Multichannel InSAR phase unwrapping using nonlinear Kalman smoother

Phase unwrapping (PU) is one of the key processing steps in reconstructing the digital elevation model (DEM) of a scene from interferometric synthetic aperture radar (InSAR) data. The PU problem entails the estimation of an absolute phase from the observation of its noisy principal (wrapped) values. Recently PU approaches based on Kalman filtering have proved their efficacy in tackling the PU problem even when strong height profile discontinuities and noisy data are involved. This paper presents a novel multichannel InSAR phase unwrapping algorithm based on the extended Kalman filter. The proposed technique exploits the capability of the Kalman algorithm to simultaneously perform noise filtering, phase unwrapping and multi-sensor data fusion. The performance of the proposed algorithm has been tested on simulated interferometric images proving the effectiveness of the proposed method.