Comparison of Kalman Filter and Wavelet Filter for Denoising

by: S Postalcloglu, K Erkan, ED Bolat

Neural Networks and Brain, 2005. ICNN&B '05. International Conference on, Vol. 2 (2005), pp. 951-954.

View FullText article

IEEE Explore

Reviews [Write a review of this article]

There are no reviews of this article

Find related articles from these CiteULike users

dcastro

Find related articles with these CiteULike tags

filter, kalman, wavelet

Abstract

This paper presents denoising the signal using Wavelet filter and Kalman filter. The noise is zero mean and the variance value is 0,001. Kalman filter removes disturbances or faults from the signal by using initialization and propagation of error covariance statistics. Implementation of Kalman filter is impractical in large scale models as shown for the oscillator system. As an alternative Wavelet filter has been used for the same system. Coiflet 2 which is orthogonal wavelet has been used. Soft thresholding has been applied. Decomposition is performed at level 9. The results of Wavelet filter and Kalman filter are shown. Response of Wavelet filter is better when compared with Kalman filter result.

@inproceedings{citeulike:1915070, abstract = {This paper presents denoising the signal using Wavelet filter and Kalman filter. The noise is zero mean and the variance value is 0,001. Kalman filter removes disturbances or faults from the signal by using initialization and propagation of error covariance statistics. Implementation of Kalman filter is impractical in large scale models as shown for the oscillator system. As an alternative Wavelet filter has been used for the same system. Coiflet 2 which is orthogonal wavelet has been used. Soft thresholding has been applied. Decomposition is performed at level 9. The results of Wavelet filter and Kalman filter are shown. Response of Wavelet filter is better when compared with Kalman filter result.}, author = {Postalcloglu, S. and Erkan, K. and Bolat, E. D. }, booktitle = {Neural Networks and Brain, 2005. ICNN\&B '05. International Conference on}, citeulike-article-id = {1915070}, journal = {Neural Networks and Brain, 2005. ICNN\&B '05. International Conference on}, keywords = {filter, kalman, wavelet}, pages = {951--954}, posted-at = {2007-11-14 17:50:43}, priority = {2}, title = {Comparison of Kalman Filter and Wavelet Filter for Denoising}, url = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=1614777}, volume = {2}, year = {2005} }

RIS record

TY - CONF ID - citeulike:1915070 L3 - citeulike-article-id:1915070 TI - Comparison of Kalman Filter and Wavelet Filter for Denoising T2 - Neural Networks and Brain, 2005. ICNN&B '05. International Conference on JF - Neural Networks and Brain, 2005. ICNN&B '05. International Conference on VL - 2 SP - 951 EP - 954 N2 - This paper presents denoising the signal using Wavelet filter and Kalman filter. The noise is zero mean and the variance value is 0,001. Kalman filter removes disturbances or faults from the signal by using initialization and propagation of error covariance statistics. Implementation of Kalman filter is impractical in large scale models as shown for the oscillator system. As an alternative Wavelet filter has been used for the same system. Coiflet 2 which is orthogonal wavelet has been used. Soft thresholding has been applied. Decomposition is performed at level 9. The results of Wavelet filter and Kalman filter are shown. Response of Wavelet filter is better when compared with Kalman filter result. KW - filter KW - kalman KW - wavelet AU - Postalcloglu, S AU - Erkan, K AU - Bolat, ED PY - 2005/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1614777 ER -

RIS BibTeX