An optimal control method for applications using wireless sensor/actuator networks
Computers and Electrical Engineering
WONS'09 Proceedings of the Sixth international conference on Wireless On-Demand Network Systems and Services
Average consensus based scalable robust filtering for sensor network
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
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IEEE Transactions on Signal Processing
Brief paper: Moving-horizon partition-based state estimation of large-scale systems
Automatica (Journal of IFAC)
Distributed consensus on camera pose
IEEE Transactions on Image Processing
Low-power distributed Kalman filter for wireless sensor networks
EURASIP Journal on Embedded Systems
Distributed consensus algorithms for merging feature-based maps with limited communication
Robotics and Autonomous Systems
Brief paper: A majorization inequality and its application to distributed Kalman filtering
Automatica (Journal of IFAC)
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Automatica (Journal of IFAC)
Brief paper: Data-driven communication for state estimation with sensor networks
Automatica (Journal of IFAC)
Distributed estimation via iterative projections with application to power network monitoring
Automatica (Journal of IFAC)
Impulsive consensus algorithms for second-order multi-agent networks with sampled information
Automatica (Journal of IFAC)
Consensus-based linear distributed filtering
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Multi-rate distributed fusion estimation for sensor networks with packet losses
Automatica (Journal of IFAC)
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Digital Signal Processing
An assessment of distributed state estimation
International Journal of Systems, Control and Communications
Graph diameter, eigenvalues, and minimum-time consensus
Automatica (Journal of IFAC)
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In this paper, we consider the problem of estimating the state of a dynamical system from distributed noisy measurements. Each agent constructs a local estimate based on its own measurements and on the estimates from its neighbors. Estimation is performed via a two stage strategy, the first being a Kalman-like measurement update which does not require communication, and the second being an estimate fusion using a consensus matrix. In particular we study the interaction between the consensus matrix, the number of messages exchanged per sampling time, and the Kalman gain for scalar systems. We prove that optimizing the consensus matrix for fastest convergence and using the centralized optimal gain is not necessarily the optimal strategy if the number of exchanged messages per sampling time is small. Moreover, we show that although the joint optimization of the consensus matrix and the Kalman gain is in general a non-convex problem, it is possible to compute them under some relevant scenarios. We also provide some numerical examples to clarify some of the analytical results and compare them with alternative estimation strategies.