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LEI Wentai, WANG Yiming, ZHONG Jiwei, XU Qiguo, JIANG Yuyin, LI Cheng. A Review of Clutter Suppression Techniques in Ground Penetrating Radar: Mechanisms, Methods, and Challenges[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250524
Citation: LEI Wentai, WANG Yiming, ZHONG Jiwei, XU Qiguo, JIANG Yuyin, LI Cheng. A Review of Clutter Suppression Techniques in Ground Penetrating Radar: Mechanisms, Methods, and Challenges[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250524

A Review of Clutter Suppression Techniques in Ground Penetrating Radar: Mechanisms, Methods, and Challenges

doi: 10.11999/JEIT250524 cstr: 32379.14.JEIT250524
Funds:  The Open Research Fund Project of State Key Laboratory of Bridge Intelligent and Green Construction(BIGCSKL24-09-GF) , The Science and Technology projects of Xizang Autonomous Region, China(XZ202501ZY0005) ,The Major Special Project of Science and Technology Research and Development of China Railway Group limited (2023-Special Project-02)
  • Received Date: 2025-06-09
  • Rev Recd Date: 2025-09-29
  • Available Online: 2025-10-11
  •   Significance   Ground Penetrating Radar (GPR) is a widely adopted non-destructive subsurface detection technology, extensively applied in urban subsurface exploration, transportation infrastructure monitoring, geophysical surveys, and military operations. It is employed to detect underground pipelines, structural foundations, road voids, and concealed defects in roadbeds, railway tracks, and tunnels, as well as shallow geological formations and military targets such as unexploded ordnance. However, the presence of clutter—unwanted signals including direct coupling waves, ground reflections, and non-target echoes—severely degrades GPR data quality and complicates target detection, localization, imaging, and parameter estimation. Effective clutter suppression is therefore essential to enhance the accuracy and reliability of GPR data interpretation, making it a central research focus in improving GPR performance across diverse application domains.  Progress   Significant progress has been achieved in GPR clutter suppression, largely through two main approaches: signal model-based and neural network-based methods. Signal model-based techniques, such as time–frequency analysis, subspace decomposition, and dictionary learning, rely on physical modeling to distinguish clutter from target signals. These methods provide clear interpretability but are limited in addressing complex and non-linear clutter patterns. Neural network-based methods, employing architectures such as Convolutional Neural Networks, U-Net, and Generative Adversarial Networks, are more effective in capturing non-linear features through data-driven learning. Recent advances, including multi-scale convolutional autoencoders, attention mechanisms, and hybrid models, have further enhanced clutter suppression under challenging conditions. Quantitative metrics such as Mean Squared Error, Peak Signal-to-Noise Ratio, and Structural Similarity Index are commonly used for performance evaluation, often complemented by qualitative visual assessment.  Conclusion  The complexity and diversity of GPR clutter, originating from direct coupling, ground reflections, equipment imperfections, non-uniform media, and non-target scatterers, demand robust suppression strategies. Signal model-based methods provide strong theoretical foundations but are constrained by simplified assumptions, whereas neural network-based approaches offer greater adaptability at the expense of large data requirements and high computational cost. Hybrid approaches that integrate the strengths of both paradigms show considerable potential in addressing complex clutter scenarios. The selection of evaluation metrics plays a pivotal role in algorithm design, with quantitative measures offering objective assessment and qualitative analyses providing intuitive validation. Despite recent advances, significant challenges remain in suppressing non-linear clutter, enabling real-time processing, and reducing reliance on labeled data.  Prospect   Future research in GPR clutter suppression is likely to emphasize integrating the strengths of signal model-based and neural network-based methods to develop robust and adaptive solutions. Real-time processing and online learning will be prioritized to meet the requirements of dynamic applications. Self-supervised and unsupervised learning approaches are expected to reduce dependence on costly labeled datasets and improve model adaptability. Cross-task learning and multi-modal fusion, combining data from multiple sensors or frequencies, are expected to enhance robustness and precision. Furthermore, deeper integration of physical principles, including electromagnetic wave propagation and media properties, into algorithm design is expected to improve suppression accuracy and computational efficiency, advancing the development of more intelligent and effective GPR systems.
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