Abstract
This paper presents a new multi-sensor data fusion method based on the combination of wavelet transform (WT) and extended Kalman filter (EKF). Input data are first filtered by a wavelet transform via Daubechies wavelet “db4” functions and the filtered data are then fused based on variance weights in terms of minimum mean square error. The fused data are finally treated by extended Kalman filter for the final state estimati on. The recent data are recursively utilized to apply wavelet transform and extract the variance of the updated data, which makes it suitable to be applied to both static and dynamic systems corrupte d by noisy environments. The method has suitable performance in state estimation in compari son with the other alternative algorithms. A three-tank benchmark system has been adopted to comparatively demonstrate the performance merits of the method compared to a known algorithm in terms of efficiently satisfying signal-tonoise (SNR) and minimum square error (MSE) criteria.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Iranian Journal of Oil and Gas Science and Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
