Abstract
In this paper, the framework of fluid antenna system (FAS)-assisted three dimensional (3D) passive unmanned aerial vehicle (UAV) positioning is developed. In the proposed framework, a set of controlled UAVs including an active UAV and four FAS-assisted passive UAVs, as well as a ground base station (BS) cooperatively estimate the real-time 3D position of a target UAV. Here, the active UAV transmits a measurement signal to the passive UAVs. This signal is reflected via the target UAV and received by the passive UAVs. Each passive UAV estimates the distance of the active-target-passive UAV link and selects an antenna port to share the distance information with the BS. The BS calculates the real-time position of the target UAV. As the target UAV is moving due to its task operation, the controlled UAVs must optimize their trajectories and select optimal antenna port for transmitting the positioning information, aiming to estimate the real-time position of the target UAV. We formulate an optimization problem that optimizes the trajectories of all controlled UAVs and antenna port selection of passive UAVs with the aim of minimizing the target UAV positioning error. To address this problem, an attention-based recurrent multiagent reinforcement learning (AR-MARL) scheme is proposed. In the proposed method, a recurrent neural network (RNN) acts as a local Q function of each controlled UAV to capture its historical state-action pairs, and a transformer is used to analyze the importance of these historical state-action pairs, thus improving the global \mathbf{Q} function approximation accuracy, thereby further improving the positioning accuracy. Simulation results show that the proposed AR-MARL scheme can reduce the average positioning error by up to 17.5 % and 58.5 % compared to the VD-MARL scheme and the proposed method without FAS.