Real-time simultaneous estimation and decomposition of random signals

In this paper, an efficient algorithm is derived for multiresolutional estimation and decomposition of noisy random signals. This algorithm performs in real-time the estimation and decomposition simultaneously, using the discrete wavelet transform implemented by a filter bank. Although the algorithm is developed based on the standard Kalman filtering scheme, the nature of blockwise Filtering results in a smoothing-equivalent effect. However, the interpolated filtering produces decomposed estimate output in real-time.