Neural network-aided adaptive unscented Kalman filter for nonlinear state estimation

2006 ◽  
Vol 13 (7) ◽  
pp. 445-448 ◽  
Author(s):  
Ronghui Zhan ◽  
J. Wan
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Nonlinear State Estimation ◽  
Adaptive Unscented Kalman Filter ◽  
Nonlinear State

A Strong Tracking Cubature Kalman Filter for Nonlinear Estimation

2013 ◽  
Vol 313-314 ◽  
pp. 1115-1119
Author(s):  
Yong Qi Wang ◽  
Feng Yang ◽  
Yan Liang ◽  
Quan Pan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Scaling Factor ◽  
Estimation Accuracy ◽  
Nonlinear State Estimation ◽  
Cubature Kalman Filter ◽  
Novel Method ◽  
Simulation Results ◽  
Tracking Filter ◽  
Nonlinear State

In this paper, a novel method based on cubature Kalman filter (CKF) and strong tracking filter (STF) has been proposed for nonlinear state estimation problem. The proposed method is named as strong tracking cubature Kalman filter (STCKF). In the STCKF, a scaling factor derived from STF is added and it can be tuned online to adjust the filtering gain accordingly. Simulation results indicate STCKF outperforms over EKF and CKF in state estimation accuracy.

Sign in / Sign up

Export Citation Format

Share Document