Abstract
The intermittency of the instantaneous concentration of a turbulent chemical plume is a fundamental cue for estimating the chemical source distance using chemical sensors. Such estimate is useful in applications such as environmental monitoring or localization of fugitive gas emissions by mobile robots or sensor networks. However, the inherent low-pass filtering of metal oxide (MOX) gas sensors-typically used in odor-guided robots and dense sensor networks due to their low cost, weight and size-hinders the quantification of concentration intermittency. In this paper, we design a digital differentiator to invert the low-pass dynamics of the sensor response, thus obtaining a much faster signal from which the concentration intermittency can be effectively computed. Using a fast photo-ionization detector as a reference instrument, we demonstrate that the filtered signal is a good approximation of the instantaneous concentration in a real turbulent plume. We then extract transient features from the filtered signal-the so-called “bouts”-to predict the chemical source distance, focusing on the optimization of the filter parameters and the noise threshold to make the predictions robust against changing wind conditions. This represents an advantage over previous bout-based models which require wind measurements-typically taken with expensive and bulky anemometers-to produce accurate predictions. The proposed methodology is demonstrated in a wind tunnel scenario where a MOX sensor is placed at various distances downwind of an emitting chemical source and the wind speed varies in the range 10-34 cm/s. The results demonstrate that models optimized with our methodology can provide accurate source distance predictions at different wind speeds.
Highlights
The detection of intermittent gas patches is key for rapid gas source localization (GSL) in turbulent environments where the ‘‘chemical plume’’ is a collection of gas patches rather than a continuous trail [1], [2]
We found that the mean bout amplitude (MBA) is less sensitive to the value of bthr than the bout frequency (BF), which degrades its behaviour if bthr is either too low or too high
We found that if the threshold is set too low or too high, there is no combination of smoothing factors that produces a monotonically increasing relationship between bout frequency and source distance with enough sensitivity across the studied distance range (Fig. 15)
Summary
The detection of intermittent gas patches is key for rapid gas source localization (GSL) in turbulent environments where the ‘‘chemical plume’’ is a collection of gas patches rather than a continuous trail [1], [2]. Under a constant emission rate and wind speed, a linear regression model relating the bout count during three minutes and the source distance yielded a root mean squared error in crossvalidation (RMSECV) of only 18 cm Based on these results, they claim that the number of bouts detected in a certain time interval is an accurate indicator of the source distance. We propose an optimization method for the parameters of the bout detection algorithm (smoothing factor and noise threshold) that instead of relying on unrealistic assumptions such as constant wind speed or normal distribution of the bout amplitudes, performs a multivariate grid search by varying all parameters simultaneously. We benchmark the results of the optimum bout-based models against the mean, maximum and variance of the response, and against other bout-based features, such as the mean bout amplitude [29]
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