Distributed Kalman filtering: a bibliographic review

Mahmoud, Magdi S.; Khalid, Haris M.

doi:10.1049/iet-cta.2012.0732

Cited by 167 publications

(89 citation statements)

References 180 publications

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“…Therefore, k i has to be designed so that (25) holds. In the following, we show a constructive algorithm to define matrix K such that the condition in Theorem 1 holds.…”

Section: Convergence Analysismentioning

confidence: 99%

“…Moreover, an important branch of research on distributed estimation is represented by distributed Kalman filters [22] and their combination with the diffusion mechanism [23], [24]. See [25] for a survey. An interesting new field is the link between distributed/decentralized estimation and distributed monitoring (see as example [26], [27] and [28]).…”

Section: Introductionmentioning

confidence: 99%

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Decentralized state estimation for heterogeneous multi-agent systems

Boem

Secchi

2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Abstract-The paper proposes a decentralized state estimation method for the control of multi-agent networked systems, where the goal is the tracking of arbitrary setpoint functions. The cooperative agents are partitioned into independent robots, providing the control inputs, and dependent robots, controlled by local interaction laws. The proposed state estimation algorithm allows the independent robots to estimate the state of the dependent robots in a completely decentralized way. To do that, it is necessary for each independent robot to estimate the control input components computed by the other independent robots, without requiring communication among the independent robots. The decentralized state estimator, including an input estimator, is developed and the convergence properties are studied. Simulation results show the effectiveness of the proposed approach.

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“…Therefore, k i has to be designed so that (25) holds. In the following, we show a constructive algorithm to define matrix K such that the condition in Theorem 1 holds.…”

Section: Convergence Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decentralized state estimation for heterogeneous multi-agent systems

Boem

Secchi

2015

2015 54th IEEE Conference on Decision and Control (CDC)

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“…Therefore, this chapter also presents a solution based on the accumulated state density (ASD), which is closely related to the DKF but does not require the measurement models to be known. Surveys that reflect the history of research in distributed estimation can be found, for instance, in [2,3].…”

Section: Introductionmentioning

confidence: 99%

Distributed Kalman Filter

Govaers¹

2018

Kalman Filters - Theory for Advanced Applications

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“…Distributed estimation has gained increasing concern since it is more robust to node failure, requires moderate communication and allows for parallel processing. Comprehensive review of the distributed estimation approaches could be found in [52,53].…”

Section: Networked Systems Introduction Of Networked Systems and Netwmentioning

confidence: 99%

Particle filtering with applications in networked systems: a survey

Wang

Yuan

2016

Complex Intell. Syst.

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The particle filtering algorithm was introduced in the 1990s as a numerical solution to the Bayesian estimation problem for nonlinear and non-Gaussian systems and has been successfully applied in various fields including physics, economics, engineering, etc. As is widely recognized, the particle filter has broad application prospects in networked systems, but network-induced phenomena and limited computing resources have led to new challenges to the design and implementation of particle filtering algorithms. In this survey paper, we aim to review the particle filtering method and its applications in networked systems. We first provide an overview of the particle filtering methods as well as networked systems, and then investigate the recent progress inThe work of W. Li, Y. Yuan and L.

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Distributed Kalman filtering: a bibliographic review

Cited by 167 publications

References 180 publications

Decentralized state estimation for heterogeneous multi-agent systems

Decentralized state estimation for heterogeneous multi-agent systems

Distributed Kalman Filter

Particle filtering with applications in networked systems: a survey

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