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GBSAR Geocoding Based on Bayes Theorem: Applications to Slope and Structural Deformation Monitoring
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Author(s): Hao Zhang (China Academy of Safety Science and Technology, China), Xiaolin Yang (China Academy of Safety Science and Technology, China), Shanshan Hou (Cathay Safety Technology Co., Ltd., China), Zhenan Yin (Nanjing Institute of Technology, China), Guiwen Ren (Cathay Safety Technology Co., Ltd., China)and Xiangtian Zheng (Nanjing Institute of Technology, China)
Copyright: 2025
Volume: 18
Issue: 1
Pages: 24
Source title:
International Journal of Information Technologies and Systems Approach (IJITSA)
Editor(s)-in-Chief: Sangbing (Jason) Tsai (International Engineering and Technology Institute (IETI), Hong Kong)and Wei Liu (Qingdao University, China)
DOI: 10.4018/IJITSA.380648
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Abstract
Ground-based synthetic aperture radar (GB-SAR) is pivotal for high-precision deformation monitoring in infrastructure (e.g., dams, bridges) and geological hazards (e.g., landslides). However, near-field 2D-to-3D geocoding challenges persist due to geometric distortions and multi-candidate ambiguities. Traditional orthogonal projection methods, reliant on far-field assumptions, fail in complex scenarios. This study proposes a Bayesian maximum a posteriori framework integrated with Doppler projection and multi-source fusion. By mapping 3D models to synthetic aperture radar coordinates via Doppler projection and incorporating physical priors (e.g., radar incidence angles), the method constructs a joint probabilistic model to screen infeasible scatterers. Experiments achieved sub-millimeter accuracy (root mean square [RMS] <0.8 mm) in deformation mapping and identified structural vibrations (10–25 hertz) with <2% deviation from accelerometer data. The framework mitigates layover and foreshortening artifacts, outperforming conventional methods, and offers robust solutions for infrastructure safety and hazard early warning.
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