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We consider the distributed Kalman filtering problem for sensor networkswhere each node takes the measurement, communicates with its local neighbors,and updates its local estimate and estimation error covariance at the samefrequency. In such a scenario, if the target is not directly observed byneither a certain node nor its local neighbors, this node is naive about thetarget. The well-known Kalman Consensus filter (KCF) has been shown to performwell if there exists no naive node in the network. The case in presence ofnaive nodes has been considered by the generalized KCF (GKCF) and theInformation Weighted Consensus filter (ICF) later on. However, all theseconsensus-embedded filters require some global information such as the maximumdegree of the graph, or the total number of the nodes. If these globalinformation are changing over the time, such as time-varying topologies orunexpected sensor failures in the network, the performance of the GKCF or theICF might be degraded. With communication topology changes, node failures, oraddition of new nodes, the filter performance would be adversely affected. Inthis paper, we consider a novel local Weighted Least Square estimator for eachnode that utilizes its generalized measurement formed by not only its own andlocal neighbors' measurements but their prior local estimates to track thetarget. With some approximations in the derivation of the covariance matrix, wepropose the Information-driven Fully Distributed Kalman filter (IFDKF), whichis able to deal with the existence of naive nodes without knowing any globalinformation. Experimental results show that the proposed algorithm performsbetter than the existing algorithms in the considered realistic scenario.

Information-driven Fully Distributed Kalman Filter for Sensor Networks in Presence of Naive Nodes