Distributed weighted averaging-based robust Cubature Kalman Filter for state estimation of nonlinear systems in wireless sensor networks

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This paper studies the problem of distributed state estimation for nonlinear systems in the presence of uncertainty with the Cubature Kalman Filter (CKF) framework by employing distributed weighted averaging in a wireless sensor network. The communication status among sensors is determined via a connected undirected graph. Firstly, each sensor node uses its own measurements and observations to estimate the states of a system locally and independently. Since the algorithm is implemented in the distributed mode and there is not any fusion center, each sensor node communicates with its neighbors through a distributed weighted averaging algorithm where the optimal weight matrix and the corresponding variance of the optimal information fusion are updated in each implementation step. This proposed algorithm does not need any specific information about the plant uncertainty, since uncertainty estimation is considered in the algorithm. Finally, a numerical example is given and the proposed filtering algorithm is evaluated through simulation of a system for a ballistic target tracking.