On Boundedness of Error Covariances for Kalman Consensus Filtering Problems
@article{Li2020OnBO, title={On Boundedness of Error Covariances for Kalman Consensus Filtering Problems}, author={Wangyan Li and Zidong Wang and Daniel W. C. Ho and Guoliang Wei}, journal={IEEE Transactions on Automatic Control}, year={2020}, volume={65}, pages={2654-2661} }
In this paper, the uniform bounds of error covariances for several types of Kalman consensus filters (KCFs) are investigated for a class of linear time-varying systems over sensor networks with given topologies. Rather than the traditional detectability assumption, a new concept called collectively uniform detectability (CUD) is proposed to address the detectability issues over sensor networks with relaxed restrictions. By using matrix inequality analysis techniques, the conditions for the…
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References
SHOWING 1-10 OF 37 REFERENCES
A New Look at Boundedness of Error Covariance of Kalman Filtering
- MathematicsIEEE Transactions on Systems, Man, and Cybernetics: Systems
- 2018
By utilizing the mathematical induction technique, a new bound function which is dependent on system parameters is proposed and based on such a bound function, the dynamic behaviors, monotonicities, and boundedness problems of error covariance are deeply explored.
Consensus based overlapping decentralized estimation with missing observations and communication faults
- MathematicsAutom.
- 2009
Stability of consensus extended Kalman filter for distributed state estimation
- Engineering, MathematicsAutom.
- 2016
Distributed H∞-consensus filtering in sensor networks with multiple missing measurements: The finite-horizon case
- MathematicsAutom.
- 2010
Convergence properties of a decentralized Kalman filter
- Mathematics2008 47th IEEE Conference on Decision and Control
- 2008
For a time-invariant process and measurement model, it is shown that this algorithm guarantees that the local estimates of the error covariance matrix converge to the centralized error covariances matrix and that theLocal estimates ofThe state converge in mean to the central Kalman filter estimates.
Protocol-Based Unscented Kalman Filtering in the Presence of Stochastic Uncertainties
- MathematicsIEEE Transactions on Automatic Control
- 2020
The unscented Kalman filtering (UKF) problem is investigated for a class of general nonlinear systems with stochastic uncertainties under communication protocols and two resource-saving UKF algorithms are developed, where the impact from the underlying protocols on the filter design is explicitly quantified.
Kalman-Consensus Filter : Optimality, stability, and performance
- MathematicsProceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference
- 2009
The main contributions of this paper are finding the optimal decentralized Kalman-Consensus filter and showing that its computational and communication costs are not scalable in n and introducing a scalable suboptimalKalman-consensus Filter.
Gossip and Distributed Kalman Filtering: Weak Consensus Under Weak Detectability
- MathematicsIEEE Transactions on Signal Processing
- 2011
The GIKF error process remains stochastically bounded, irrespective of the instability of the random process dynamics; and the network achieves weak consensus, i.e., the conditional estimation error covariance at a (uniformly) randomly selected sensor converges in distribution to a unique invariant measure on the space of positive semidefinite matrices.
A distributed Kalman filter with global covariance
- MathematicsProceedings of the 2011 American Control Conference
- 2011
The main contribution consists of a proof that the proposed DKF algorithm, in combination with EI for state-fusion, enjoys the desired property under similar conditions that should hold for observability of standard Kalman filters.