Baseline drift caused by slowly changing environment and other instability factors affects significantly the performance of gas sensors, resulting in reduced accuracy of gas classification and quantification of the electronic nose. In this work, a two-stage method is proposed for real-time sensor baseline drift compensation based on estimation theory and piecewise linear approximation. In the first stage, the linear information from the baseline before exposure is extracted for prediction. The second stage continuously predicts changing linear parameters during exposure by combining temperature change information and time series information, and then the baseline drift is compensated by subtracting the predicted baseline from the real sensor response. The proposed method is compared to three efficient algorithms and the experiments are conducted towards two simulated datasets and two surface acoustic wave sensor datasets. The experimental results prove the effectiveness of the proposed algorithm. Moreover, the proposed method can recover the true response signal under different ambient temperatures in real-time, which can guide the future design of low-power and low-cost rapid detection systems.

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