GPR clutter reduction and buried target detection by improved Kalman filter technique

Yuan Luo*, Guang You Fang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

The reduction of background signal, or clutter, from Ground Penetrating Radar (GPR) measurements is an area of active research. The weak reflection signal obtained from subsurface targets is usually blurred by such strong clutter, which mainly comes from flat or rough ground surfaces, underground inhomogeneities, and coupling between the transmitting and receiving antennae. In this paper, the improved Kalman filter techniques have been studied and applied to reduce the background interference signals and detect the buried targets in GPR dataset. The effectiveness and validities of the proposed improvement methods in this paper for processing GPR detection data are studied. The processed results prove that the proposed methods are effective and adaptive for reducing clutter and detecting subsurface targets.

Original languageEnglish (US)
Title of host publication2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Pages5432-5436
Number of pages5
StatePublished - 2005
Externally publishedYes
EventInternational Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China
Duration: Aug 18 2005Aug 21 2005

Publication series

Name2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

Conference

ConferenceInternational Conference on Machine Learning and Cybernetics, ICMLC 2005
Country/TerritoryChina
CityGuangzhou
Period8/18/058/21/05

Keywords

  • Background clutter removal
  • Buried targets detecting
  • Ground penetrating radar (GPR)
  • Kalman filter

ASJC Scopus subject areas

  • Engineering(all)

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