A novel statistical model for differential synthetic aperture radar tomography

A deterministic differential tomographic synthetic aperture radar (D-TomoSAR) model, based on geometrical derivations and the assumption of accurate phase calibration, has been widely employed for spatially locating and temporally monitoring the point-like scatterers. In this work, we model the phase miscalibration effects of the extended scatters caused by partial correlation, i.e. the decorrelation effects from temporal and spatial changes as well as the residual atmospheric and deformation effects after preprocessing. Starting from the origin of four-dimensional SAR focusing, correlation of the target is analysed and a statistical D-TomoSAR model accounting for partial correlation effects is proposed. Based on the proposed model, a D-TomoSAR stack simulator is designed using Cholesky decomposition. Moreover, a linear minimum mean square error estimator based on the proposed model is developed for estimation of the height and deformation velocity of extended scatterers. Reconstruction results with both simulated data and real data acquired by TerraSAR-X/Tandem-X sensors are provided to demonstrate the effectiveness of the proposed model. (paper)