Parameter identification of reduced-order electrochemical model simplified by spectral methods and state estimation based on square-root cubature Kalman filter

2022 ◽  
Vol 46 ◽  
pp. 103828
Author(s):  
Qiuyu Ding ◽  
Yujie Wang ◽  
Zonghai Chen
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
State Estimation ◽  
Spectral Methods ◽  
Square Root ◽  
Reduced Order ◽  
Cubature Kalman Filter ◽  
Electrochemical Model

Square Root Cubature Particle Filter

2011 ◽  
Vol 219-220 ◽  
pp. 727-731 ◽  
Author(s):  
Jing Mu ◽  
Yuan Li Cai ◽  
Jun Min Zhang
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
State Estimation ◽  
Numerical Stability ◽  
Current Measurement ◽  
Square Root ◽  
Proposal Distribution ◽  
Unscented Particle Filter ◽  
Cubature Kalman Filter ◽  
Simulation Results

The square root cubature particle filter (SRCPF) uses the square root cubature Kalman filter (SRCKF) for generating the proposal distribution. The SRCPF algorithm is easy to be implemented and has numerical stability. Moreover, the SRCKF based proposal distribution approximates the optimal importance distribution by incorporating the current measurement. Simulation results demonstrate that the SRCPF algorithm has the better performance for state estimation than the generic particle filter (GPF), extended particle filter (EPF) and unscented particle filter (UPF), and its calculation cost decreases largely.

Square-Root Cubature Kalman Filter and Its Application to SLAM of an Mobile Robot

ROBOT ◽  
2013 ◽  
Vol 35 (2) ◽  
pp. 186 ◽  
Author(s):  
Yifei KANG ◽  
Yongduan SONG ◽  
Yu SONG ◽  
Deli YAN ◽  
Danyong LI
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Square Root ◽  
Cubature Kalman Filter

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.

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