Optimal Kalman filtering fusion with cross-correlated sensor noises

Song, Enbin; Zhu, Yunmin; Zhou, Jie; Zhou, You

doi:10.1016/j.automatica.2007.01.010

Cited by 194 publications

(118 citation statements)

References 5 publications

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“…In [211] [212], distributed recursive mean-square error optimal quantizer-estimator based on the quantized observations is presented. Other DKF applications can be seen in [335], [336], [338], [339] . [38], [39], [40], [41], [42], [43], [44], [105], [106], [109], [114], [119], [156], [179], [191], [197], [213], [214], [215], [216], [221], [233], [237] , [238] and [242].…”

Section: Dkf With Applicationsmentioning

confidence: 99%

Distributed Kalman Filtering

Khalid¹

2014

Networked Filtering and Fusion in Wireless Sensor Networks

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Section: Dkf With Applicationsmentioning

confidence: 99%

Distributed Kalman Filtering

Khalid¹

2014

Networked Filtering and Fusion in Wireless Sensor Networks

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“…Sensor noises of converted system cross-correlated, and also correlated with the original system is treated in [335]. Centralized fusion center, expressed by a linear combination of the local estimates is presented in [336]. Bayesian framework for adaptive quantization, fusion-center feedback, and estimation of a spatial random field and its parameters are treated in [65].…”

Section: Msdf Systemsmentioning

confidence: 99%

Distributed Kalman filtering

Mahmoud¹

2012

Distributed Control and Filtering for Industrial Systems

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“…In fusion nodes a global state estimate is calculated. It may be equivalent to the optimal centralized estimate [4,5,10] or suboptimal [1,2,3].…”

Section: Introductionmentioning

confidence: 99%

“…In these architecture each node carries out Kalman filtering upon its own measurements and then transmits the local processed data to a fusion center [4,5,10] (hierarchical structure) or to other nodes [9,8] (fully decentralized structure). In fusion nodes a global state estimate is calculated.…”

Section: Introductionmentioning

confidence: 99%

State estimation in a decentralized discrete time LQG control for a multisensor system

Duda

2017

Archives of Control Sciences

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In the paper a state filtration in a decentralized discrete time Linear Quadratic Gaussian problem formulated for a multisensor system is considered. Local optimal control laws depend on global state estimates and are calculated by each node. In a classical centralized information pattern the global state estimators use measurements data from all nodes. In a decentralized system the global state estimates are computed at each node using local state estimates based on local measurements and values of previous controls, from other nodes.In the paper, contrary to this, the controls are not transmitted between nodes. It leads to nonconventional filtration because the controls from other nodes are treated as random variables for each node. The cost for the additional reduced transmission is an increased filter computation at each node.

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Optimal Kalman filtering fusion with cross-correlated sensor noises

Cited by 194 publications

References 5 publications

Distributed Kalman Filtering

Distributed Kalman Filtering

Distributed Kalman filtering

State estimation in a decentralized discrete time LQG control for a multisensor system

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