Towards SAR Tomographic Inversion via Sparse Bayesian Learning

Existing SAR tomography (TomoSAR) algorithms are mostly based on an inversion of the SAR imaging model, which are often computationally expensive. Previous study showed perspective of using data-driven methods like KPCA to decompose the signal and reduce the computational complexity. This paper gives a preliminary demonstration of a new data-driven method based on sparse Bayesian learning. Experiments on simulated data show that the proposed method significantly outperforms KPCA methods in estimating the steering vectors of the scatterers. This gives a perspective of data-drive approach or combining it with model-driven approach for high precision tomographic inversion of large areas.Comment: accepted in preliminary version for EUSAR2020 conferenc

Convergence Analysis of Ensemble Kalman Inversion: The Linear, Noisy Case

We present an analysis of ensemble Kalman inversion, based on the continuous time limit of the algorithm. The analysis of the dynamical behaviour of the ensemble allows us to establish well-posedness and convergence results for a fixed ensemble size. We will build on the results presented in [26] and generalise them to the case of noisy observational data, in particular the influence of the noise on the convergence will be investigated, both theoretically and numerically. We focus on linear inverse problems where a very complete theoretical analysis is possible

Direct numerical simulation of fog: The sensitivity of a dissipation phase to environmental conditions

The sensitivity of fog dissipation to the environmental changes in radiation, liquid-water lapse rate, free tropospheric temperature and relative humidity was studied through numerical experiments designed based on the 2007-Paris Fog observations. In particular, we examine how much of the stratocumulus-thinning mechanism can be extended to the near-surface clouds or fog. When the free troposphere is warmed relative to the reference case, fog-top descends and become denser. Reducing the longwave radiative cooling via a more emissive free troposphere favors thickening the physical depth of fog, unlike cloud-thinning in a stratocumulus cloud. Drying the free troposphere allows fog thinning and promotes fog dissipation while sustaining the entrainment rate. The numerical simulation results suggest that the contribution of entrainment drying is more effective than the contribution of entrainment warming yielding the reduction in liquid water path tendency and promoting the onset of fog depletion relative to the reference case studied here. These sensitivity experiments indicate that the fog lifting mechanism can enhance the effect of the inward mixing at the fog top. However, to promote fog dissipation, an inward mixing mechanism only cannot facilitate removing humidity in the fog layer unless a sufficient entrainment rate is simultaneously sustained