Estimation from lossy sensor data: jump linear modeling and Kalman filtering
Proceedings of the 3rd international symposium on Information processing in sensor networks
Decentralized Bayesian algorithms for active sensor networks
Information Fusion
Stability of Kalman filtering with Markovian packet losses
Automatica (Journal of IFAC)
Brief paper: Optimal Kalman filtering fusion with cross-correlated sensor noises
Automatica (Journal of IFAC)
Optimal update with out-of-sequence measurements
IEEE Transactions on Signal Processing
Multisensor optimal information fusion input white noise deconvolution estimators
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Active and dynamic information fusion for multisensor systems with dynamic bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
State estimation with asynchronous multi-rate multi-smart sensors
Information Sciences: an International Journal
Asynchronous H∞ filtering of discrete-time switched systems
Signal Processing
On Kalman filtering over fading wireless channels with controlled transmission powers
Automatica (Journal of IFAC)
Linear estimation for networked control systems with random transmission delays and packet dropouts
Information Sciences: an International Journal
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A novel networked multisensor data-fusion method is developed in this paper. A federated filter is employed to fuse the data transmitted over the network, which plays an important role in the data-processing center. The stability of filters under the network is considered; an algorithm to deal with the delayed data is introduced, and the principle for data fusion is presented. Finally, two numerical examples show the effectiveness of the proposed scheme.