GENERALIZED FIXED-INTERVAL SMOOTHERS FOR DISCRETE-TIME-SYSTEMS
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WATANABE, K
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TZAFESTAS, SG
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The generalized fixed-interval smoothing problem involves predictive information concerning the final state in addition to a priori information concerning the initial state. Forwards and backwards Markovian models which incorporate the predictive and a priori information, respectively, are constructed by simply using the standard smoothing formulae. The generalized backward- or forward-pass fixed-interval smoothing algorithm and two-filter smoothing algorithm are described in a unified manner. It is then shown that the generalized smoothers include as special cases almost all existing smoothers, e.g., Rauch-Tung-Striebel smoother, Mayne-Fraser two-filter smoother, Wall-Willsky-Sandell two-filter smoother and Desai-Weinert-Yusypchuk smoother. Simulation examples are included to illustrate the characteristics of the present fixed-interval smoothers.
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