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Discrete Time: Filtering Algorithms
2015In this chapter, we describe various algorithms for the filtering problem. Recall from Section 2.4 that filtering refers to the sequential update of the probability distribution on the state given the data, as data is acquired, and that \(Y _{j} =\{ y_{\ell}\}_{\ell=1}^{j}\) denotes the data accumulated up to time j. The filtering update from time j to
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Continuous Time: Filtering Algorithms
2015In this chapter, we describe various algorithms for determination of the filtering distribution μ t in continuous time. We begin in Section 8.1 with the Kalman–Bucy filter, which provides an exact algorithm for linear problems. Since the filtering distribution is Gaussian in this case, the distribution is entirely characterized by the mean and ...
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Thermochimica Acta, 1992
Abstract A method based on the Wiener approach has been presented for filtering calorimetric signals with the technique of windowing the power spectrum in the frequency domain instead of an abrupt rectangular cut-off of the noise frequencies. This paper presents a time-domain approach which, for the ideal case of additive noise-corrupted signals ...
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Abstract A method based on the Wiener approach has been presented for filtering calorimetric signals with the technique of windowing the power spectrum in the frequency domain instead of an abrupt rectangular cut-off of the noise frequencies. This paper presents a time-domain approach which, for the ideal case of additive noise-corrupted signals ...
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A higher-order Robert-Asselin type time filter
Journal of Computational Physics, 2014Yong Li, C. Trenchea
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