Diffusion Kalman Filtering Based on Covariance Intersection

Abstract

This paper is concerned with distributed Kalman filtering for linear time-varying systems over multiagent sensor networks. We propose a diffusion Kalman filtering algorithm based on the covariance intersection method, where local estimates are fused by incorporating the covariance information of local Kalman filters. Our algorithm leads to a stable estimate for each agent regardless of whether the system is uniformly observable locally by the measurements of its neighbors which include the measurements of itself as long as the system is uniformly observable by the measurements of all the agents and the communication is sufficiently fast compared to the sampling. Simulation results validate the effectiveness of the proposed distributed Kalman filtering algorithm.