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Online Drift Compensation For Chemical Sensors Using Estimation Theory, Thomas H. Wenzel, Arnold Kweku Mensah-Brown, Fabien Josse, Edwin E. Yaz

Electrical and Computer Engineering Faculty Research and Publications

Sensor drift from slowly changing environmental conditions and other instabilities can greatly degrade a chemical sensor's performance, resulting in poor identification and analyte quantification. In the present work, estimation theory (i.e., various forms of the Kalman filter) is used for online compensation of baseline drift in the response of chemical sensors. Two different cases, which depend on the knowledge of the characteristics of the sensor system, are studied. First, an unknown input is considered, which represents the practical case of analyte detection and quantification. Then, the more general case, in which the sensor parameters and the input are both unknown, …