Adaptive Fading Extended Kalman Filter Applied in Phase Unwrapping of InSAR

Abstract

Phase Unwrapping (PU) is a key step and an important problem in the extraction of Digital Elevation Model and the measurement of small surface deformation of Interferometric Synthetic Aperture Radar (InSAR) and It has been a research hotspot. The observation equation that used in the Extended Kalman Filter (EKF) model is nonlinear for Phase Unwrapping, usually through linear processing, and required the system model and noise statistics are known. But in fact the mathematical model or statistical noise of SAR interferogram is completely or partially unknown, the results have been inevitably lead to the declining of valuation accuracy and even the phenomenon of filter divergence, making it impossible to retrieve surface deformation information, if it is directly applied to phase unwrapping. In order to solve this problem, a Phase Unwrapping (PU) algorithm based on Adaptive Fading Extended Kalman Filter (AFEKF) for InSAR is presented. The fading factor is calculated by innovation covariance and adaptively adjusted with the error covariance so as to suppress the memory length of the filter, compensating the effect of incomplete information on phase unwrapping. The simulation results are proved the validity of proposed method, it can not only be dealt with phase unwrapping and filtering at the same time, but also can be compensated for model error, improving the accuracy of phase unwrapping.