
Research Article
LS-SVM Assisted Multi-rate INS UWB Integrated Indoor Quadrotor Localization Using Kalman Filter
@INPROCEEDINGS{10.1007/978-3-031-50577-5_2, author={Dong Wan and Yuan Xu and Chenxi Li and Yide Zhang}, title={LS-SVM Assisted Multi-rate INS UWB Integrated Indoor Quadrotor Localization Using Kalman Filter}, proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part III}, proceedings_a={ICMTEL PART 3}, year={2024}, month={2}, keywords={INS/UWB least squares support vector machine extended Kalman filter}, doi={10.1007/978-3-031-50577-5_2} }
- Dong Wan
Yuan Xu
Chenxi Li
Yide Zhang
Year: 2024
LS-SVM Assisted Multi-rate INS UWB Integrated Indoor Quadrotor Localization Using Kalman Filter
ICMTEL PART 3
Springer
DOI: 10.1007/978-3-031-50577-5_2
Abstract
This paper focuses on the problem of positioning accuracy degradation caused by inconsistent sampling frequencies of INS/UWB navigation system. In order to achieve the same sampling frequency of INS and UWB, this paper proposes a data fusion algorithm combining extended Kalman filter (EKF), locally weighted linear regression (LWLR), least squares support vector machine (LS-SVM). First, during the UWB data sampling interval, the UWB data are fitted by LWLR, and the fitted UWB data and INS data are fused by EKF. Then, estimation error of EKF is optimized by LS-SVM. At last, the simulation results indiciate data fusion algorithm restrains divergence problem in the UWB sampling interval. And the positioning accuracy of indoor quadrotor INS/UWB navigation system has been increased through the algorithm.