The Selection of Process Noise Covariance Q and Measurement Noise Covariance R on Kalman Filter

Chao Yi Wei; Shu Jian Ye; Xu Guang Li; Mei Zhi Xie; Feng Yan Yi

doi:10.4028/www.scientific.net/AMM.513-517.4342

Paper Titles

A Parallel Communication Power Supply Based on UC3846
p.4323

The Simulation of a Kind of X-Type Multi-Layer Fabric
p.4328

Development of a Digitized Medium Frequency Electronic Therapeutic Instrument
p.4333

The Circular Seal Identification Method Based on Average Relative Error
p.4338

The Selection of Process Noise Covariance Q and Measurement Noise Covariance R on Kalman Filter
p.4342

Navigation Satellite's Lifetime Model and Optimization Design
p.4346

The Intuitionistic Fuzzy S-Rough Sets Model and Dynamic Transfer Degree
p.4352

Performance Enhancement of 16 Channels Back Haul DWDM OADM Ring Network Using Electronic Dispersion Compensation
p.4357

Business Intelligence: Relationship between Capabilities, Success and Decision Environment
p.4361

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 513-517The Selection of Process Noise Covariance Q and...

The Selection of Process Noise Covariance Q and Measurement Noise Covariance R on Kalman Filter

Abstract:

At first, a seven degree of freedom dynamic model of tractor semi-trailer was established, and a simulation model with the Matlab/Simulink software was established too. Based on the model, a state estimator based on Kalman theory was designed, using easily measured parameters to estimate the parameters that are difficultly measured or have high measurement cost, after that, compared estimated values and simulation measurements and analyzed the influence with changes of process noise covariance Q and measurement noise covariance R.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 513-517)

Pages:

4342-4345

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.513-517.4342

Citation:

Cite this paper

Online since:

February 2014

Authors:

Chao Yi Wei*, Shu Jian Ye, Xu Guang Li, Mei Zhi Xie, Feng Yan Yi

Keywords:

Kalman Filter, Measurement Noise Covariance r, Process Noise Covariance Measurement Noise Covariance q, State Estimation

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Jiang Zhi-kai. Digital filtering and Kalman filtering, [M], Beijing: China Science and Technology Press, (1993).

Google Scholar

[2] Li Hai-qing, Huang Zhi-de. Principle and application of soft measurement technology, [M], Beijing: Chemical Industry Press, (2000).

Google Scholar

[3] Wang Guo-lin, Wei Chao-yi, etc. The establish and test of tractor semi-trailer model, , [J] , Journal of agricultural machinery, 2005, 36(11): P17～P20.

Google Scholar

[4] Wang De-bing, Xie Jin-rong. The research of steering characteristics of tractor semi-trailer, [J], Automotive Engineering, 1988, 10(2): 23~31.

Google Scholar

[5] Greg Welch, Gary Bishop. Introduction to Kalman filter, [D], Department of Computer Science, University of North Carolina at Chapel Hill3, Chapel Hill, NC 27599-3175.

DOI: 10.17140/doj-2-110

Google Scholar