Speaker
Description
Commercial Microwave Links (CMLs) are increasingly used as an opportunistic sensing modality for near-ground two-dimensional rainfall mapping. In this work, we apply graph signal processing (GSP) to address missing attenuation measurements caused by link failures in operational CML networks. Instead of assuming that measurements originate from link midpoints, we identify an optimal representative point along each link using a graph smoothness formulation. The CML network is then modeled as a graph where rainfall values are defined on the nodes, enabling restoration of missing measurements by leveraging spatial correlations between neighboring links prior to rainfall field reconstruction.
In addition, we explore a dynamic framework in which the graph structure and representative points are periodically updated (e.g., hourly) to reflect changing network conditions and rainfall patterns. By exploiting the evolving topology and smoothness of the rainfall field, the method improves robustness to outages and measurement gaps. Experiments on real data from the OpenMRG dataset demonstrate substantial reductions in reconstruction error compared to conventional midpoint-based approaches, highlighting the potential of adaptive GSP models for resilient rainfall monitoring.