A Clutter Suppression Method Based on Improved Principal Component Selection Rule for Ground Penetrating Radar

Zhu, Jichao; Xue, Wei; Xia, Rong; Yu, and Yunyun

doi:10.2528/pierm16102903

Cited by 11 publications

(11 citation statements)

References 20 publications

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“…Principal component analysis (PCA) and zerophase component analysis (ZCA) have been implemented as clutter and background removal methods in Refs. [28][29][30]. Nevertheless, their high artefact rates represent major drawbacks in average similarity function (ASF) imaging method.…”

Section: Introductionmentioning

confidence: 99%

Robust Wiener filter‐based time gating method for detection of shallowly buried objects

Gharamohammadi

Behnia

Shokouhmand

et al. 2021

IET Signal Processing

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A robust method for ultra-wideband (UWB) imaging of buried shallow objects based on time gating, Wiener filtering, as well as constant false alarm rate (CFAR) is proposed. Moreover, it is demonstrated that Wiener filtering can be used as a clutter removal tool in UWB signal applications. Basically, the problem with time gating method is that the length of the timing window for unknown targets cannot be determined accurately in advance. In fact, it is a blind methodology and some targets can be missed due to a lack of preknowledge about their depth. Imprecise window length selection leads to missing some parts of the target signals along with the clutter, which in turn increases the missed detection rate. Herein, an algorithm to tackle this problem is proposed by using a Wiener filter along with CFAR as a primary detector of the target positions employing average similarity function imaging. The time gating method is then built on top of the information achieved for the window length selection from the primary detection. The combination of the two steps provides better detection of shallowly buried objects with less missed detection of targets, besides having fewer artefacts in comparison to other methods. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 99%

Robust Wiener filter‐based time gating method for detection of shallowly buried objects

Gharamohammadi

Behnia

Shokouhmand

et al. 2021

IET Signal Processing

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“…For evaluation step, results of the proposed method, both A-scan and B-scan, are employed for analysis. The comparison and numerical analysis to the existing SVD based methods [9,11] are conducted in the perspective of the signal-to-clutter-plus-noise-ratio (SCNR) and root-mean-squareerror (RMSE) in both flat and rough surfaces. SCNR is needed to evaluate the quality of the enhanced target signal while RMSE is important for investigating the similarity of the enhanced signal with the reference signal.…”

Section: Methodsmentioning

confidence: 99%

“…Different approaches have been introduced and proposed for both clutter and noise reduction such as background subtraction based method [2], clutter model approach [3,4], wavelet transform approach [5,6], and subspace projection based method [7][8][9][10][11][12]. Singular value decomposition (SVD) is a statistic based method of subspace projection which is considered by many researchers for building a robust GPR target signal enhancement as follows.…”

Section: Introductionmentioning

confidence: 99%

“…In 2013, the method based on singular value decomposition and Fuzzy C-means (FCM) was proposed for GPR image enhancement [9]. Later, in 2017, the method proposed in [11] was able to select the principal component automatically according to its singular spectrum, its first difference and second difference singular values. According to both experimental and simulated results, this method can enhance signal against the presence on ground clutter and white Gaussian noise in flat surface condition.…”

Section: Introductionmentioning

confidence: 99%

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GPR Target Signal Enhancement Using Least Square Fitting Background and Multiple Clustering of Singular Values

Rohman¹,

Nishimoto²

2019

PIER Letters

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Ground penetrating radar is an effective nondestructive method for exploring subsurface object information by exploiting the differences in electromagnetic characteristics. However, this task is negatively affected by the existence of ground clutter and noise especially if the object is weak or/and shallowly buried. Therefore, this paper proposes a novel method for suppressing the clutter and background noise simultaneously in both flat and rough surfaces. First, the ground clutter is removed mainly by applying a simplified least square fitting background method, which remains the residual random noise signal. The remaining signal is then decomposed by singular value decomposition, which assumes that the decomposed signal contains four main components including strong target, weak target, very weak target, and accumulated noise signals. The powered singular values and their differences are clustered by K-means to extract the target signal components. The simulation results indicate that the proposed method is able to enhance the target signal with satisfactory results under both flat and rough surfaces as well as in a high-level background noise. Besides, this method also shows its superiority to the latest existing proposed methods.

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“…There are different approaches that can remove the clutter in the GPR images. Among those, the subspace-based methods such as principal component analysis (PCA) [3][4][5], independent component analysis (ICA) [6,7], singular value decomposition (SVD) [8][9][10], and the possible combination between them. These techniques are based on eigen values to perform matrix decomposition on the GPR image with different constraints.…”

Section: Introductionmentioning

confidence: 99%

Subspace Clutter Removal Techniques in GPR Images

Shehab¹,

Obaidi²,

Hoş³

et al. 2019

PIER M

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In many modern GPR systems, it is desired to detect the presence of targets in the interference which includes clutter and noise. Detection of water leaks using GPR has been aimed in this work. Pipe and soil are known as the clutter of data in this scenario. Various signal processing techniques like multivariate subspace-based algorithms are proposed to effectively suppress the clutter and increase the signal to interference ratio. Combining Independent Component Analysis (ICA) and Principal Component Analysis (PCA) as a unique algorithm has demonstrated the ability to eliminate the GPR clutter and extract the target signal.

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A Clutter Suppression Method Based on Improved Principal Component Selection Rule for Ground Penetrating Radar

Cited by 11 publications

References 20 publications

Robust Wiener filter‐based time gating method for detection of shallowly buried objects

Robust Wiener filter‐based time gating method for detection of shallowly buried objects

GPR Target Signal Enhancement Using Least Square Fitting Background and Multiple Clustering of Singular Values

Subspace Clutter Removal Techniques in GPR Images

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