Comparison of Kalman filter and Wavelet filter for denoising

Postalcioglu S., Erkan K., Bolat E.

International Conference on Neural Networks and Brain (ICNN&B 2005), Beijing, Çin, 13 - 15 Ekim 2005, ss.951-954 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Basıldığı Şehir: Beijing
  • Basıldığı Ülke: Çin
  • Sayfa Sayıları: ss.951-954


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.