Multisensor data fusion: A review of the state-of-the-art

被引：1944

作者：

Khaleghi, Bahador ^{[1
]}

Khamis, Alaa ^{[1
]}

Karray, Fakhreddine O. ^{[1
]}

Razavi, Saiedeh N. ^{[2
]}

机构：

[1] Univ Waterloo, Pattern Anal & Machine Intelligence Lab, Waterloo, ON N2L 3G1, Canada

[2] McMaster Univ, Dept Civil Engn, Hamilton, ON, Canada

来源：

INFORMATION FUSION | 2013年 / 14卷 / 01期

关键词：

Multisensor data fusion; Taxonomy; Fusion methodologies; DEMPSTER-SHAFER THEORY; INFORMATION FUSION; PERFORMANCE EVALUATION; MULTIPLE-SENSOR; COMBINATION; PROBABILITY; TRACKING; RECONSTRUCTION; ALGORITHMS; METRICS;

D O I：

10.1016/j.inffus.2011.08.001

中图分类号：

TP18 [人工智能理论];

学科分类号：

140502 [人工智能];

摘要：

There has been an ever-increasing interest in multi-disciplinary research on multisensor data fusion technology, driven by its versatility and diverse areas of application. Therefore, there seems to be a real need for an analytical review of recent developments in the data fusion domain. This paper proposes a comprehensive review of the data fusion state of the art, exploring its conceptualizations, benefits, and challenging aspects, as well as existing methodologies. In addition, several future directions of research in the data fusion community are highlighted and described. (c) 2011 Elsevier B.V. All rights reserved.

引用

页码：28 / 44

页数：17

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