Distributed Kalman filtering with reduced transmission rate

Dormann, Katharina; Noack, Benjamin; Hanebeck, Uwe D.

doi:10.1109/mfi.2017.8170437

Cited by 1 publication

(1 citation statement)

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“…As compared to the standard formulation of the ODKF, the advantage of the proposed extension is that the data sink can now compute an estimate of the state based on a subset of local estimates. This article continues the work in [ 31 ] by introducing an additional criterion for the data-driven transmission strategy, providing more details, and extending the evaluation and discussions.…”

Section: Introductionmentioning

confidence: 54%

Optimally Distributed Kalman Filtering with Data-Driven Communication

Dormann

Noack

Hanebeck

2018

Sensors

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For multisensor data fusion, distributed state estimation techniques that enable a local processing of sensor data are the means of choice in order to minimize storage and communication costs. In particular, a distributed implementation of the optimal Kalman filter has recently been developed. A significant disadvantage of this algorithm is that the fusion center needs access to each node so as to compute a consistent state estimate, which requires full communication each time an estimate is requested. In this article, different extensions of the optimally distributed Kalman filter are proposed that employ data-driven transmission schemes in order to reduce communication expenses. As a first relaxation of the full-rate communication scheme, it can be shown that each node only has to transmit every second time step without endangering consistency of the fusion result. Also, two data-driven algorithms are introduced that even allow for lower transmission rates, and bounds are derived to guarantee consistent fusion results. Simulations demonstrate that the data-driven distributed filtering schemes can outperform a centralized Kalman filter that requires each measurement to be sent to the center node.

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Section: Introductionmentioning

confidence: 54%