Approach for Localizing Scatterers in Urban Drone-to-Drone Propagation Environments
2021
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.
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