| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 411155 | Neurocomputing | 2009 | 5 Pages |
Abstract
The Kalman filter (KF) models the propagation of uncertainty for a dynamic system where the noise distribution is Gaussian. This letter mainly explores the property of uncertainty propagation in the case where the noise property is unknown. The Dirichlet process mixture (DPM) model is employed to construct a general estimator of the noise distribution. Under the framework of nonparametric Bayes, we use the block sampling and KF techniques to approximate the posterior distribution of the noise. The simulation experiment shows that the proposed algorithm is efficient.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Juyang Lei, Ke Huang, Haixiang Xu, Xizhi Shi,