Noise covariance estimation using dual estimation for disturbance storm time index application

• Main Authors: Boonsri Kaewkham-ai, Kasemsak Uthaichana
• Format: Conference Proceeding
• Published: 2018
• Subjects: Computer Science; Engineering
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79952374066&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/50698
• Institution: Chiang Mai University

The disturbance storm time (Dst) index is used for predicting the geomagnetic storm that can affect many systems on earth. The application of the dual unscented Kalman filter (DUKF) to improve the quality of the D st index prediction by simultaneously estimating the process noise covariance is set forth in this paper. In DUKF, two unscented Kalman filters (UKFs) are run in parallel. The UKF applied to a model-based Dst index prediction is so called a state estimator; while the other, a parameter estimator, is for identifying and recursively updating the process noise covariance. The performance comparison between the traditional UKF with fixed constant values of the process noise covariance, and the DUKF are examined. The actual all Dst and the Dst data during the storm (below -80 nT) are used to assess the quality of the predictions. It is found that root mean square error (RMSE) of Dst index prediction using the DUKF is lower than that of the UKF with fixed constant process noise covariances. Specifically, RMSEs of the DUKF are 6.5816 for all Dst and 18.0615 for Dst below -80 nT, whereas, the prediction using a fixed constant process noise covariance yield RMSEs of at least 6.6678 and 19.3954 for all Dst and Dst below -80 nT, respectively. Hence, the DUKF outperforms the traditional UKF with fixed constant process noise covariances in this study. © 2010 IEEE.