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<p>In this paper, we present a novel, high-resolution method, referred to as HRTR, for localizing underground objects. HRTR is based on a combination of the Time Reversal (TR) and Multiple Signal Classification (MUSIC) algorithms, and can be readily integrated with conventional ground-penetrating radar (GPR) systems without requiring any additional hardware. The proposed method offers significant advantages, particularly in achieving higher resolution, which enhances the ability to distinguish ground surface reflections and detect shallowly buried objects—challenges often encountered with conventional methods. The theoretical foundation of the proposed method is validated through numerical simulations using gprMax, as well as through experimental measurements from laboratory and field tests. The performance of HRTR is compared with conventional GPR methods, focusing on resolution improvements. Both simulations and experimental results demonstrate that HRTR produces clearer, sharper images with enhanced resolution. Unlike classical TR-MUSIC, the proposed HRTR method can be applied directly to conventional GPR measurements without the need for additional hardware or intensive computation. Moreover, it operates with just one antenna in monostatic mode or two in bistatic mode, avoiding the multiple-antenna requirement of TR-MUSIC. Furthermore, the proposed method enables the detection of deeply buried objects by using low-frequency signals for greater penetration while preserving spatial resolution. A graphical user interface and accompanying Python source code were developed and made publicly available on GitHub to facilitate the application of the proposed method to GPR A- and B-scans.</p>

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Enhanced GPR imaging using high-resolution TR-MUSIC for underground object localization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月28日

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  • Hamidreza Karami,
  • Carlos Romero,
  • Marcos Rubinstein,
  • Farhad Rachidi

摘要

In this paper, we present a novel, high-resolution method, referred to as HRTR, for localizing underground objects. HRTR is based on a combination of the Time Reversal (TR) and Multiple Signal Classification (MUSIC) algorithms, and can be readily integrated with conventional ground-penetrating radar (GPR) systems without requiring any additional hardware. The proposed method offers significant advantages, particularly in achieving higher resolution, which enhances the ability to distinguish ground surface reflections and detect shallowly buried objects—challenges often encountered with conventional methods. The theoretical foundation of the proposed method is validated through numerical simulations using gprMax, as well as through experimental measurements from laboratory and field tests. The performance of HRTR is compared with conventional GPR methods, focusing on resolution improvements. Both simulations and experimental results demonstrate that HRTR produces clearer, sharper images with enhanced resolution. Unlike classical TR-MUSIC, the proposed HRTR method can be applied directly to conventional GPR measurements without the need for additional hardware or intensive computation. Moreover, it operates with just one antenna in monostatic mode or two in bistatic mode, avoiding the multiple-antenna requirement of TR-MUSIC. Furthermore, the proposed method enables the detection of deeply buried objects by using low-frequency signals for greater penetration while preserving spatial resolution. A graphical user interface and accompanying Python source code were developed and made publicly available on GitHub to facilitate the application of the proposed method to GPR A- and B-scans.