Signal Processing
A fully decentralized multi-sensor system for tracking and surveillance
International Journal of Robotics Research
Robust distributed estimation in sensor networks using the embedded polygons algorithm
Proceedings of the 3rd international symposium on Information processing in sensor networks
Data fusion in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Distributed LMS for consensus-based in-network adaptive processing
IEEE Transactions on Signal Processing
Distributed recursive least-squares for consensus-based in-network adaptive estimation
IEEE Transactions on Signal Processing
Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals
IEEE Transactions on Signal Processing
Distributing the Kalman Filter for Large-Scale Systems
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Signal Processing
Technical Communique: The optimality for the distributed Kalman filtering fusion with feedback
Automatica (Journal of IFAC)
Distributed Kalman filtering based on consensus strategies
IEEE Journal on Selected Areas in Communications
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A method to implement the optimal decentralized Kalman filter and the optimal decentralized Lainiotis filter is proposed; the method is based on the a priori determination of the optimal distribution of measurements into parallel processors, minimizing the computation time. The resulting optimal Kalman filter and optimal Lainiotis filter require uniform distribution or near to uniform distribution of measurements into parallel processors. The optimal uniform distribution has the advantages of elimination of idle time for the local processors and of low hardware cost, but it is not always applicable. The optimal filters present high parallelism speedup; this is verified through simulation results and is very important due to the fact that, in most real-time applications, it is essential to obtain the estimate in the shortest possible time.