Approach for Localizing Scatterers in Urban Drone-to-Drone Propagation Environments

Abstract

In the near future an increasing number of unmanned aerial vehicles (UAVs) are expected to be integrated into urban airspace. Direct Drone-to-Drone (D2D) communication is a promising approach for exchanging information in order to prevent mid-air collisions especially in dense urban areas. For a reliable and efficient communication the fundamental propagation mechanisms must be understood and specific channel models be developed. In previous work we identified the origin of some multipath components (MPCs) in first wideband channel measurements by applying a geometrical signal path simulation considering the outline of buildings and recorded flight tracks. But the performance of this approach depends on the degree of simulated details and can easily get computationally expensive in order to identify the origin of all measured MPCs. Therefore, in this work we enhance the identification by jointly estimating the delay and doppler frequency probability density functions (PDFs) for each scatterer and localize their origins by transforming the estimation into the 3D Cartesian domain and intersecting the results with known objects. We show the feasibility of this approach by investigating the parameter dependency on the results under simulated conditions and then compare the results when being applied on real measurement data. For estimating key parameters of the MPCs, we employ the Kalman enhanced super resolution tracking algorithm (KEST) algorithm.