Blind drift calibration of sensor networks using signal space projection and Kalman filter

As wireless sensor network (WSN) technologies become mature, an increasing number of large-scale WSN-based long-term monitoring systems are deployed. However, data quality, especially sensor drift, is affecting the trustworthiness of sensor data. In this paper, we proposed an online algorithm to blindly calibrate sensor drift using signal space projection and Kalman filter. By utilizing data correlation among sensors, the proposed method neither requires sensors to be densely deployed nor needs prior knowledge of data models. Simulation results showed the proposed method can detect and calibrate sensor drift successfully. The mean square error of estimated drift is less than 1%, which is more accurate than existing prediction-based methods. The proposed method is also robust to measurement noise, multiplicative drift, and signal subspace estimation error.

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