• Corpus ID: 9908089

Decentralized State Estimation via a Hybrid of Consensus and Covariance intersection

@article{Tamjidi2016DecentralizedSE,
  title={Decentralized State Estimation via a Hybrid of Consensus and Covariance intersection},
  author={Amirhossein Tamjidi and Suman Chakravorty and Dylan A. Shell},
  journal={ArXiv},
  year={2016},
  volume={abs/1603.00955}
}
This paper presents a new recursive information consensus filter for decentralized dynamic-state estimation. No structure is assumed about the topology of the network and local estimators are assumed to have access only to local information. The network need not be connected at all times. Consensus over priors which might become correlated is performed through Covariance Intersection (CI) and consensus over new information is handled using weights based on a Metropolis Hastings Markov Chains… 

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References

SHOWING 1-10 OF 13 REFERENCES

Diffusion Kalman Filtering Based on Covariance Intersection

TLDR
A diffusion Kalman filtering algorithm based on the covariance intersection method, where local estimates are fused by incorporating the covariances information of local Kalman filters, which leads to a stable estimate for each agent.

Distributed information filtering using consensus filters

TLDR
It is shown that local information consensus filter estimates are unbiased, and the actual variance of the local estimation errors is comparable to a centralized estimate, however, local agents believe their local estimates are less accurate.

Delayed-state information filter for cooperative decentralized tracking

TLDR
A Decentralized Delayed-State Extended Information Filter is described, where full state trajectories are considered to fuse the information, and this permits to obtain an estimation equal to that obtained by a centralized system, and allows delays and latency in the communications.

Estimation under unknown correlation: covariance intersection revisited

TLDR
It is proved that the global optimal solution is actually given by the covariance intersection algorithm, which conducts the search only along a one-dimensional curve in the n-squared-dimensional space of combination gains.

A scheme for robust distributed sensor fusion based on average consensus

  • Lin XiaoStephen P. BoydS. Lall
  • Computer Science, Mathematics
    IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005.
  • 2005
TLDR
This work proposes a simple distributed iterative scheme, based on distributed average consensus in the network, to compute the maximum-likelihood estimate of the parameters, and shows that it works in a network with dynamically changing topology, provided that the infinitely occurring communication graphs are jointly connected.

Distributed Kalman Filter with Embedded Consensus Filters

  • R. Olfati-Saber
  • Mathematics
    Proceedings of the 44th IEEE Conference on Decision and Control
  • 2005
TLDR
This paper shows that a central Kalman filter for sensor networks can be decomposed into n micro-Kalman filters with inputs that are provided by two types of consensus filters, and demonstrates that these filters can approximate these sums and give an approximate distributed Kalman filtering algorithm.

Distributed Estimation Fusion with Unavailable Cross-Correlation

  • Yimin WangX. Li
  • Computer Science, Mathematics
    IEEE Transactions on Aerospace and Electronic Systems
  • 2012
TLDR
A fault-tolerant GCC fusion algorithm is proposed by introducing an adaptive parameter, which can obtain robust fusion and the degree of robustness varies with that of incoherency between estimates to be fused.

Sequential covariance intersection fusion Kalman filter

Dynamic Field Estimation Using Wireless Sensor Networks: Tradeoffs Between Estimation Error and Communication Cost

TLDR
Efficient methods are presented to apply Pareto optimality to evaluate the tradeoffs between communication costs and RMS estimation error to select the best reduced-order Kalman-Bucy filter.

Distributed Information Filters for MAV Cooperative Localization

TLDR
This paper introduces a new approach to the problem of simultaneously localizing a team of micro aerial vehicles equipped with inertial sensors able to monitor their motion and with exteroceptive sensors based on an Extended Information Filter whose implementation is distributed over the team members.