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On applying the extended Kalman filter to nonlinear regression models

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2 Author(s)
Robertazzi, T.G. ; Dept. of Electr. Eng., State Univ. of New York, Stony Brook, NY ; Schwartz, S.C.

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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Aerospace and Electronic Systems, IEEE Transactions on  (Volume:25 ,  Issue: 3 )