Distributed Kalman Filtering With Dynamic Observations Consensus

This paper studies distributed estimation of unstable dynamic random fields observed by a sparsely connected network of sensors. The field dynamics are globally detectable, but not necessarily locally detectable. We propose a consensus+innovations distributed estimator, termed Distributed Information Kalman Filter. We prove under what conditions this estimator is asymptotically unbiased with bounded mean-squared error, smaller than for other alternative distributed estimators. Monte Carlo simulations confirm our theoretical error asymptotic results.