T. Lefebvre, H. Bruyninckx, and J. De Schutter. Kalman Filters for Nonlinear Systems: A Comparison of Performance, October 2001. Internal Report 01R033, Department of Mechanical Engineering, Katholieke Universiteit Leuven, Belgium. Home/Search Document Details and Download
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Bayesian Hybrid Model-State Estimation Applied To.. - Gadeyne, Lefebvre.. (2005) Self-citation (Lefebvre Bruyninckx) (Correct)
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T. Lefebvre, H. Bruyninckx, and J. De Schutter. Kalman filters for nonlinear systems: a comparison of performance. Internal report 01R033, KULeuven, 2001. http://people.mech.kuleuven.ac.be/~tlefebvr/publicatie.htm.
Construction of a geometric 3-D model from sensor.. - Peter Slaets Johan (2004) Self-citation (Lefebvre Bruyninckx De schutter) (Correct)
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T. Lefebvre, H. Bruyninckx, and J. De Schutter, "Kalman filters for nonlinear systems: a comparison of performance," The International Journal of Control, 2004, (in press).
A Rigorous Bayesian Approach to Simultaneous Model.. - Gadeyne Lefebvre.. (2003) Self-citation (Lefebvre Bruyninckx De schutter) (Correct)
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T. Lefebvre, H. Bruyninckx, and J. De Schutter. Kalman filters for nonlinear systems: a comparison of performance. Internal report 01R033, KULeuven, 2001. http://people.mech.kuleuven.ac.be/~tlefebvr/ publicatie.htm.
A Rigorous Bayesian Approach to Simultaneous Model .. - Gadeyne.. (2003) Self-citation (Lefebvre Bruyninckx De schutter) (Correct)
....two CFs are probable: the same CF as before the inconsistency detection (false alarm) and the next CF in the task plan. 15, 3] estimate the geometrical parameters with Kalman Filters and detect inconsistency by a SNIS test [2] Several Bayesian techniques [8] such as Kalman Filter variants [13, 14] and Monte Carlo methods [6, 7] have been used for the estimation of the geometrical parameters, assuming the CF is known. In [17] a first step towards a multiple model posterior is made. Debus et al. 5] present similar results with de terministic multiple model estimation based on pose ....
T. Lefebvre, H. Bruyninckx, and J. De Schutter. Kalman filters for nonlinear systems: a comparison of performance. Internal report 01R033, KULeuven, 2001. http://people.mech.kuleuven.ac.be/~tlefebvr/ publicatie.htm.
A Non-minimal State Kalman Filter for Nonlinear.. - Lefebvre.. (2003) Self-citation (Lefebvre Bruyninckx De schutter) (Correct)
....Kalman Filters for nonlinear systems linearize the process and measurement functions with respect to the (original) state variables. Consistency of the estimates is obtained by adding extra process and measurement uncertainty on the linearized functions, representing the linearization errors [2]. This extra uncertainty, however, corresponds to a loss of information. The loss of information is permanent as the filters do not revise the linearizations at later time steps. Hence, the filter results are only as informative as the ones obtained by an efficient nonlinear estimator if the ....
....at later time steps. Hence, the filter results are only as informative as the ones obtained by an efficient nonlinear estimator if the linearization errors are negligible. This is for instance the case for the Iterated Extended Kalman Filter (IEKF) measurement update for fully observed systems [2]. For static systems, a batch processing of all past measurements solves the problem of permanent information loss. However, due to its increasing complexity this is not a possible solution for on line estimation. The Nonminimal State Kalman Filter (NMSKF) described in this Fully observed in ....
[Article contains additional citation context not shown here]
T. Lefebvre, H. Bruyninckx, and J. De Schutter, "Kalman Filters for nonlinear systems: a comparison of performance," Internal report 01R033, Dept. of Mech. Engineering, K.U.Leuven, Belgium, 2001.
Comment on "A new nonlinear iterated filter with.. - Lefebvre.. Self-citation (Lefebvre Bruyninckx De schutter) (Correct)
....to Internal Report 01R033 Comment on A new nonlinear iterated filter with applications to target tracking Tine Lefebvre, H. Bruyninckx and J. De Schutter Department of Mechanical Engineering Katholieke Universiteit Leuven, Belgium E mail: Tine.Lefebvre mech.kuleuven.ac. be May 2002 Report [1] compares the Extended Kalman Filter [2, 3, 4] the Iterated Extended Kalman Filter, IEKF, 2, 3, 4] and the Linear Regression Kalman Filter [5] e.g. the Unscented Kalman Filter, UKF, 6, 7, 8] on (i) consistency and (ii) information content of their results (estimates and covariance ....
.... IEKF, 2, 3, 4] and the Linear Regression Kalman Filter [5] e.g. the Unscented Kalman Filter, UKF, 6, 7, 8] on (i) consistency and (ii) information content of their results (estimates and covariance matrices) The nonlinear filter proposed by Bellaire et al. in [9] is not discussed in report [1]. This addition to report [1] gives some comments on the consistency and information content of Bellaire s filter. 1 Process update The process update of Bellaire s filter is the UKF update which yields consistent and informative results ( 1] section 3.2) 2 Measurement update For the ....
[Article contains additional citation context not shown here]
T. Lefebvre, H. Bruyninckx, and J. De Schutter, "Kalman filters for nonlinear systems: a comparison of performance," Internal Report 01R033, Department of Mechanical Engineering, Katholieke Universiteit Leuven, Belgium, October 2001.
A Survey of Maneuvering Target Tracking: Approximation.. - Li, Jilkov (2004) (Correct)
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T. Lefebvre, H. Bruyninckx, and J. De Schutter. Kalman Filters for Nonlinear Systems: A Comparison of Performance, October 2001. Internal Report 01R033, Department of Mechanical Engineering, Katholieke Universiteit Leuven, Belgium.
Unscented Kalman filtering for nonlinear - Structural Dynamics Stefano (Correct)
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T. Lefebvre, H. Bruyninckx, and J. De Schutter. Kalman filters for nonlinear systems: a comparison of performance. International Journal of Control, 77:639--653, 2004.
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