Clutter reduction techniques for GPR based buried landmine detection

Tesfamariam,; Gebremichael, T.; Mali, Dilip; Zoubir, Abdelhak M.

doi:10.1109/icsccn.2011.6024540

Cited by 13 publications

(3 citation statements)

References 10 publications

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“…Raw measurements must be pre-processed to remove clutter from GPR internal reflections and the ground interface. To this end, the literature provides various methods for ground clutter rejection, such as the mean subtraction method [52][53][54], the subspace projection method [55], etc. These methods remove the clutter but tend to partially filter the already weak signals coming from buried targets.…”

Section: Resultsmentioning

confidence: 99%

An Insight into the Warping Spatial Sampling Method in Subsurface Radar Imaging and Its Experimental Validation

Maisto¹,

Bhat

Solimene

2023

Remote Sensing

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In this paper, we are concerned with microwave subsurface imaging achieved by inverting the linearized scattering operator arising from the Born approximation. In particular, we consider the important question of reducing the required data to achieve imaging. This can help to reduce the radar system’s cost and complexity and mitigate the imaging algorithm’s computational burden and the needed storage resources. To cope with these issues, in the framework of a multi-monostatic/multi-frequency configuration, we introduce a new spatial sampling scheme, named the warping method, that allows for a significant reduction in spatial measurements compared to other literature approaches. The basic idea is to introduce some variable transformations that “warp” the measurement space so that the reconstruction point-spread function obtained by adjoint inversion is recast as a Fourier-like transformation, which provides insights into how to achieve the sampling. In our previous contributions, we focused on presenting and checking the theoretical background with simple numerical examples. In this contribution, we briefly review the key components of the warping method and present its experimental validation by considering a realistic subsurface scattering scenario for the case of a buried water pipe. Essentially, we show that the latter succeeds in reducing the number of data compared to other approaches in the literature, without significantly affecting the reconstruction results.

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Section: Resultsmentioning

confidence: 99%

An Insight into the Warping Spatial Sampling Method in Subsurface Radar Imaging and Its Experimental Validation

Maisto¹,

Bhat

Solimene

2023

Remote Sensing

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“…The methods proposed appear initially suited to targets with small lateral dimension, overlapping hyperbola signatures, varying clutter along the scanning direction (a nonstationary signal), overlapped target and clutter signals in time and frequency [11]. The semi-automatic methods tested and compared are the mean or median subtraction [12], the Principal Component Analysis (PCA) that has been modified and the Independent Component Analysis (ICA) [13], [14]. Basically, PCA relies on the second order statistics to perform the correlation analysis, whereas ICA requires higher order statistics (4th moment).…”

Section: Pre-processingmentioning

confidence: 99%

Development of Data Processing Tools for the Analysis of Radargrams in Utility Detection Using Ground Penetrating Radar

Sagnard¹

2017

JTIT

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Abstract-The extraction of quantitative information fromGround Penetrating Radar (GPR) data sets (radargrams) to detect and map underground utility pipelines is a challenging task. This study proposes several algorithms included in the main stages of a data processing chain associated with radargrams. It comprises pre-processing, hyperbola enhancing, hyperbola detection and localization, and parameter extraction. Additional parameters related to the GPR system such as the frequency band and the polarization bring data sets additional information that need to be exploited. Presently, the algorithms have been applied step by step on synthetic and experimental data. The results help to guide future developments in signal processing for quantitative parameter estimation.

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“…Advanced signal processing techniques was to be used to reduce the clutter. We can found that running median of an appropriate window size is the best in reducing background clutter [9]. The objective of this paper is to propose a novel method for removing clutters of GPR data of RC structure voids.…”

Section: Introductionmentioning

confidence: 99%

A Clutter Suppression Algorithm for GPR Data Based on PCA Combining with Gradient Magnitude

Wang

et al. 2014

AMM

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Ground penetrating radar (GPR) is a powerful tool for detecting defects behind reinforced concrete (RC) structures. However, the received data from GPR includes a large number of clutters which are easy overwhelming the signal of target. In order to successfully extract the target signature, these clutters effects need to be eliminated. In this article, a clutter suppression algorithm based on Principal Component Analysis (PCA) combining with gradient magnitude is presented. PCA clutter suppression algorithm is applied to the data and removes most of the echoes from ground surface and portion of other clutters with weak energy. Then gradient magnitude clutter suppression is used to remove majority of the residue clutters. It is demonstrated from simulation that the proposed algorithm is able to significantly suppress the clutters and is superior to the PCA clutter suppression, magnitude clutter suppression and means subtraction method.

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Clutter reduction techniques for GPR based buried landmine detection

Cited by 13 publications

References 10 publications

An Insight into the Warping Spatial Sampling Method in Subsurface Radar Imaging and Its Experimental Validation

An Insight into the Warping Spatial Sampling Method in Subsurface Radar Imaging and Its Experimental Validation

Development of Data Processing Tools for the Analysis of Radargrams in Utility Detection Using Ground Penetrating Radar

A Clutter Suppression Algorithm for GPR Data Based on PCA Combining with Gradient Magnitude

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