Exploiting plume structure to decode gas source distance using metal-oxide gas sensors

Schmuker, Michael, Bahr, Viktor and Huerta, Ramón (2016) Exploiting plume structure to decode gas source distance using metal-oxide gas sensors. Sensors and Actuators B: Chemical, 235. pp. 636-646. ISSN 0925-4005

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Abstract

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-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.

Item Type: Article
Keywords: Metal-oxide sensors, Turbulence, Gas plumes, Signal processing, Source proximity estimation
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > Q Science (General)
Depositing User: michael Schmuker
Date Deposited: 14 Jun 2016 09:19
Last Modified: 08 Mar 2017 05:02
URI: http://sro.sussex.ac.uk/id/eprint/61468

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Project NameSussex Project NumberFunderFunder Ref
Biomachinelearning: Bio-inspired Machine Learning for Chemical Sensing (fellow: Michael Schmuker)G1382EUROPEAN UNIONPIEF-GA-2012-331892