Prediction consensus-based distributed Kalman filtering with packet loss

This paper is concerned with the problem of distributed state estimation for linear discrete system with packet loss. A state prediction consensus mechanism is introduced to promote the estimation performance. By applying matrix theory, a sufficient condition for the convergence of the estimation error system is derived. Then, a numerical example is given to show the algorithm proposed in this paper has better consensus performance compared with a classical Kalman consensus filtering (KCF) by some simulations in real and packet-dropping cases, respectively. Finally, the influence of consensus coefficient and prediction consensus coefficient on the estimation error covariance are presented by simulation.