Message Passing Enhanced Distributed Kalman Filter for Cooperative Localization

This letter proposes a message passing enhanced distributed Kalman filter (MP-KF) for cooperative localization (CL). By simplifying the factor graph (FG) model of the traditional belief propagation (BP) algorithm, MP-KF replaces part of the message passing (MP) process in BP with the distributed Kalman filtering. According to the analysis, the computational complexity of MP-KF is lower than that of the traditional BP estimator. The results based on the experimental data set verify the effectiveness and advantages of MP-KF, it outperforms KF-based methods by fully exploiting the correlation inside a CL system, and is better than the BP-based methods by avoiding the performance loss caused by data smoothing. Results also show that MP-KF is a cost-effective approach for CL systems with an acceptable real-time performance, which is suitable for practical CL systems.