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

Yuan Luo; Guang You Fang

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 proceeding › Conference contribution

12 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 language	English (US)
Title of host publication	2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Pages	5432-5436
Number of pages	5
State	Published - 2005
Externally published	Yes
Event	International Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China Duration: Aug 18 2005 → Aug 21 2005

Publication series

Name	2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

Conference

Conference	International Conference on Machine Learning and Cybernetics, ICMLC 2005
Country/Territory	China
City	Guangzhou
Period	8/18/05 → 8/21/05

Keywords

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

ASJC Scopus subject areas

General Engineering

Cite this

@inproceedings{da33e0c2e67047cb8285d53fa5d868fd,

title = "GPR clutter reduction and buried target detection by improved Kalman filter technique",

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.",

keywords = "Background clutter removal, Buried targets detecting, Ground penetrating radar (GPR), Kalman filter",

author = "Yuan Luo and Fang, {Guang You}",

year = "2005",

language = "English (US)",

isbn = "078039092X",

series = "2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005",

pages = "5432--5436",

booktitle = "2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005",

note = "International Conference on Machine Learning and Cybernetics, ICMLC 2005 ; Conference date: 18-08-2005 Through 21-08-2005",

}

Luo, Y & Fang, GY 2005, GPR clutter reduction and buried target detection by improved Kalman filter technique. in 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005. 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005, pp. 5432-5436, International Conference on Machine Learning and Cybernetics, ICMLC 2005, Guangzhou, China, 8/18/05.

GPR clutter reduction and buried target detection by improved Kalman filter technique. / Luo, Yuan; Fang, Guang You.
2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005. 2005. p. 5432-5436 (2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - GPR clutter reduction and buried target detection by improved Kalman filter technique

AU - Luo, Yuan

AU - Fang, Guang You

PY - 2005

Y1 - 2005

N2 - 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.

AB - 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.

KW - Background clutter removal

KW - Buried targets detecting

KW - Ground penetrating radar (GPR)

KW - Kalman filter

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M3 - Conference contribution

AN - SCOPUS:28444466241

SN - 078039092X

SN - 9780780390928

T3 - 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

SP - 5432

EP - 5436

BT - 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

T2 - International Conference on Machine Learning and Cybernetics, ICMLC 2005

Y2 - 18 August 2005 through 21 August 2005

ER -

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

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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Cite this