Information Theoretic Criterion Based Clutter Reduction for Ground Penetrating Radar

Riaz, Muhammad Mohsin; Ghafoor, Abdul

doi:10.2528/pierb12080802

Cited by 19 publications

(3 citation statements)

References 33 publications

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“…The drawback of these subspace decomposition methods is how to differentiate between the components spanning the clutter, target, and noise subspaces. Riaz et al assumed the first dominant component to span the clutter subspace and applied minimum description length (MDL) or Akaike information criterion (AIC) to separate the target and noise subspaces [20]. However, the rank of the background clutter subspace can be greater than one in practical applications, due to the inhomogeneity of the soil and roughness of the ground surface.…”

Section: Introductionmentioning

confidence: 99%

GPR Target Detection by Joint Sparse and Low-Rank Matrix Decomposition

Tivive

Bouzerdoum

Abeynayake

2019

IEEE Trans. Geosci. Remote Sensing

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Ground penetrating radar (GPR) uses electromagnetic waves to image, locate, and identify changes in electric and magnetic properties in the ground. The received signal comprises not only the target echoes but also strong reflections from the rough, uneven ground surface, which impair subsurface inspections and visualization of buried objects. In this paper, a background clutter mitigation and target detection method using low-rank and sparse priors is proposed for GPR data. The radar signal is decomposed into the sum of a low-rank component and a sparse component, plus noise. The low-rank component captures the ground surface reflections and background clutter, whereas the sparse component contains the target reflections. The effectiveness of the proposed method is evaluated on real radar signals collected from buried landmines and improvised explosive devices. The experimental results show that the proposed method successfully removes the background clutter and estimates the target signals.

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Section: Introductionmentioning

confidence: 99%

GPR Target Detection by Joint Sparse and Low-Rank Matrix Decomposition

Tivive

Bouzerdoum

Abeynayake

2019

IEEE Trans. Geosci. Remote Sensing

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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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“…Moreover, the algorithm is utilized in a clutter-free environment. This algorithm could also be used considering clutter reduction techniques as a pre-processing stage, as the ones used in ground [17] and/or sea [18] clutter-governed environments.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Scan Mixture Particle Filter for Joint Detection and Tracking of Multiple Targets

Liu¹,

Han²,

Yao³

et al. 2013

PIER B

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Abstract-In this paper, a novel algorithm named multi-scan mixture particle filter is proposed for joint detection and tracking for a varying number of targets. The posterior distribution of multiple target state in a single-target state space is a multi-mode distribution with each mode corresponding to either a target or clutter. A general global posterior distribution is adopted in this work, which consists of existing components and new components. The new components are generated at each time step to capture the new modes due to newly appeared targets or clutter. In order to distinguish targets from clutter, multiple scan information is incorporated. The history of each component's associate weights is stored in a multi-scan sliding window, which is used to judge whether the component is from a target or clutter. Moreover, a novel sampling method which combines the likelihood sampling and prior sampling is proposed to draw particles from the desired parts of the state space at each time step. From the simulation results, it could be seen that the proposed algorithm can effectively detect the appearance/disappearance of the targets as well as track the existing target.

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Information Theoretic Criterion Based Clutter Reduction for Ground Penetrating Radar

Cited by 19 publications

References 33 publications

GPR Target Detection by Joint Sparse and Low-Rank Matrix Decomposition

GPR Target Detection by Joint Sparse and Low-Rank Matrix Decomposition

Subspace Clutter Removal Techniques in GPR Images

A Multi-Scan Mixture Particle Filter for Joint Detection and Tracking of Multiple Targets

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