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Hybrid Transition Density Approximation for Efficient Recursive Prediction of Nonlinear Dynamic Systems

Huber, Marco F.; Hanebeck, Uwe D.


For several tasks in sensor networks, such as localization, information fusion, or sensor scheduling, Bayesian estimation is of paramount importance. Due to the limited computational and memory resources of the nodes in a sensor network, evaluation of the prediction step of the Bayesian estimator has to be performed very efficiently. An exact and closed-form representation of the predicted probability density function of the system state is typically impossible to obtain, since exactly solving the prediction step for nonlinear discrete-time dynamic systems in closed form is unfeasible. Assuming additive noise, we propose an accurate approximation of the predicted density, that can be calculated efficiently by optimally approximating the transition density using a hybrid density. A hybrid density consists of two different density types: Dirac delta functions that cover the domain of the current density of the system state, and another density type, e.g. Gaussian densities, that cover the domain of the predicted density. The freely selectable, second density type of the hybrid density depends strongly on the noise affecting the nonlinear system. ... mehr

Volltext §
DOI: 10.5445/IR/1000034841
DOI: 10.1145/1236360.1236398
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Informatik – Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2007
Sprache Englisch
Identifikator urn:nbn:de:swb:90-348416
KITopen-ID: 1000034841
Erschienen in International Conference on Information Processing in Sensor Networks (IPSN 2007), Cambridge, Massachusetts, USA, April 25 - 27, 2007
Verlag Cambridge
Seiten 283-292
Nachgewiesen in Dimensions
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