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Sensors 2016, 16(7), 1034; doi:10.3390/s16071034

Multi-Sensor Integration to Map Odor Distribution for the Detection of Chemical Sources

Department of Electrical and Computer Engineering, Missouri University of Science and Technology, 301 West 16th Street, Rolla, MO 65409, USA
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Author to whom correspondence should be addressed.
Academic Editors: Xue-Bo Jin, Feng-Bao Yang, Shuli Sun and Hong Wei
Received: 14 March 2016 / Revised: 24 May 2016 / Accepted: 25 May 2016 / Published: 4 July 2016
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)
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Abstract

This paper addresses the problem of mapping odor distribution derived from a chemical source using multi-sensor integration and reasoning system design. Odor localization is the problem of finding the source of an odor or other volatile chemical. Most localization methods require a mobile vehicle to follow an odor plume along its entire path, which is time consuming and may be especially difficult in a cluttered environment. To solve both of the above challenges, this paper proposes a novel algorithm that combines data from odor and anemometer sensors, and combine sensors’ data at different positions. Initially, a multi-sensor integration method, together with the path of airflow was used to map the pattern of odor particle movement. Then, more sensors are introduced at specific regions to determine the probable location of the odor source. Finally, the results of odor source location simulation and a real experiment are presented. View Full-Text
Keywords: sensor integration; odor source detection; odor distribution map sensor integration; odor source detection; odor distribution map
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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Gao, X.; Acar, L. Multi-Sensor Integration to Map Odor Distribution for the Detection of Chemical Sources. Sensors 2016, 16, 1034.

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