Abstract
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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Hong, L., Chen, G. & Chui, C.K. Real-Time Simultaneous Estimation and Decomposition of Random Signals. Multidimensional Systems and Signal Processing 9, 273–289 (1998). https://doi.org/10.1023/A:1008280719917
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DOI: https://doi.org/10.1023/A:1008280719917