Estimation from lossy sensor data: jump linear modeling and Kalman filtering
Proceedings of the 3rd international symposium on Information processing in sensor networks
Estimation in sensor networks: a graph approach
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Optimal recursive estimation with uncertain observation
IEEE Transactions on Information Theory
Recursive Bayesian estimation with uncertain observation (Corresp.)
IEEE Transactions on Information Theory
Asymptotic stability of the MMSE linear filter for systems with uncertain observations (Corresp.)
IEEE Transactions on Information Theory
H∞ filtering of network-based systems with random delay
Signal Processing
Remote Estimation with Sensor Scheduling
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Brief paper: Kalman filtering over a packet-delaying network: A probabilistic approach
Automatica (Journal of IFAC)
Kalman filtering with faded measurements
Automatica (Journal of IFAC)
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Wireless Communications
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CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
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IEEE Transactions on Communications
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Automatica (Journal of IFAC)
Brief paper: Stabilization of Markov jump linear systems using quantized state feedback
Automatica (Journal of IFAC)
IEEE Transactions on Signal Processing
Networked H∞ filtering for linear discrete-time systems
Information Sciences: an International Journal
Mean square stability for Kalman filtering with Markovian packet losses
Automatica (Journal of IFAC)
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Automatica (Journal of IFAC)
On Kalman filtering over fading wireless channels with controlled transmission powers
Automatica (Journal of IFAC)
SIAM Journal on Control and Optimization
Automatica (Journal of IFAC)
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Information Sciences: an International Journal
On mode-dependent H∞ filtering for network-based discrete-time systems
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Adaptive controller placement for wireless sensor-actuator networks with erasure channels
Automatica (Journal of IFAC)
Hi-index | 22.17 |
We consider Kalman filtering in a network with packet losses, and use a two state Markov chain to describe the normal operating condition of packet delivery and transmission failure. Based on the sojourn time of each visit to the failure or successful packet reception state, we analyze the behavior of the estimation error covariance matrix and introduce the notion of peak covariance, as an estimate of filtering deterioration caused by packet losses, which describes the upper envelope of the sequence of error covariance matrices {P"t,t=1} for the case of an unstable scalar model. We give sufficient conditions for the stability of the peak covariance process in the general vector case, and obtain a sufficient and necessary condition for the scalar case. Finally, the relationship between two different types of stability notions is discussed.