DocumentCode
2699745
Title
An Unscented Transformation for Conditionally Linear Models
Author
Morelande, Mark R. ; Moran, Bill
Author_Institution
Dept. of Electr. & Electron. Eng., Melbourne Univ., Vic., Australia
Volume
3
fYear
2007
fDate
15-20 April 2007
Abstract
A new method of applying the unscented transformation to conditionally linear transformations of Gaussian random variables is proposed. This method exploits the structure of the model to reduce the required number of sigma points. A common application of the unscented transformation is to nonlinear filtering where it used to approximate the moments required in the Kalman filter recursion. The proposed procedure is applied to a nonlinear filtering problem which involves tracking a falling object.
Keywords
Gaussian processes; Kalman filters; filtering theory; nonlinear filters; random processes; Gaussian random variables; Kalman filter recursion; conditionally linear models; linear transformations; nonlinear filtering; sigma points; unscented transformation; Covariance matrix; Filtering; Kalman filters; Laboratories; Mean square error methods; Monte Carlo methods; Nonlinear filters; Nonlinear systems; Random variables; Statistics; Kalman filtering; Nonlinear filters;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
Conference_Location
Honolulu, HI
ISSN
1520-6149
Print_ISBN
1-4244-0727-3
Type
conf
DOI
10.1109/ICASSP.2007.367112
Filename
4217985
Link To Document