Multiagent Cooperative Positioning Under Unstable Communications and Measurement Biases

Cooperative positioning enhances positioning performance but suffers significantly from unstable communication and measurement biases. To address these challenges, this work proposes a communication and measurement robust message passing (CMR-MP) method. First, we establish a multicentralized framework that decouples communication from measurement processes. Accordingly, a communication-robust factor graph is designed with constraints adapting to varying communication statuses, enabling neighbor state propagation without control information. This allows full data exploitation even when communication status changes. Second, to mitigate biases, we incorporate a general robust kernel into the Gaussian message passing process. This adaptive robust factor dynamically fits bias distributions to properly weight measurements. Experimental results demonstrate that CMR-MP outperforms state-of-the-art methods; specifically, its circular error probability is at least two times better than conventional approaches under unstable communication and mixed bias conditions.