GB-SAR Engineering Interference Suppression Method Integrating Amplitude-Phase Feature Analysis and Robust Regression

Complex construction environments can interfere with Ground-Based Synthetic Aperture Radar (GB-SAR) deformation monitoring, potentially leading to missed or false alarms. Current research on addressing engineering interference in GB-SAR deformation monitoring remains preliminary, with existing methods exhibiting limitations in interference pattern coverage, feature extraction, and algorithm robustness. To address these challenges, this paper proposes a joint processing method integrating amplitude-phase feature analysis and robust regression. The method first constructs a multi-threshold detection framework in the amplitude-phase dual-domain and combines the wavelet-Monte Carlo confidence screening strategy to achieve precise identification of engineering interference. Subsequently, a two-stage suppression model based on robust estimation theory is developed to effectively suppress interference. Experimental results from the Dabao Mountain and Pearl River mining areas demonstrate that the proposed method achieves a reduction of over 80% in the maximum standard deviation of the time-series cumulative phase after suppression, significantly improving phase stability. These findings validate the applicability and effectiveness of the proposed method in complex construction environments, providing strong support for the practical application of GB-SAR in engineering monitoring.