Abstract

Compositing of ground penetrating radar (GPR) scans of differing frequencies have been found to produce cleaner images at depth using the Gaussian mixture model (GMM) feature of the expectation-maximization (EM) algorithm. GPR scans at various heights (“Stand Off”), as well as ground-based scans, have been studied. In this paper, we compare the GPR response from a chirp excitation function-based radar with the response from the EM GMM algorithm compositing process, using the same mix of frequencies. A chirp excitation pulse was found to be effective in delineating the defined buried object, but the resulting image is less sharp than the GMM EM method.

Highlights

  • Methods to produce sharper delineated objects at depth using ground penetrating radar (GPR) is a continuing topic of study

  • We explore using a chirp excitation function-based radar to replace the multiple frequency scans used in the EM Gaussian mixture model (GMM) algorithm analysis, comparing the result with EM processed scans

  • Our EM developed method, the compositing of GPR scans [1,2,3,4,5], was preceded by Dougherty et al [20] who focused on a method to subtract the direct arrival pulse and combine each signal weighted with equal magnitudes

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Summary

Introduction

Methods to produce sharper delineated objects at depth using ground penetrating radar (GPR) is a continuing topic of study. Imaging results from ground-based scans and scans at various heights have benefited from the Gaussian mixture model (GMM) feature of the expectation-maximization (EM) algorithm technique [1,2,3,4,5] of combining scans at differing frequencies over the same terrain. We explore using a chirp excitation function-based radar to replace the multiple frequency scans used in the EM GMM algorithm analysis, comparing the result with EM processed scans.

Related Work
Expectation-Maximization Data Mixture Process
Chirp Excitation Function-Based Radar Signal
5.5.Results
Defined Simulated Analysis Space 1
Defined
14. Defined space
Defined Simulated Analysis Space 3
Conclusions
Computer Modeling Verification
Target
GprMax
Expectation-Maximization
Finite-difference time-domain analysis results the 2-D normally model using
Expectation-Maximization Algorithm Test Case
Compositing ‘Standoff’
Full Text
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