BLIND CALIBRATION METHOD FOR WIRELESS SENSOR NETWORK DATA DRIFT

摘要

Disclosed is a blind calibration method for a wireless sensor network data drift, falling within the technical field of wireless sensor networks. In the method, a data drift of a sensor node is calibrated by means of a method of combining a constraint-based extreme learning machine and a Kalman filter. In the present invention, a node measured value is firstly preprocessed; then, mathematical modeling is carried out on the space-time correlation between a node to be calibrated and a neighboring node by using a constraint extreme learning machine (CELM), so as to obtain a predicted value of a target node; and finally, the predicted value and a measured value of the target node are fed back to a Kalman filter to track and calibrate a data drift thereof. The average mean error between a calibrated value obtained by means of the method and a real value is extremely small, indicating that the method has extremely high model fitting precision and takes less training time. The algorithm complexity is reduced, and the reliability of WSN data is improved.