Details

Autor(en) / Beteiligte

• Julier, S.J.
• Uhlmann, J.K.

Titel

Reduced sigma point filters for the propagation of means and covariances through nonlinear transformations

Ist Teil von

• Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301), 2002, Vol.2, p.887-892 vol.2

Ort / Verlag

Piscataway NJ: IEEE

Erscheinungsjahr

2002

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• The Unscented Transform (UT) approximates the result of applying a specified nonlinear transformation to a given mean and covariance estimate. The UT works by constructing a set of points, referred to as sigma points, which has the same known statistics, e.g., first and second and possibly higher moments, as the given estimate. The given nonlinear transformation Is applied to the set, and the unscented estimate is obtained by computing the statistics of the transformed set of sigma points. For example, the mean and covariance of the transformed set approximates the nonlinear transformation of the original mean and covariance estimate. The computational efficiency of the UT therefore depends on the number of sigma points required to capture the known statistics of the original estimate. In this paper we examine methods for minimizing the number of sigma points for real-time control, estimation, and filtering applications. We demonstrate results in a 3D localization example.