DocumentCode
2832346
Title
The Denoising Based on the Fusion Method of Kalman Filter and EMD
Author
Zhang Anbing ; Liu xinxia ; Li Xipan
Author_Institution
Hebei Univ. of Eng., Handan, China
fYear
2009
fDate
19-20 Dec. 2009
Firstpage
1
Lastpage
4
Abstract
In this paper, the structure of multi-scale decomposition and reconstruction of empirical mode decomposition (EMD) theory is defined. Through the combination of Kalman and EMD theory, a new Kalman-EMD dynamic deformation data de-noising model is proposed. The model is presented to reduce noise of deformation series. Then, simulated data and real data(i.e.,GPS data)are used to test the method separately. The following conclusions are drawn from these tests: Kalman and EMD method can better mitigate the random errors which hide in periodic signal; After significantly mitigating the influence of multi-path and others errors by the new mode. The results show that the Kalman-EMD model has relative advantage.
Keywords
Kalman filters; feature extraction; image denoising; EMD theory; GPS; Kalman filter; Kalman theory; Kalman-EMD dynamic deformation data de-noising model; empirical mode decomposition; fusion method; multi-scale decomposition; Additive noise; Data mining; Deformable models; Global Positioning System; Kalman filters; Monitoring; Noise reduction; Signal analysis; Signal to noise ratio; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-4994-1
Type
conf
DOI
10.1109/ICIECS.2009.5364204
Filename
5364204
Link To Document