bandwidth dependent. The layer thicknesses are deduced from both the time delays of backscattered echoes and the permittivity of layers. In contrast with conventional spectral analysis approaches, this letter focuses on one of the machine learning algorithms, namely, the support vector machine, to perform time delay estimation and dielectric constant estimation of the medium from backscattered radar signals. This letter shows the super time resolution capability of such technique to resolve overlapping and fully correlated echoes within the context of thin pavement layer testing.Keywords
This publication has 28 references indexed in Scilit:
- Support Vector Regression method applied to thin pavement thickness estimation by GPR2012
- Automatic Classification of Ground-Penetrating-Radar Signals for Railway-Ballast AssessmentIEEE Transactions on Geoscience and Remote Sensing, 2011
- Automatic Analysis of GPR Images: A Pattern-Recognition ApproachIEEE Transactions on Geoscience and Remote Sensing, 2009
- A smart antenna system for direction of arrival estimation based on a support vector regressionIEEE Transactions on Antennas and Propagation, 2005
- An Electromagnetic Approach Based on Neural Networks for the GPR Investigation of Buried CylindersIEEE Geoscience and Remote Sensing Letters, 2005
- Travel-Time Prediction With Support Vector RegressionIEEE Transactions on Intelligent Transportation Systems, 2004
- A tutorial on support vector regressionStatistics and Computing, 2004
- Practical selection of SVM parameters and noise estimation for SVM regressionNeural Networks, 2004
- An overview of statistical learning theoryIEEE Transactions on Neural Networks, 1999
- The Nature of Statistical Learning Theory1995