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Distributed Multisensor Data Fusion under Unknown Correlation and Data Inconsistency
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DOI：10.3390/s17112472.png)
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摘要
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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.
Keyword:
multisensor data fusion
decentralized estimation
distributed fusion
inconsistent estimates
spurious data
unknown correlation
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3.5
论文数:
7.2W
被引数:
20.9W
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