Accurate reconstruction of rain field maps from Commercial Microwave Networks using sparse field modeling

Recently, it has been demonstrated that Commercial Microwave Networks (CMN) can be considered as an opportunistic sensor networks for rainfall monitoring, and in particular, for rain fields reconstruction. While different rainfall mapping techniques have been proposed, their absolute performance has never been evaluated. This paper presents a novel algorithm, which generates an accurate reconstruction of rain field maps, given measurements from commercial microwave links (ML). The accuracy is achieved by using the sparse properties of the rain field, which enables an optimal and unique recovery of the rain rates along the ML, under certain regularity conditions. We demonstrate that the performance of the proposed algorithm is close to the actual measurements of the rain intensity in a given location, and that it outperforms the reconstruction done by the Radar, almost uniformly. The proposed approach is not restricted to the specific application of rainfall mapping. It can also be used for reconstructing images, especially sparse images, which are sampled by projections on arbitrary lines.