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

Authors:
Bahador Khaleghi;Alaa Khamis;Fakhreddine O. Karray;Saiedeh N. Razavi
Affiliations:
Pattern Analysis and Machine Intelligence Lab, University of Waterloo, Waterloo, ON, Canada;Pattern Analysis and Machine Intelligence Lab, University of Waterloo, Waterloo, ON, Canada;Pattern Analysis and Machine Intelligence Lab, University of Waterloo, Waterloo, ON, Canada;Department of Civil Engineering, McMaster University, Hamilton, ON, Canada
Venue:
Information Fusion
Year:
2013

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Abstract

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.