Cubature Kalman Particle Filters
- Ganlin Shan, Hai Chen, Bing Ji, Kai Zhang
- Corresponding Author
- Ganlin Shan
Available Online March 2013.
- https://doi.org/10.2991/iccsee.2013.117How to use a DOI?
- particle filter, cubature kalman filter, importance density function
- To resolve the tracking problem of nonlinear/non-Gaussian systems effectively, this paper proposes a novel combination of the cubature kalman filter(CKF) with the particle filters(PF), which is called cubature kalman particle filters(CPF). In this algorithm, CKF is used to generate the importance density function for particle filter. It linearizes the nonlinear functions using statistical linear regression method through a set of Gaussian cubature points. It need not compute the Jacobian matrix and is easy to be implemented. Moreover, it makes efficient use of the latest observation information into system state transition density, thus greatly improving the filter performance. The simulation results are compared against the widely used unscented particle filter(UPF), and have demonstrated that CPF has higher estimation accuracy and less computational load.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Ganlin Shan AU - Hai Chen AU - Bing Ji AU - Kai Zhang PY - 2013/03 DA - 2013/03 TI - Cubature Kalman Particle Filters BT - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/iccsee.2013.117 DO - https://doi.org/10.2991/iccsee.2013.117 ID - Shan2013/03 ER -