1602.01815v2.pdf (3.44 MB)
Download fileExploiting plume structure to decode gas source distance using metal-oxide gas sensors
journal contribution
posted on 2023-06-09, 01:39 authored by Michael SchmukerMichael Schmuker, Viktor Bahr, Ramón HuertaEstimating 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-theshelf 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.
Funding
Biomachinelearning: Bio-inspired Machine Learning for Chemical Sensing (fellow: Michael Schmuker); G1382; EUROPEAN UNION; PIEF-GA-2012-331892
History
Publication status
- Published
File Version
- Accepted version
Journal
Sensors and Actuators B: ChemicalISSN
0925-4005Publisher
ElsevierExternal DOI
Volume
235Page range
636-646Department affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes