What is it about?
The problem of state estimation of nonlinear systems based on CKF is widely concerned, while the inaccurate noise has an impact on the accuracy of state estimation. In this paper, simulation examples are given to verify the effectiveness of the improved algorithm.
Featured Image
Photo by Markus Spiske on Unsplash
Why is it important?
Our research results show that the improved cubature Kalman filter algorithm has high filtering accuracy, and the improved noise covariance matrix estimator can play a good role in the multi-demension nonlinear system.
Perspectives
I hope that through this article, more people can pay attention to the state estimation of nonlinear systems. It is a very happy thing for scholars to study, communicate and make progress together.
Jun Zhu
Henan Polytechnic University
Read the Original
This page is a summary of: State estimation based on improved cubature Kalman filter algorithm, IET Science Measurement & Technology, December 2019, the Institution of Engineering and Technology (the IET), DOI: 10.1049/iet-smt.2019.0363.
You can read the full text:
Contributors
The following have contributed to this page
