Journal of Jilin University(Engineering and Technology Edition) ›› 2026, Vol. 56 ›› Issue (8): 2150-2161.doi: 10.13229/j.cnki.jdxbgxb.20250012

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Multi-layer rebar detection method based on spread spectrum and polarization gain compensation

Zi-han XIA1(),Song-tao XUE1,2,3,Li-yu XIE1,3,4(),Jiang LIU5,Lei-jun ZHOU5   

  1. 1.College of Civil Engineering,Tongji University,Shanghai 200092,China
    2.Department of Architecture,Tohoku Institute of Technology,Sendai 982-8577,Japan
    3.State Key Laboratory of Disaster Reduction in Civil Engineering,Tongji University,Shanghai 200092,China
    4.School of Civil Engineering and Architecture,Xinjiang University,Urumqi 830049,China
    5.Shanghai Choyoin Construction Group Co. ,Ltd. ,Shanghai 201318,China
  • Received:2025-01-06 Online:2026-08-01 Published:2026-09-02
  • Contact: Li-yu XIE E-mail:xzhooo@tongji.edu.cn;liyuxie@tongji.edu.cn

Abstract:

To address the issues of insufficient resolution, signal overlap, and reduced signal-to-noise ratio that existing Ground Penetrating Radar(GPR) technology still faces in multi-layer dense rebar scenarios, a multi-layer rebar detection method based on spread spectrum and polarization gain compensation is proposed. The proposed approach optimizes the spread spectrum technique to effectively mitigate the issues of frequency discontinuities and information loss during data fusion. Additionally, it leverages the electromagnetic reflective properties of metal and the amplification mechanism of polarization reversal to enhance the intensity of rebar reflection signals, thereby significantly improving detection performance. The numerical simulation results obtained using CST demonstrate that the polarization deflection characteristics of metal reflection signals can significantly enhance the SNR of rebar reflections in echo signals. The polarization matching transmission efficiency can be improved by up to approximately 20 dB. Additionally, simulations and post-processing analyses were performed on GPR data before and after spectrum expansion using gprMax. The results demonstrate that the GPR frequency expansion technique proposed in this study effectively enhances distance resolution, resolves the issue of overlapping reflection signals from closely spaced double-layered rebars, and successfully achieves their independent localization, with a maximum error of only 5.25%. In addition, the robustness analysis under multiple SNR conditions is also carried out in this paper to further validate the applicability and reliability of the method in complex practical environments.

Key words: non-destructive detection of rebar, ground penetrating radar(GPR), spectrum expansion, data fusion, metal reflection signals, polarization reversal, gain compensation, radar range resolution

CLC Number: 

  • TU375

Fig.1

Single-based SFCW radar system"

Fig.2

GPR signal propagation path diagram"

Fig. 3

Dual-based SFCW radar system"

Fig.4

Measurement of dual-based SFCW radar"

Fig.5

Schematic diagram of metal reflections for circular polarization"

Fig.6

A dual-polarized horn antenna in CST"

Fig.7

Schematic diagram of incident electromagnetic wave polarization matching"

Fig.8

Evaluation of polarization matching efficiency"

Fig.9

Schematic diagram of gprMax model"

Fig.10

Comparison of GPR B?scan data"

Fig.11

Comparison of gain-processed data for filtered direct wave"

Fig.12

Comparison of data for time scale enlargement"

Fig.13

GPR A-Scan before and after spectrum expansion"

Fig.14

Inversion results for two layers of rebar height"

Table 1

Inversion results for two layers of rebar height"

钢筋距离底

部高度/cm

真实结果频谱未扩展频谱扩展2倍
原始数据后处理原始数据后处理
第一层钢筋7.007.26无法计测7.216.89
第一层误差/%-3.71-31.57
第二层钢筋4.00无法计测无法计测无法计测4.21
第二层误差/%-3.71--5.25

Fig.15

Comparison of high, medium and low SNR conditions"

Table 2

Inversion results for three SNR conditions"

钢筋距离

底部高度

真实

结果

SNR=

30 dB

SNR=

20 dB

SNR=

10 dB

第一层钢筋7.007.187.206.62
第一层误差/%-2.572.865.43
第二层钢筋4.004.154.16

无法

计测

第二层误差/%-3.754-
[1] Xu X, Li J, Qiao X, et al. Fusion of multiple time‐domain GPR datasets of different center frequencies[J]. Near Surf Geophys, 2019,17: 141-150.
[2] Zhao W, Yuan L, Forte E, et al. Multi-frequency GPR data fusion with genetic algorithms for archaeological prospection[J]. Remote Sens-Basel, 2021, 13: No.2804.
[3] Zhao W, Lu G. A novel multifrequency GPR data fusion algorithm based on time-varying weighting strategy[J]. IEEE Geosci Remote S, 2021,19: 1-4.
[4] Guan Z, Liu W. Multi-Frequency GPR data fusion through a joint sliding window and wavelet transform-weighting method for top-coal structure detection[J]. Applied Sciences, 2024,14: No.2721.
[5] Bi W, Zhao Y, Shen R, et al. Multi-frequency GPR data fusion and its application in NDT[J]. Ndt & E Int, 2020,115: No.102289.
[6] de Coster A, Lambot S. Fusion of multifrequency GPR data freed from antenna effects[J]. IEEE J-Stars, 2018,11: 664-674.
[7] Annan A P. Electromagnetic principles of ground penetrating radar[J]. Ground Penetrating Radar: Theory and Applications,2009,1: 3-41.
[8] Leucci G. Ground penetrating radar: the electromagnetic signal attenuation and maximum penetration depth[J]. Scholarly Research Exchange, 2008,2008: No. 926091.
[9] Liu Y, Li K, Jia Y, et al. Wideband RCS reduction of a slot array antenna using polarization conversion metasurfaces[J]. IEEE T Antenn Propag, 2015,64:326-331.
[10] Lambot S, Slob E C, van den Bosch I, et al. Modeling of ground-penetrating radar for accurate characterization of subsurface electric properties[J]. IEEE T Geosci Remote, 2004,42: 2555-2568.
[11] 任仕召, 魏光辉, 潘晓东, 等. 典型雷达装备带内连续波辐射效应试验研究[J]. 强激光与粒子束, 2020,32(5):61-66.
Ren Shi-zhao, Wei Guang-hui, Pan Xiao-dong, et al. Experimental study on radiation effect of in-band continuous wave on typical radar equipment[J]. High Power Laser and Particle Beams, 2020, 32(5): 61-66.
[12] Hiebel M. Fundamentals of Vector Network Analysis[M]. Berlin: Rohde & Schwarz, 2007.
[13] Zhang J, Ye S, Lin Y, et al. A modified model for quasi-monostatic ground penetrating radar[J]. IEEE Geosci Remote S, 2019,17:406-410.
[14] Qureshi M A, Schmidt C H, Eibert T F. Efficient near-field far-field transformation for nonredundant sampling representation on arbitrary surfaces in near-field antenna measurements[J]. IEEE T Antenn Propag, 2012,61: 2025-2033.
[15] Mourmeaux N, Tran A P, Lambot S. Soil permittivity and conductivity characterization by full-wave inversion of near-field GPR data[C]∥Proceedings of the 15th International Conference on Ground Penetrating Radar, Brussels, Belgium, 2014: 497-502.
[16] Noon D A. Stepped-frequency radar design and signal processing enhances ground penetrating radar performance[D]. St. Lucia: Department of Electrical and Computer Engineering,The University of Queensland, 1996.
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