The iterated Kalman filter update as a Gauss-Newton method

1993490 citationsJournal Article

Authors

F.W. Cathey · Defense Systems (United States)

Abstract

It is shown that the iterated Kalman filter (IKF) update is an application of the Gauss-Newton method for approximating a maximum likelihood estimate. An example is presented in which the iterated Kalman filter update and maximum likelihood estimate show correct convergence behavior as the observation becomes more accurate, whereas the extended Kalman filter update does not.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

Topics & Keywords

Target Tracking and Data Fusion in Sensor Networks Statistical and numerical algorithms Scientific Research and Discoveries

Publication Details

Published in: IEEE Transactions on Automatic Control

Volume 38, Issue 2, pp. 294-297

DOI: 10.1109/9.250476

Field-Weighted Citation Impact: 2.90

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The iterated Kalman filter update as a Gauss-Newton method

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Abstract

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