Inherent MEMS sensor array variability reduction using robust regression

Gas sensor arrays have proven to be an effective and low-cost solution for measurement of pollutants in large area wherein they can be deployed in the form of network mesh. However, while using multiple identical sensor arrays, the inherent sensor array variability associated with these sensor array needs to be tackled prior to its deployment. In the present work MEMS based gas sensors are used to form three identical gas sensor arrays. Out of the three sensor arrays one was identified as master sensor array and other two as slave sensor arrays. M-estimators were used to develop a robust regression model to map the responses of slave onto master using various ethanol concentrations. The results of the same are presented in this paper.