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Sensors 2017, 17(11), 2472; doi:10.3390/s17112472

Distributed Multisensor Data Fusion under Unknown Correlation and Data Inconsistency

Intelligent Systems Research Institute, Sungkyunkwan University, Suwon, Gyeonggi-do 440-746, Korea
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Received: 7 September 2017 / Revised: 21 October 2017 / Accepted: 25 October 2017 / Published: 27 October 2017
(This article belongs to the Section Sensor Networks)
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Abstract

The paradigm of multisensor data fusion has been evolved from a centralized architecture to a decentralized or distributed architecture along with the advancement in sensor and communication technologies. These days, distributed state estimation and data fusion has been widely explored in diverse fields of engineering and control due to its superior performance over the centralized one in terms of flexibility, robustness to failure and cost effectiveness in infrastructure and communication. However, distributed multisensor data fusion is not without technical challenges to overcome: namely, dealing with cross-correlation and inconsistency among state estimates and sensor data. In this paper, we review the key theories and methodologies of distributed multisensor data fusion available to date with a specific focus on handling unknown correlation and data inconsistency. We aim at providing readers with a unifying view out of individual theories and methodologies by presenting a formal analysis of their implications. Finally, several directions of future research are highlighted. View Full-Text
Keywords: multisensor data fusion; decentralized estimation; distributed fusion; inconsistent estimates; spurious data; unknown correlation multisensor data fusion; decentralized estimation; distributed fusion; inconsistent estimates; spurious data; unknown correlation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Bakr, M.A.; Lee, S. Distributed Multisensor Data Fusion under Unknown Correlation and Data Inconsistency. Sensors 2017, 17, 2472.

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