Distributed state estimation with stochastic parameters and nonlinearities through sensor networks: The finite-horizon case

Ding, Derui; Wang, Zidong; Dong, Hongli; Shu, Huisheng

doi:10.1016/j.automatica.2012.05.070

Cited by 210 publications

(125 citation statements)

References 39 publications

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“…Solutions G ϕ i|t and H ϕ i|t are feasible to problem (21). Thus, we've proved problem (21) is feasible.…”

Section: Distributed Minimum Variance Estimatormentioning

confidence: 78%

Section: Distributed Minimum Variance Estimatormentioning

confidence: 99%

“…We remark that in optimization problem (21), if C i ν ϕ i|t is full column rank, we can choose any γ ≤ 1/ A according to the requirement of the estimation error bound given in Theorem 3.1. Otherwise, if C i ν ϕ i|t is not full column rank (when A ≤ 1), we let 1 ≤ γ ≤ 1/ A to bound the estimation error given in Theorem 3.1.…”

Section: Distributed Minimum Variance Estimatormentioning

confidence: 99%

“…w(t) ∈ R n models the disturbance in the state, whose 2 -norm is bounded by ∆ for all the time [37,38]. Note that the unknown disturbance is commonly addressed by H ∞ filtering [21,39,37,38]. Suppose we place N sensor nodes at static positions in the space.…”

Section: Problem Formulationmentioning

confidence: 99%

“…In [20], a theoretical framework for coupled distributed estimation and motion control of mobile sensor networks for collaborative target tracking is proposed. In [21] a distributed H ∞ state estimation for discrete time varying nonlinear systems is studied. In this estimator, sensor nodes have knowledge of the fixed network topology.…”

Section: Introductionmentioning

confidence: 99%

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Model based peer-to-peer estimator over wireless sensor networks with lossy channels

Fischione

Speranzon³

2015

Automatica

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In this paper, the design methods and fundamental performance analysis of an adaptive peer-to-peer estimator are established for networks exhibiting message losses. Based on a signal state model, estimates are locally computed at each node of the network by adaptively filtering neighboring nodes' estimates and measurements communicated over lossy channels. The computation is based on a distributed optimization approach that guarantees the stability of the estimator while minimizing the estimation error variance. The fundamental performance limitations of the estimator are established based on the variance of the estimation error in relation to the message loss process. Numerical simulations validate the theoretical analysis and illustrate the performance with respect to other estimators.

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“…Solutions G ϕ i|t and H ϕ i|t are feasible to problem (21). Thus, we've proved problem (21) is feasible.…”

Section: Distributed Minimum Variance Estimatormentioning

confidence: 78%

Section: Distributed Minimum Variance Estimatormentioning

confidence: 99%

Section: Distributed Minimum Variance Estimatormentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Model based peer-to-peer estimator over wireless sensor networks with lossy channels

Fischione

Speranzon³

2015

Automatica

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References

2015

Variance‐Constrained Multi‐Objective Stochastic Control and Filtering

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Distributed resilient state estimation for nonlinear systems with randomly occurring communication delays and missing measurements

Qian

Guo

Zhao

et al. 2021

Adaptive Control & Signal

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This article is concerned with the distributed H ∞ resilient state estimation problem for a class of nonlinear systems with randomly occurring communication delays and missing measurements in sensor networks. A novel sensor model is proposed, in which two Bernoulli distributed white sequences are introduced to describe the random communication delay and missing measurements in a unified framework. Meanwhile, the estimator gain is allowed to fluctuate within a certain range. Based on the developed model, a novel Lyapunov-Krasovskii functional with multiple delay information terms is constructed, then the stochastic analysis technique and the extended integral inequality are used to calculate the functional derivative. Consequently, the existence conditions for the required distributed estimator are established to ensure that the estimation error system is asymptotically mean-square stable and satisfies the prescribed H ∞ performance constraint, and the desired gain of distributed resilient estimator is also solved by linearizing the nonlinear terms. Finally, a numerical example is given to illustrate the effectiveness of the proposed algorithm.

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Distributed state estimation with stochastic parameters and nonlinearities through sensor networks: The finite-horizon case

Cited by 210 publications

References 39 publications

Model based peer-to-peer estimator over wireless sensor networks with lossy channels

Model based peer-to-peer estimator over wireless sensor networks with lossy channels

References

Distributed resilient state estimation for nonlinear systems with randomly occurring communication delays and missing measurements

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