Accuracy Evaluation Method for GB-SAR-Based Beidou Surface Deformation Measurement Radar

Before geological disasters occur, the ground surface typically experiences minor displacements that can provide early warning signals. Consequently, high-precision real-time 3D deformation monitoring of high-risk areas is critically required for effective geological-disaster early warning. The Beidou surface deformation measurement radar is a new type of surface deformation monitoring technology that can be applied to multiple scenarios such as slope monitoring, mining area settlement, and bridge deformation. It offers the advantages of wide coverage and low cost. It is capable of achieving 3D deformation monitoring of an entire scene using a single receiver, which can be stationary or mounted on a vehicle or aircraft. By leveraging in-orbit satellites as transmitters, the system significantly reduces long-term monitoring costs compared to ground-based synthetic aperture radar (GB-SAR) and spaceborne interferometric synthetic aperture radar (InSAR) systems. However, the low transmission power from navigation satellites to the Earth results in diminished signal-to-noise ratio (SNR) of scene echoes, leading to phase instability and reduced measurement accuracy. In this study, high-precision one-dimensional deformation data from GB-SAR were adopted as ground truth to preliminarily validate the deformation monitoring capability of the Beidou radar system. The operational workflow of the Beidou radar is systematically described, with specific adaptations tailored to its technical characteristics. A cross-system accuracy evaluation methodology was implemented, leveraging GB-SAR deformation data as reference benchmarks. Field experiments conducted in the Laolingou area of Chongqing over a seven-day observation period yielded quantified measurement accuracy metrics for the Beidou radar system. Experimental results demonstrate the competence of the system in full-scene 3D deformation monitoring, highlighting its potential for broad applications in disaster prediction scenarios across diverse environments.