TY - JOUR
T1 - Exploiting plume structure to decode gas source distance using metal-oxide gas sensors
AU - Schmuker, Michael
AU - Bahr, Viktor
AU - Huerta, Ramón
N1 - This document is the Accepted Manuscript version of the following article: Michael Schmuker, Viktor Bahr, & Ramon Huerta, ‘Exploiting plume structure to decode gas source distance using metal-oxide gas sensors’, Sensors and Actuators B: Chemical, Vol. 235: 636-646, November 2016, doi: http://dx.doi.org/10.1016/j.snb.2016.05.098.
This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
Published by Elsevier.
PY - 2016/11/1
Y1 - 2016/11/1
N2 - Estimating the distance of a gas source is important in many applications of chemical sensing, like e.g. environmental monitoring, or chemically-guided robot navigation. If an estimation of the gas concentration at the source is available, source proximity can be estimated from the time-averaged gas concentration at the sensing site. However, in turbulent environments, where fast concentration fluctuations dominate, comparably long measurements are required to obtain a reliable estimate. A lesser known feature that can be exploited for distance estimation in a turbulent environment lies in the relationship between source proximity and the temporal variance of the local gas concentration - the farther the source, the more intermittent are gas encounters. However, exploiting this feature requires measurement of changes in gas concentration on a comparably fast time scale, that have up to now only been achieved using photo-ionisation detectors. Here, we demonstrate that by appropriate signal processing, off-the-shelf metal-oxide sensors are capable of extracting rapidly fluctuating features of gas plumes that strongly correlate with source distance. We show that with a straightforward analysis method it is possible to decode events of large, consistent changes in the measured signal, so-called 'bouts'. The frequency of these bouts predicts the distance of a gas source in wind-tunnel experiments with good accuracy. In addition, we found that the variance of bout counts indicates cross-wind offset to the centreline of the gas plume. Our results offer an alternative approach to estimating gas source proximity that is largely independent of gas concentration, using off-the-shelf metal-oxide sensors. The analysis method we employ demands very few computational resources and is suitable for low-power microcontrollers.
AB - Estimating the distance of a gas source is important in many applications of chemical sensing, like e.g. environmental monitoring, or chemically-guided robot navigation. If an estimation of the gas concentration at the source is available, source proximity can be estimated from the time-averaged gas concentration at the sensing site. However, in turbulent environments, where fast concentration fluctuations dominate, comparably long measurements are required to obtain a reliable estimate. A lesser known feature that can be exploited for distance estimation in a turbulent environment lies in the relationship between source proximity and the temporal variance of the local gas concentration - the farther the source, the more intermittent are gas encounters. However, exploiting this feature requires measurement of changes in gas concentration on a comparably fast time scale, that have up to now only been achieved using photo-ionisation detectors. Here, we demonstrate that by appropriate signal processing, off-the-shelf metal-oxide sensors are capable of extracting rapidly fluctuating features of gas plumes that strongly correlate with source distance. We show that with a straightforward analysis method it is possible to decode events of large, consistent changes in the measured signal, so-called 'bouts'. The frequency of these bouts predicts the distance of a gas source in wind-tunnel experiments with good accuracy. In addition, we found that the variance of bout counts indicates cross-wind offset to the centreline of the gas plume. Our results offer an alternative approach to estimating gas source proximity that is largely independent of gas concentration, using off-the-shelf metal-oxide sensors. The analysis method we employ demands very few computational resources and is suitable for low-power microcontrollers.
KW - Gas plumes
KW - Metal-oxide sensors
KW - Signal processing
KW - Source proximity estimation
KW - Turbulence
UR - http://www.scopus.com/inward/record.url?scp=84973530106&partnerID=8YFLogxK
U2 - 10.1016/j.snb.2016.05.098
DO - 10.1016/j.snb.2016.05.098
M3 - Article
AN - SCOPUS:84973530106
SN - 0925-4005
VL - 235
SP - 636
EP - 646
JO - Sensors and Actuators B: Chemical
JF - Sensors and Actuators B: Chemical
ER -