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Proceedings Paper

New nonlinear iterated filter with applications to target tracking
Author(s): R. Louis Bellaire; Edward W. Kamen; Serena M. Zabin
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Paper Abstract

The two most popular solutions to the nonlinear filtering problem are the Extended Kalman filter (EKF) and the Iterated Extended Kalman filter (IKF). Both are sub-optimal algorithms which employ a first-order, Taylor-series approximation to adapt the linear, Kalman filter to the nonlinear problem. While the Taylor-series approximation makes an implementation realizable, its accuracy depends heavily on the stability of the Jacobian matrix. In practice the Jacobian matrix is often numerically unstable, resulting in filter divergence and, in the case of the IKF, slowed or even non-convergence of the iterates. This paper identifies inadequacies inherent to the EKF and IKF, discusses their detrimental effect on performance, and then proposes a solution which uses the Julier et al. time update and a new iterated procedure for computing the measurement update. The resulting new iterated filter is believed to be a robust alternative to prevailing methods. Examples involving target tracking are considered.

Paper Details

Date Published: 1 September 1995
PDF: 12 pages
Proc. SPIE 2561, Signal and Data Processing of Small Targets 1995, (1 September 1995); doi: 10.1117/12.217701
Show Author Affiliations
R. Louis Bellaire, Georgia Institute of Technology (United States)
Edward W. Kamen, Georgia Institute of Technology (United States)
Serena M. Zabin, Georgia Institute of Technology (United States)

Published in SPIE Proceedings Vol. 2561:
Signal and Data Processing of Small Targets 1995
Oliver E. Drummond, Editor(s)

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