Sparse reconstruction automaton for synthetic aperture radar tomography

Abstract

In this paper, we report our findings on automating the sparse reconstruction process in tomography with synthetic aperture radar. Two hyperparameter-free approaches are introduced into the framework of SL1MMER (Scale-down by L1 norm Minimization, Model selection, and Estimation Reconstruction). By means of numerical simulations, we evaluate their performance regarding mean and standard deviation of elevation estimates, as well as detection rate. Preliminary results with real data are also provided.