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Chemical Source Localization Fusing Concentration Information in the Presence of Chemical Background Noise

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Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia, Baldiri Reixac 4-8, Barcelona 08028, Spain
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Department of Engineering: Electronics, Universitat de Barcelona, Martí i Franqués 1, Barcelona 08028, Spain
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Department of Computer Science and Industrial Engineering, Universitat de Lleida, Jaume II 69, Lleida 25001, Spain
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper: Pomareda, V.; Marco, S. Chemical Plume Source Localization with Multiple Mobile Sensors using Bayesian Inference under Background Signals. In Proceedings of the International Symposium on Olfaction and Electronic Nose, New York, NY, USA, 2–5 May 2011.
Academic Editor: W. Rudolf Seitz
Sensors 2017, 17(4), 904; https://doi.org/10.3390/s17040904
Received: 14 February 2017 / Revised: 6 April 2017 / Accepted: 11 April 2017 / Published: 20 April 2017
(This article belongs to the Section Chemical Sensors)
We present the estimation of a likelihood map for the location of the source of a chemical plume dispersed under atmospheric turbulence under uniform wind conditions. The main contribution of this work is to extend previous proposals based on Bayesian inference with binary detections to the use of concentration information while at the same time being robust against the presence of background chemical noise. For that, the algorithm builds a background model with robust statistics measurements to assess the posterior probability that a given chemical concentration reading comes from the background or from a source emitting at a distance with a specific release rate. In addition, our algorithm allows multiple mobile gas sensors to be used. Ten realistic simulations and ten real data experiments are used for evaluation purposes. For the simulations, we have supposed that sensors are mounted on cars which do not have among its main tasks navigating toward the source. To collect the real dataset, a special arena with induced wind is built, and an autonomous vehicle equipped with several sensors, including a photo ionization detector (PID) for sensing chemical concentration, is used. Simulation results show that our algorithm, provides a better estimation of the source location even for a low background level that benefits the performance of binary version. The improvement is clear for the synthetic data while for real data the estimation is only slightly better, probably because our exploration arena is not able to provide uniform wind conditions. Finally, an estimation of the computational cost of the algorithmic proposal is presented. View Full-Text
Keywords: machine olfaction; odor robots; chemical sensors; Bayesian inference machine olfaction; odor robots; chemical sensors; Bayesian inference
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Pomareda, V.; Magrans, R.; Jiménez-Soto, J.M.; Martínez, D.; Tresánchez, M.; Burgués, J.; Palacín, J.; Marco, S. Chemical Source Localization Fusing Concentration Information in the Presence of Chemical Background Noise . Sensors 2017, 17, 904.

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