Multiuser Augmented Reality (MuAR) is essential to implementing the vision of Metaverse for its capability to provide immersive and interactive experiences. In such experiences, peer positions are critical to understand each other’s intentions and actions so as to guarantee the smooth cooperation among users. However, we find that the explicit peer positions provided by the current practice could be incomplete and/or inaccurate in some situations, which leads to the weakened spatial awareness. To achieve the accurate peer tracking in MuAR, we propose a novel multiple sensors information fusion method, CSA (Coordinate System Alignment), to detect and correct defective relative positions by the current practice. CSA firstly formulates problem of correcting erroneous positions into an overdetermined system, and then finds the solution by applying the simulated annealing algorithm to expedite the search process. The evaluation results show that CSA’s ability to reduce errors significantly (58.3% on average) under long-term error duration, especially its advantage in reducing the relative direction errors. The result confirms the potential of CSA to provide reliable peer tracking in MuAR. Meanwhile, it does not impose extra restrictions on users’ practice with current mobile devices in experiences.