An efficient tuning framework for Kalman filter parameter optimization using design of experiments and genetic algorithms

Alan Zhang; Mohamed Maher Atia

doi:10.1002/navi.399

Article Information

vol. 67 no. 4 775-793

DOI

https://doi.org/10.1002/navi.399

Published By

Institute of Navigation

Print ISSN

0028-1522

Online ISSN

2161-4296

History

Received November 6, 2019
Revision received July 3, 2020
Accepted September 7, 2020
Published online October 22, 2020.

Copyright © 2020 Institute of Navigation This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Author Information

Alan Zhang¹ ⇑ and
Mohamed Maher Atia²

¹Department of Systems and Computer Engineering, Carleton University Embedded and Multi-sensor Systems Lab, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada
²Department of Systems and Computer Engineering, Carleton University Embedded and Multi-sensor Systems Lab, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada

Correspondence
Alan Zhang, Department of Systems and Computer Engineering, Carleton University, Carleton University Embedded and Multi-sensor Systems Lab, 1125 Colonel By Drive, Ottawa, ON, Canada, K1S 5B6. Email: Alanzhang4{at}cmail.carleton.ca

View Full Text