Please use this identifier to cite or link to this item:
Title: Chemical source localization fusing concentration information in the presence of chemical background noise
Author: Pomareda Sesé, Victor
Magrans, Rudys
Jiménez-Soto, Juan M.
Martínez, Dani
Tresánchez, Marcel
Burgués, Javier
Palacín Roca, Jordi
Marco Colás, Santiago
Keywords: Detectors de gasos
Estadística bayesiana
Gas detectors
Bayesian statistical decision
Issue Date: 20-Apr-2017
Publisher: MDPI
Abstract: 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
Note: Reproducció del document publicat a:
It is part of: Sensors, 2017, vol. 17, num. 4, p. 904-927
Related resource:
ISSN: 1424-8220
Appears in Collections:Articles publicats en revistes (Electrònica)

Files in This Item:
File Description SizeFormat 
671056.pdf3.2 MBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons