Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1713256 | Journal of Systems Engineering and Electronics | 2006 | 5 Pages |
Abstract
Extended Kalman filter (EKF) is one of the most widely used methods for nonlinear system estimation. A new filtering algorithm, called particle filtering (PF) is introduced. PF can yield better performance than that of EKF, because PF does not involve the linearization approximating to nonlinear systems, that is required by the EKF. PF has been shown to be a superior alternative to the EKF in a variety of applications. The base idea of PF is the approximation of relevant probability distributions using the concepts of sequential importance sampling and approximation of probability distributions using a set of discrete random samples with associated weights. PF methods still need to be improved in the aspects of accuracy and calculating speed.
Related Topics
Physical Sciences and Engineering
Engineering
Control and Systems Engineering
Authors
Weiming Zhang, Gang Du, Shan Zhang, Yanhua Zhang,