Nonlinear filtering update phase via the Single Point Truncated Unscented Kalman filter

García Fernández, Ángel Froilán and Morelande, Mark R. and Grajal de la Fuente, Jesús (2011). Nonlinear filtering update phase via the Single Point Truncated Unscented Kalman filter. In: "14th International Conference on Information Fusion", 5-8 Julio 2011.

Description

Title: Nonlinear filtering update phase via the Single Point Truncated Unscented Kalman filter
Author/s:
  • García Fernández, Ángel Froilán
  • Morelande, Mark R.
  • Grajal de la Fuente, Jesús
Item Type: Presentation at Congress or Conference (Article)
Event Title: 14th International Conference on Information Fusion
Event Dates: 5-8 Julio 2011
Title of Book: 14th International Conference on Information Fusion
Date: July 2011
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Otro
Creative Commons Licenses: None

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Abstract

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.

More information

Item ID: 9622
DC Identifier: http://oa.upm.es/9622/
OAI Identifier: oai:oa.upm.es:9622
Deposited by: Ángel Froilán García Fernández
Deposited on: 10 Nov 2011 13:45
Last Modified: 20 Apr 2016 17:58
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