2018-03-20T03:58:41Z
http://oa.upm.es/cgi/oai2
oai:oa.upm.es:9622
2016-04-20T17:58:24Z
7374617475733D707562
7375626A656374733D74656C65636F6D756E69636163696F6E6573
7375626A656374733D6D6174656D617469636173
747970653D636F6E666572656E63655F6974656D
Nonlinear filtering update phase via the Single Point Truncated Unscented Kalman filter
García Fernández, Ángel Froilán
Morelande, Mark R.
Grajal de la Fuente, Jesús
Telecomunicaciones
Matemáticas
A fast algorithm to approximate the first two moments of the posterior probability density function (pdf) in nonlinear non-Gaussian Bayesian filtering is proposed. If the pdf of the measurement noise has a bounded support and the measurement function is continuous and bijective, we can use a modified prior pdf that meets Bayes' rule exactly. The central idea of this paper is that a Kalman filter applied to a modified prior distribution can improve the estimate given by the conventional Kalman filter. In practice, bounded support is not required and the modification of the prior is accounted for by adding an extra-point to the set of sigma-points used by the unscented Kalman filter.
E.T.S.I. Telecomunicación (UPM)
(c) Editor/Autor
2011-07
info:eu-repo/semantics/conferenceObject
Ponencia en Congreso o Jornada
14th International Conference on Information Fusion | 14th International Conference on Information Fusion | 5-8 Julio 2011 |
PeerReviewed
application/pdf
eng
info:eu-repo/semantics/openAccess
http://oa.upm.es/9622/