Optimal consensus-based distributed estimation with intermittent communication

In this article, we consider the problem of distributed state estimation over a wireless sensor network (WSN). We firstly propose a distributed algorithm to estimate the state of a system by modelling the WSN as a directed network. Based on the Kalman filter, we introduce a consensus scheme for each sensor by including the estimates received from its neighbour sensors. We also consider intermittent and random data packet drops which are frequently seen in wireless networks. A sufficient condition is derived for the convergence of the state estimation error, and a upper bound is obtained for the estimation error covariance. We further consider minimising the estimation error by finding an optimal consensus gain for a given fixed network. The performance and effectiveness of the proposed algorithm are compared with existing well-known results from the literature.

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