Diffusion Kalman filter for distributed estimation with intermittent observations

We consider the problem of distributed estimation for stochastic linear systems with intermittent observations. An optimal diffusion Kalman filter has been derived by minimizing the mean-squared estimation error for each node. Convergence of the estimation error covariance is proved under some mild assumptions and an upper bound is obtained for the estimation error covariance. A critical value for the arrival rate of observations is provided such that the estimation error covariance is bounded. The effectiveness of the proposed filter is validated via a numerical example involving tracking a moving target in a sensor network.