On applying the extended Kalman filter to nonlinear regressionmodels |
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Authors: | Robertazzi T.G. Schwartz S.C. |
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Affiliation: | Dept. of Electr. Eng., State Univ. of New York, Stony Brook, NY; |
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Abstract: | In using an extended Kalman filter to estimate the parameters of a nonlinear regression model, the order in which the measurements are processed can be important, as the filter cannot always be expected to produce a satisfactory global fit when processing the measurements in the causal order in which they occur. To obtain a better fit, the possibility is explored of using a sequential state estimator in an offline mode to process the measurements in a random order rather than in the causal order in which they occur |
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