Hybrid estimation algorithms

The optimal mean-square estimate of the state of a hybrid system is difficult to determine because the equations of state evolution are nonlinear and non-Gaussian. When there is a direct, albeit noisy, measurement of the modal state, it is possible to derive a useful approximation to the optimal est...

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Veröffentlicht in:	Journal of optimization theory and applications 1994-04, Vol.81 (1), p.143-167
Hauptverfasser:	SWORDER, D. D, VOJAK, R
Format:	Artikel
Sprache:	eng
Schlagworte:	Applied sciences Exact sciences and technology Operational research and scientific management Operational research. Management science Target location and tracking
Online-Zugang:	Volltext
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Beschreibung
Zusammenfassung:	The optimal mean-square estimate of the state of a hybrid system is difficult to determine because the equations of state evolution are nonlinear and non-Gaussian. When there is a direct, albeit noisy, measurement of the modal state, it is possible to derive a useful approximation to the optimal estimator. This simplified algorithm is tested on a target tracking problem, and is seen to be superior to the conventional extended Kalman filter. (Author)
ISSN:	0022-3239 1573-2878
DOI:	10.1007/BF02190317