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