In complex environments, communication networks within an unmanned ground vehicle (UGV) swarm are vulnerable to disruption due to terrain-induced blockage, high node mobility, and limited communication ranges. Employing an unmanned aerial vehicle (UAV) as a mobile relay provides an effective means of maintaining reliable communication. However, under partially observable and high-noise conditions, noisy observations may fail to accurately reflect the actual motion states of ground nodes, thereby degrading the reliability of UAV relay decisions. To address this issue, this paper proposes a Kalman filter–Soft Actor–Critic (KF-SAC) algorithm. This method employs a Kalman filter to fuse the motion models of the ground nodes with noisy measurements, thereby obtaining posterior estimates of motion states, such as position and velocity, as well as the corresponding motion trends. By feeding these estimates into the SAC agent as state inputs, the method improves the reliability of UAV relay trajectory decisions and enhances adaptability to dynamic and noisy environments. Simulation results demonstrate that, compared with standard SAC, the proposed method effectively reduces the overall communication cost of the UGV swarm and improves communication quality amidst complex terrain and noisy observation conditions.