Optimized filter design for irregular acquired data stack in persistent scatterers synthetic aperture radar interferometry

After more than a decade of continuous improvements, Persistent SAR Interferometry (PSI) has become an accepted tool for long-term monitoring of geophysical phenomena such as volcanoes, earthquakes, and city deformation. To extract surface deformation from PSI observations, different phase component are separated based on their physical or stochastic properties. Atmospheric signals, one of the main error contributions, are usually mitigated using a low-pass filter in time. This approach provides unsatisfactory results if the temporal sampling of the surface deformation signal is close to Nyquist, and if there are sampling gaps in the time series. Hence, in many cases, a more sophisticated way to mitigate atmospheric signals is required. This paper proposes an adaptive filter design that is utilizing knowledge of the stochastic properties of the atmosphere at SAR acquisition times. The concept of the filter is presented and its performance is tested on simulated signals. In these studies, the adaptive filter design performed favorably and we are expecting this approach to be of major benefit for PSI processing. In current work, the performance of the adaptive filter is tested on real data. For this study, the pre-known statistics of atmospheric delay is provided by Numerical Weather Forecast Models.