Convex Structure-Based Nonlinear State Estimation Using Linear Kalman Filter and Developing an MPC Scheme

2018 ◽  
Author(s):  
Zeinab Echreshavi ◽  
Mohsen Farbood ◽  
Mokhtar Shasadeghi ◽  
Behrouz Safarinejadian
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Convex Structure ◽  
Nonlinear State Estimation ◽  
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.

Nonlinear State Estimation Using Fuzzy Kalman Filter

2006 ◽  
Vol 45 (25) ◽  
pp. 8678-8688 ◽  
Author(s):  
R. Senthil ◽  
K. Janarthanan ◽  
J. Prakash
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Nonlinear State Estimation ◽  
Nonlinear State

scholarly journals Adaptively Random Weighted Cubature Kalman Filter for Nonlinear Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Zhaohui Gao ◽  
Dejun Mu ◽  
Yongmin Zhong ◽  
Chengfan Gu ◽  
Chengcai Ren
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
System Noise ◽  
Nonlinear State Estimation ◽  
Cubature Kalman Filter ◽  
Random Weighting ◽  
Improved Performance ◽  
Noise Statistics ◽  
Estimate System ◽  
Nonlinear State

This paper presents a new adaptive random weighting cubature Kalman filtering method for nonlinear state estimation. This method adopts the concept of random weighting to address the problem that the cubature Kalman filter (CKF) performance is sensitive to system noise. It establishes random weighting theories to estimate system noise statistics and predicted state and measurement together with their associated covariances. Subsequently, it adaptively adjusts the weights of cubature points based on the random weighting estimations to improve the prediction accuracy, thus restraining the disturbances of system noises on state estimation. Simulations and comparison analysis demonstrate the improved performance of the proposed method for nonlinear state estimation.

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