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
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;
Conference_Titel :
Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4994-1
DOI :
10.1109/ICIECS.2009.5364204