Collaborative Reinforcement Learning Based Unmanned Aerial Vehicle (UAV)
  Trajectory Design for 3D UAV Tracking
Yujiao Zhu Mingzhe Chen Sihua Wang Ye Hu Yuchen Liu Changchuan Yin
In this paper, the problem of using one active unmanned aerial vehicle (UAV)
and four passive UAVs to localize a 3D target UAV in real time is investigated.
In the considered model, each passive UAV receives reflection signals from the
target UAV, which are initially transmitted by the active UAV. The received
reflection signals allow each passive UAV to estimate the signal transmission
distance which will be transmitted to a base station (BS) for the estimation of
the position of the target UAV. Due to the movement of the target UAV, each
active/passive UAV must optimize its trajectory to continuously localize the
target UAV. Meanwhile, since the accuracy of the distance estimation depends on
the signal-to-noise ratio of the transmission signals, the active UAV must
optimize its transmit power. This problem is formulated as an optimization
problem whose goal is to jointly optimize the transmit power of the active UAV
and trajectories of both active and passive UAVs so as to maximize the target
UAV positioning accuracy. To solve this problem, a Z function decomposition
based reinforcement learning (ZD-RL) method is proposed. Compared to value
function decomposition based RL (VD-RL), the proposed method can find the
probability distribution of the sum of future rewards to accurately estimate
the expected value of the sum of future rewards thus finding better transmit
power of the active UAV and trajectories for both active and passive UAVs and
improving target UAV positioning accuracy. Simulation results show that the
proposed ZD-RL method can reduce the positioning errors by up to 39.4% and
64.6%, compared to VD-RL and independent deep RL methods, respectively.