Near Real-Time InSAR Deformation Time Series Estimation With Modified Kalman Filter and Sequential Least Squares

The current and planned synthetic aperture radar (SAR) sensors mounted on satellite platforms will continue to operate over the coming years, providing unprecedented SAR data for monitoring wide-range surface deformations. near real-time processing of interferometry (InSAR) retrieval ground-deformation time series is urgently required in era big data. state-of-the-art Kalman filter (KF) sequential least squares (SLS) algorithms have been proposed update an InSAR-driven series. As a contribution this study, we customize conventional KF SLS InSAR processing. development accurate prediction model KF-based challenge owing large scale targets monitoring. We developed modified algorithm, abbreviated as npKF, that does not require any information, npKF. In context, avoid occupying storage space SLS-based processing, algorithm with truncated cofactor matrix, TSLS. Using both simulated actual data, evaluated performance these methods under three different aspects: accuracy, computation, performance. With method can estimate deformation real time. It be reliable effective tool producing products play part geologic hazard routine early warning system.