Toward State Estimation Algorithms in Cooperative Positioning: A Comprehensive Framework Design and Its Realization

Cooperative positioning enhances localization accuracy in intelligent artifact swarms through shared measurements, especially in environments such as urban canyons or disaster zones. Dynamic topologies, heterogeneous measurements, and scalability demands create complex challenges for state estimation. This paper introduces MRCA, the first comprehensive framework unifying heterogeneous measurements, reasoning processes, environment cognition, and scenario-based applications for cooperative positioning. The four-layer architecture comprises: i) the Measurement block converts signals into measurement parameters in a standard format independent of sensor types and measurement methods; ii) the Reasoning block uncovers the underlying principles of state transition using a four reasoning elements; iii) the Cognition block underscores the importance of node-environment interactions by identifying new valid radiation sources and verifying the ongoing validity of existing ones; iv) the Application block offers users task-driven cooperative positioning services, like execute potential navigation actions. Our proposed framework demonstrates performance improvements in various key metrics compared to legacy swarm positioning algorithms. Focusing on the framework itself, we employ the most basic algorithms within each block, and the experimental results are as follows: In scenarios featuring static unknown anchors, sparse perception, and collision interference, MRCA reduces cumulative positioning error by over 51% for dead-reckoning and 39% for traditional distributed cooperative positioning, while reducing average orientation error by over 31% and 27%, respectively. Running on${\$}5$consumer-grade IMUs, MRCA achieves a final RMSE of 10.27 m, outperforming even a${\$}1~500$industrial-grade IMU in dead-reckoning mode (11.99 m). Through the validation of real - scene datasets, the paper illustrates how this framework integrates into system-level solutions for real-world implementation, further advancing the theoretical understanding of state estimation in cooperative positioning systems.