Peer-to-peer estimation over wireless sensor networks via Lipschitz optimization
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
An optimal control method for applications using wireless sensor/actuator networks
Computers and Electrical Engineering
Estimation of target location via likelihood approximation in sensor networks
IEEE Transactions on Signal Processing
Hearing the clusters of a graph: A distributed algorithm
Automatica (Journal of IFAC)
Design and implementation of a robust sensor data fusion system for unknown signals
DCOSS'10 Proceedings of the 6th IEEE international conference on Distributed Computing in Sensor Systems
On Kalman filtering over fading wireless channels with controlled transmission powers
Automatica (Journal of IFAC)
Consensus-based linear distributed filtering
Automatica (Journal of IFAC)
State fusion with unknown correlation: Ellipsoidal intersection
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
ACM Transactions on Sensor Networks (TOSN)
An assessment of distributed state estimation
International Journal of Systems, Control and Communications
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A distributed estimation algorithm for sensor networks is proposed. A noisy time-varying signal is jointly tracked by a network of sensor nodes, in which each node computes its estimate as a weighted sum of its own and its neighbors' measurements and estimates. The weights are adaptively updated to minimize the variance of the estimation error. Both estimation and the parameter optimization is distributed; no central coordination of the nodes is required. An upper bound of the error variance in each node is derived. This bound decreases with the number of neighboring nodes. The estimation properties of the algorithm are illustrated via computer simulations, which are intended to compare our estimator performance with distributed schemes that were proposed previously in the literature. The results of the paper allow to trading-off communication constraints, computing efforts and estimation quality for a class of distributed filtering problems.