A novel approach for gas discrimination in natural environments with Open Sampling Systems

This work presents a gas discrimination approach for Open Sampling Systems (OSS), composed of non-specific metal oxide sensors only. In an OSS, as used on robots or in sensor networks, the sensors are exposed to the dynamics of the environment and thus, most of the data corresponds to highly diluted samples while high concentrations are sparse. In addition, a positive correlation between class separability and concentration level can be observed. The proposed approach computes the class posteriors by coupling the pairwise probabilities between the compounds to a confidence model based on an estimation of the concentration. In this way a rejection posterior, analogous to the detection limit of the human nose, is learned. Evaluation was conducted in indoor and outdoor sites, with an OSS equipped robot, in the presence of two gases. The results show that the proposed approach achieves a high classification performance with a low sensitivity to the selection of meta parameters.