Real-Time Simultaneous Estimation and Decomposition of Random Signals
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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.
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