Gradient Descent GLRT Detector for Urban Tomographic SAR Applications

The accurate localization of multiple scatterers within a single-resolution cell constitutes a challenging task in the context of Tomographic Synthetic Aperture Radar (TomoSAR). In order to produce comprehensive three-dimensional maps, Generalized Likelihood Ratio Test (GLRT)-based detectors can be used. The most precise 3D point clouds can be achieved using the Support GLRT variant, at the expense of computationally intensive multidimensional grid search for finding the scatterers’ positions. In this paper, we propose Gradient Descent GD-GLRT, a gridless approach for effective yet precise scatterers’ detection in TomoSAR. The proposed detector uses the Fast Sup-GLRT for initializing the search followed by local descent optimization that operates in the continuous domain. The experimental results on both synthetic and real data show the effectiveness of the GD-GLRT in resolving the off-grid issue while decreasing computational costs.