A communication approach for aerial surveillance of long linear infrastructures in non-urban terrain

3-D modeling of aerial wireless coverage is an important requirement for many an applications of UAVs. Typically such model is used in communication-aware path planning of the vehicle. When the surveillance area is vast, such as that of long linear utility infrastructures e.g. power grids, oil and gas pipelines, railway corridors etc., signal coverage is quite sparse due to lack of transmitters in rural/forested terrain. On the other hand, maximal connectivity so as to robustly transmit huge volumes of sensed data in downlink is highly required. However, coverage models for vast areas, especially in remote rural surroundings having complex terrain, are not publicly known, thus inhibiting mission planning for an important class of UAV applications. In this paper, we have tried to address this requirement by establishing such a coverage model. The model is based on a 3-D mesh of sampling locations, over which a sparse set of signal strength measurements are available using pilot flights. From the sparse set, we use heuristics of distance-weighted averaging to closely approximate and predict signal strength measurements at other grid points. We propose a novel metric of representative distance, which leads to approximation error become as less as 5%. The solution holds the promise of being scalable to big grid sizes, as is demonstrated by the simulation results. We believe that this model will form an important stepping stone towards evolution of more robust models, which in turn are needed to enable many an important civilian applications of UAV as a sensing platform.