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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 thetarget UAV, which are initially transmitted by the active UAV. The receivedreflection signals allow each passive UAV to estimate the signal transmissiondistance which will be transmitted to a base station (BS) for the estimation ofthe position of the target UAV. Due to the movement of the target UAV, eachactive/passive UAV must optimize its trajectory to continuously localize thetarget UAV. Meanwhile, since the accuracy of the distance estimation depends onthe signal-to-noise ratio of the transmission signals, the active UAV mustoptimize its transmit power. This problem is formulated as an optimizationproblem whose goal is to jointly optimize the transmit power of the active UAVand trajectories of both active and passive UAVs so as to maximize the targetUAV positioning accuracy. To solve this problem, a Z function decompositionbased reinforcement learning (ZD-RL) method is proposed. Compared to valuefunction decomposition based RL (VD-RL), the proposed method can find theprobability distribution of the sum of future rewards to accurately estimatethe expected value of the sum of future rewards thus finding better transmitpower of the active UAV and trajectories for both active and passive UAVs andimproving target UAV positioning accuracy. Simulation results show that theproposed ZD-RL method can reduce the positioning errors by up to 39.4% and64.6%, compared to VD-RL and independent deep RL methods, respectively.

Collaborative Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Trajectory Design for 3D UAV Tracking