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Conservative Quantization of Fast Covariance Intersection

Funk, Christopher; Noack, Benjamin; Hanebeck, Uwe D.

Abstract:
Sensor data fusion in wireless sensor networks poses challenges with respect to both theory and implementation. Unknown cross-correlations between estimates distributed across the network need to be addressed carefully as neglecting them leads to overconfident fusion results. In addition, limited processing power and energy supply of the sensor nodes prohibit the use of complex algorithms and high-bandwidth communication. In this work, fast covariance intersection using both quantized estimates and quantized covariance matrices is considered. The proposed method is computationally efficient and significantly reduces the bandwidth required for data transmission while retaining unbiasedness and conservativeness of fast covariance intersection. The performance of the proposed method is evaluated with respect to that of fast covariance intersection, which proves its effectiveness even in the case of substantial data reduction.

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Postprint §
DOI: 10.5445/IR/1000123692
Frei zugänglich ab 01.10.2021
Originalveröffentlichung
DOI: 10.1109/MFI49285.2020.9235249
Scopus
Zitationen: 1
Dimensions
Zitationen: 1
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 09.2020
Sprache Englisch
Identifikator ISBN: 978-1-72816-422-9
KITopen-ID: 1000123692
Erschienen in Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020), Karlsruhe, 14 - 16 September 2020
Veranstaltung International Conference on Multisensor Fusion and Integration for Intelligent Systems (2020), Online, 14.09.2020 – 16.09.2020
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 68-74
Bemerkung zur Veröffentlichung Die Veranstaltung fand als Online-Event statt
Nachgewiesen in Dimensions
Scopus
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