@inproceedings{dae075e62913402086d06200f4cdc270,
title = "Extension of the sparse grid quadrature filter",
abstract = "The sparse grid quadrature filter is a point-based Gaussian filter in which expectations of nonlinear functions of Gaussian random vectors are computed using the sparse grid quadrature. The sparse grid quadrature can be considered a generalization of the Unscented Transform in that the Unscented Transform is equivalent to the level-2 sparse grid quadrature. A novel extension of the sparse grid quadrature filter is presented that directly transforms the points in time update and measurement update to eliminate repeated covariance decomposition based point generation and to relax the Gaussian assumption inherent in the sparse grid quadrature filter as well as the sigma-point filters. A tracking example is presented to demonstrate the performance of the novel filter.",
author = "Yang Cheng and Yang Tian and Crassidis, \{John L.\}",
note = "Publisher Copyright: {\textcopyright} 2014 International Society of Information Fusion.; 17th International Conference on Information Fusion, FUSION 2014 ; Conference date: 07-07-2014 Through 10-07-2014",
year = "2014",
month = oct,
day = "3",
language = "English",
series = "FUSION 2014 - 17th International Conference on Information Fusion",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "FUSION 2014 - 17th International Conference on Information Fusion",
address = "United States",
}