On Applying the Extended Kalman Filter to Nonlinear Regression Models

T. G. Robertazzi; S. C. Schwartz

doi:10.1109/7.30802

On Applying the Extended Kalman Filter to Nonlinear Regression Models

T. G. Robertazzi
, S. C. Schwartz

Electrical Engineering

Stony Brook University

Princeton University

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

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 The filter cannot always be expected to produce a satisfactory global fit when processing the measurements in the causal order in which they occur. Interestingly, if one can accept off-line processing, it may be possible to obtain such a fit when the measurements are processed in a random order.

Original language	English
Pages (from-to)	433-438
Number of pages	6
Journal	IEEE Transactions on Aerospace and Electronic Systems
Volume	25
Issue number	3
DOIs	https://doi.org/10.1109/7.30802
State	Published - May 1989

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title = "On Applying the Extended Kalman Filter to Nonlinear Regression Models",

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 The filter cannot always be expected to produce a satisfactory global fit when processing the measurements in the causal order in which they occur. Interestingly, if one can accept off-line processing, it may be possible to obtain such a fit when the measurements are processed in a random order.",

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AB - 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 The filter cannot always be expected to produce a satisfactory global fit when processing the measurements in the causal order in which they occur. Interestingly, if one can accept off-line processing, it may be possible to obtain such a fit when the measurements are processed in a random order.

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JO - IEEE Transactions on Aerospace and Electronic Systems

JF - IEEE Transactions on Aerospace and Electronic Systems

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On Applying the Extended Kalman Filter to Nonlinear Regression Models

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