Partial Diffusion Kalman Filtering
For distributed state estimation in sensor networks, this work offers a communication-efficient variant of diffusion Kalman filtering with theoretical guarantees.
The paper proposes a partial diffusion Kalman filter (PDKF) that reduces internode communication by sharing only a subset of state estimates, and proves its stability and convergence in mean and mean-square senses, with experimental validation showing a profitable trade-off between communication cost and estimation performance.
In conventional distributed Kalman filtering, employing diffusion strategies, each node transmits its state estimate to all its direct neighbors in each iteration. In this paper we propose a partial diffusion Kalman filter (PDKF) for state estimation of linear dynamic systems. In the PDKF algorithm every node (agent) is allowed to share only a subset of its intermediate estimate vectors at each iteration among its neighbors, which reduces the amount of internode communications. We study the stability of the PDKF algorithm where our analysis reveals that the algorithm is stable and convergent in both mean and mean-square senses. We also investigate the steady-state mean-square deviation (MSD) of the PDKF algorithm and derive a closed-form expression that describes how the algorithm performs at the steady-state. Experimental results validate the effectiveness of PDKF algorithm and demonstrate that the proposed algorithm provides a trade-off between communication cost and estimation performance that is extremely profitable.