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Optimally distributed kalman filtering with data-driven communication

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

Abstract (englisch):
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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Verlagsausgabe §
DOI: 10.5445/IR/1000082254
Veröffentlicht am 23.04.2018
DOI: 10.3390/s18041034
Zitationen: 6
Web of Science
Zitationen: 3
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Zeitschriftenaufsatz
Jahr 2018
Sprache Englisch
Identifikator ISSN: 1424-8220
KITopen-ID: 1000082254
Erschienen in Sensors
Band 18
Heft 4
Seiten Article: 1034
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Schlagworte distributed Kalman Filtering; data-driven communication; distributed data fusion; sensor networks
Nachgewiesen in Scopus
Web of Science
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