A real-time processing method for GB-SAR monitoring data by using the dynamic Kalman filter based on the PS network

Ground-based synthetic aperture radar (GB-SAR) has been widely used in the safety monitoring of slopes, dams, and buildings due to its high precision, large coverage area, and fast image acquisition. The real-time processing of high frequency and continuous deformation monitoring data is particularly important for early warning of landslides and high-risk buildings. Yet very limited studies have been conducted on the real-time processing method of GB-SAR monitoring data. In this study, a novel real-time processing method of GB-SAR monitoring data is proposed by using the Kalman filter based on the permanent scatterer (PS) network. The proposed method starts from the radiation characteristic and the phase composition of the GB-SAR monitoring data and instantaneously processes the acquired radar image by using the dynamic Kalman filter based on PSs and PS network. Then, a real-time processing Kalman mathematical model can be established, the model parameters are initialized, and the recursive Kalman filter to solve the timely deformation monitoring. By continuously updating the image data, the real-time and high-efficient calculation of PS deformation parameters can be achieved, which realizes the high accuracy and continuous deformation monitoring. The proposed novel method fills the gap in the real-time processing techniques of GB-SAR monitoring data and solves key problems of PS network updating, phase unwrapping, atmospheric phase correction, deformation calculation, etc.