Scalable Navigation for Tracking a Cooperative Unpredictably Moving Target in an Urban Environment

Target tracking in urban environments using a fixed-wing unmanned aerial vehicle (UAV) is challenging due to the line of sight obstructions which are caused by buildings. Even with a cooperative target that sends out its location to the UAV, the vehicle may inevitably lose the line of sight due to its limited turning rate. Parts of the UAV operating space in which the UAV loses the line of sight are denoted in this paper as shadows. The shadows have complex shapes and move as the target changes its relative position to buildings. Avoiding the shadows increases the observation time while tracking the cooperative target. We present here a scalable feedback control approach for target tracking with shadow avoidance, which is based on a stochastic optimal feedback control solution. Our results are illustrated by numerical simulations.