An unscented transformation for conditionally linear models
Mark R Morelande, Bill Moran
2007 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL III, PTS 1-3, PROCEEDINGS | IEEE | Published : 2007
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. © 2007 IEEE.
Awarded by Defense Advanced Research Projects Agency of the US Department of Defense
This work was supported by the Defense Advanced Research Projects Agency of the US Department of Defense and was monitored by the Office of Naval Research under Contract No. N00014-04-C-0437.