Cubature Kalman Particle Filters

DOI

10.2991/iccsee.2013.117How to use a DOI?

Keywords

particle filter, cubature kalman filter, importance density function

Abstract

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.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

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 (ICCSEE 2013)
PB  - Atlantis Press
SP  - 456
EP  - 460
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.117
DO  - 10.2991/iccsee.2013.117
ID  - Shan2013/03
ER  -