Chemical source localization fusing concentration information in the presence of chemical background noise

dc.contributor.authorPomareda, Víctor
dc.contributor.authorMagrans, Rudys
dc.contributor.authorJiménez Soto, Juan M.
dc.contributor.authorMartínez Lacasa, Daniel
dc.contributor.authorTresánchez Ribes, Marcel
dc.contributor.authorBurgués, Javier
dc.contributor.authorPalacín Roca, Jordi
dc.contributor.authorMarco, Santiago
dc.date.accessioned2017-05-29T10:10:46Z
dc.date.available2017-05-29T10:10:46Z
dc.date.issued2017
dc.description.abstractWe 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.ca_ES
dc.description.sponsorshipThe research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under Grant Agreement No. 217925: LOTUS: Localization of Threat Substances in an Urban Society. This work was partially funded by the Spanish MINECO program, under grants TEC2011-26143 (SMART-IMS), TEC2014-59229-R (SIGVOL), and BES-2015-071698 (SEVERO-OCHOA). The Signal and Information Processing for Sensor Systems group is a consolidated Grup de Recerca de la Generalitat de Catalunya and has support from the Departament d’Universitats, Recerca i Societat de la Informació de la Generalitat de Catalunya (expedient 2014-SGR-1445). This work has received support from the Comissionat per a Universitats i Recerca del DIUE de la Generalitat de Catalunya and the European Social Fund (ESF). Additional financial support has been provided by the Institut de Bioenginyeria de Catalunya (IBEC). IBEC is a member of the CERCA Programme/Generalitat de Catalunya.
dc.identifier.doihttps://doi.org/10.3390/s17040904
dc.identifier.idgrec025954
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10459.1/59724
dc.language.isoengca_ES
dc.publisherMDPIca_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN//TEC2011-26143/ES/PROCESADO DE SEÑAL PARA ESPECTROSCOPIA DE MOVILIDAD DE IONES: ANALISIS DE FLUIDOS BIOMEDICOS Y DETECCION DE SUSTANCIAS TOXICAS/
dc.relationinfo:eu-repo/grantAgreement/MINECO//TEC2014-59229-R/ES/MEJORA DE LA SEÑAL PARA INSTRUMENTACION QUIMICA: APLICACIONES EN METABOLOMICA DE VOLATILES Y EN OLFACCION PARA ROBOTS MOVILES/
dc.relation.isformatofReproducció del document publicat a https://doi.org/10.3390/s17040904ca_ES
dc.relation.ispartofSensors, 2017, vol. 17, núm. 904, p. 1-23ca_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/FP7/217925
dc.rightscc-by (c) Pomareda et al., 2017ca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectMachine olfactionca_ES
dc.subjectOdor robotsca_ES
dc.subjectChemical sensorsca_ES
dc.subjectBayesian inferenceca_ES
dc.titleChemical source localization fusing concentration information in the presence of chemical background noiseca_ES
dc.typearticleca_ES
dc.type.versionpublishedVersionca_ES
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