A System-Level Cooperative Multiagent GNSS Positioning Solution

We present a multiagent cooperative estimation method for improving the performance of global navigation satellite systems (GNSSs). The proposed method uses existing receiver technology, avoids interagent communication, and minimizes the computational overhead in the agents. The method is based on recursive mixed-integer Kalman filtering for a system characterized by several agents in a bipartite star graph structure, where the nodes in one of the vertex sets perform local filtering based on local information, and a single node in the other vertex set estimates all of the system states using interagent error correlations in the context of partially overlapping local state spaces. We conduct extensive Monte-Carlo (MC) simulation studies in an urban driving scenario using a road map from an actual city, incorporating real satellite trajectories and realistic ionospheric bias modeling. In addition, we perform a hardware-in-the-loop study. The results indicate that the method can correct erroneous estimates in faulty agents by leveraging cooperation with other agents, improving accuracy from decimeter level to centimeter level for that particular agent. When all agents have similar residual biases, expected improvements in the root-mean-square position error typically range between 20% and 100%.