A Coherent Difference Imaging Method for Antenna Decoupling in Ground-Penetrating Radar
Ground-penetrating radar (GPR) is a key non-destructive technique for subsurface reconstruction, widely valued for its ability to image buried structures without disruption. Among its various implementations, vehicle-mounted GPR has emerged as particularly suitable for highway tunnel assessment due to its rapid non-contact operation. However, current systems are often constrained by closely spaced antennas that generate strong direct coupling and consequently limit detection depth. To mitigate this issue, this paper proposes an antenna decoupling method based on coherent difference imaging. A differential decoupling model is first established to characterize the relationship between conventional transceiver signals and the derived differential signals, explicitly accounting for parameters such as antenna height and target depth. Furthermore, a coherent difference imaging algorithm is developed, employing a sliding-window coherence process to resolve dual-peak artifacts and restore focused target images. Simulations validate consistent performance across varying antenna heights, while experiments demonstrate over 37.2 dB isolation in the 1–3 GHz band and markedly improved imaging focus compared to conventional configurations, thereby enhancing buried target detection and supporting reliable data interpretation.
Authors
- ShengBo Ye
- Menghao Zhu
- Yicai Ji
- Xiaojun Liu
- Guangyou Fang
- Yang Xu
- Yudong Fang
- Zihao Wang
Institutions
- Chinese Academy of Sciences (CN)
- National Earthquake Response Support Service (CN)
- Aerospace Information Research Institute (CN)
- University of Chinese Academy of Sciences (CN)
- Ministry of Industry and Information Technology (CN)
Publication Details
- Journal
- Electronics
- Published
- 2026-02-21
- DOI
- https://doi.org/10.3390/electronics15040893
- Primary Topic
- Geophysical Methods and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Chinese Academy of Sciences