Weighted Average Consensus-Based Unscented Kalman Filtering

Li, Wangyan; Han, Fei; Liu, Yurong

doi:10.1109/tcyb.2015.2409373

Cited by 261 publications

(140 citation statements)

References 27 publications

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“…Future research topics would include the extension of the main results to more complex systems by using more up-to-date techniques. [28][29][30][31][32][33][34][35]…”

Section: Resultsmentioning

confidence: 99%

Sampled‐data consensus of nonlinear multiagent systems subject to cyber attacks

Zhang

Wang

Liu

et al. 2017

Intl J Robust & Nonlinear

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This paper is concerned with the sampled-data consensus problem for a class of nonlinear multiagent systems subject to cyber attacks. The considered attacks are assumed to be recoverable, which destroy the connectivity of the network topology with directed spanning tree. In light of the designed sampled-data control protocol, a more general switched system is proposed to model both the cyber attacks and the sampled-data mechanism within a unified framework. Then, by using the contradiction methods, the solution of a class of differential equations is provided to deal with the technical challenge resulting from the switching and sampling issues. Furthermore, in terms of such a solution combined with the constructed piecewise Lyapunov function, sufficient conditions are derived under which the nonlinear multiagent systems subject to attacks achieve the consensus exponentially. The relationship between the attack frequency and the sampling period is also revealed through the obtained conditions. Finally, simulations are given to show the effectiveness of the proposed results. KEYWORDSconsensus, cyber attacks, multiagent systems, sampled data, switched systems Corrections added on 3 July 2017, after first online publication: the word 'Equation' had been inserted incorrectly in some cases in the text and has since been removed.Int J Robust Nonlinear Control. 2018;28:53-67.wileyonlinelibrary.com/journal/rnc

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“…Future research topics would include the extension of the main results to more complex systems by using more up-to-date techniques. [28][29][30][31][32][33][34][35]…”

Section: Resultsmentioning

confidence: 99%

Sampled‐data consensus of nonlinear multiagent systems subject to cyber attacks

Zhang

Wang

Liu

et al. 2017

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

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“…Proof of Theorem 2: Assume that as n → ∞ both matrix sets {M l,n+1|n , Φ l,n : ∀l ∈ N } and {M l,n+1|n , Φ l,n : ∀l ∈ N } stabilize (17). Then, for l ∈ N , the evolution of M l,n+1|n − M l,n+1|n is attainable from (21)-(20) where M l,n|n−1 and Φ l,n−1 are to be replaced by M l,n+1|n and Φ l,n .…”

Section: Algorithm 2 Distributed Kalman Filter Through Embedded Avementioning

confidence: 99%

“…In order to present filtering solutions for sensor networks that are robust to link/agent failure and scalable with the size of the network, distributed filtering algorithms have been proposed in the context of consensus [12,16,17] and diffusion [9,15] or diffusion steps where agents of the sensor network average their intermediate state vector estimates with their neighbors allowing the agents to establish a consensus on the estimate of the state vector [12,15,16,18]. However, the current literature on distributed Kalman filtering is mostly concerned with estimation problems; hence, the developed frameworks are not readily expandable to decentralized control applications.…”

Section: Introductionmentioning

confidence: 99%

Distributed Kalman Filtering and Control Through Embedded Average Consensus Information Fusion

Talebi

Werner

2019

IEEE Trans. Automat. Contr.

123

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This work presents a unified framework for distributed filtering and control of state-space processes. To this end, a distributed Kalman filtering algorithm is developed via decomposition of the optimal centralized Kalman filtering operations. This decomposition is orchestrated in a fashion so that each agent retains a Kalman style filtering operation and an estimate of the state vector. In this setting, the agents mirror the operations of the centralized Kalman filter in a distributed fashion through embedded average consensus fusion of local state vector estimates and their associated covariance information. For rigor, closed-form expressions for the mean and mean square error performance of the developed distributed Kalman filter are derived. More importantly, in contrast to current approaches, due to the comprehensive framework for fusion of the covariance information, a duality between the developed distributed Kalman filter and decentralized control is established. Thus, resulting in an effective and all inclusive distributed framework for filtering and control of state-space processes over a network of agents. The introduced theoretical concepts are validated using simulations that indicate a precise match between simulation results and the theoretical analysis. In addition, simulations indicate that performance levels comparable to that of the optimal centralized approaches are attainable.

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“…So far, considerable effort has been devoted to the investigation of the distributed filtering problems and a number of strategies have been developed based on the Kalman filtering theory or the H ∞ filtering theory, see [5], [6], [13], [14], [16], [26], [31] for some recent results. As is well known, the Kalman filtering technique requires an assumption of Gaussian distributions for the process and measurement noises, while the H ∞ theory can be utilized in the occasion when the disturbances are assumed to have bounded energy.…”

Section: Introductionmentioning

confidence: 99%

Variance-Constrained Distributed Filtering for Time-Varying Systems With Multiplicative Noises and Deception Attacks Over Sensor Networks

et al. 2017

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This paper is concerned with the varianceconstrained distributed filtering problem for a class of timevarying systems subject to multiplicative noises, unknown but bounded disturbances and deception attacks over sensor networks. The available measurements at each sensing node are collected not only from the individual sensor but also from its neighbors according to the given topology. A new deception attack model is proposed where the malicious signals are injected by the adversary into both control and measurement data during the process of information transmission via the communication network. By resorting to the recursive linear matrix inequality approach, a sufficient condition is established for the existence of the desired filter satisfying the prespecified requirements on the estimation error variance. Subsequently, an optimization problem is formulated in order to seek the filter parameters ensuring the locally optimal filtering performance at each time instant. Finally, an illustrative example is presented to demonstrate the effectiveness and applicability of the proposed algorithm.

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Weighted Average Consensus-Based Unscented Kalman Filtering

Cited by 261 publications

References 27 publications

Sampled‐data consensus of nonlinear multiagent systems subject to cyber attacks

Sampled‐data consensus of nonlinear multiagent systems subject to cyber attacks

Distributed Kalman Filtering and Control Through Embedded Average Consensus Information Fusion

Variance-Constrained Distributed Filtering for Time-Varying Systems With Multiplicative Noises and Deception Attacks Over Sensor Networks

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