Sensor networks localisation using distributed Extended Kalman Filter

In recent years sensor networks have interested fields such as environment monitoring, surveillance and other distributed extraction data applications. This interest has been based on the decentralized approach in treating the information and it is still a challenge to manipulate such streams of data when the dimension of the net becomes large despite computational capabilities and consumption constraint. In most of applications a good estimate of the position of the nodes is required to fuse information respect to a common frame. This need requires the network to localize itself by proper algorithms to minimize the computational costs and communications between nodes due to the hardware characteristics. These difficulties can be mitigated when the network is still; in this way low time convergence in the algorithm is not required because no dynamic constraints are present and the localization process can be more accurate. Exploiting the properties of still network, a distributed Extended Kalman Filter is proposed in this paper.