Measured accuracy improvement method of velocity and displacement based on adaptive Kalman filter
ISSN: 0260-2288
Article publication date: 12 August 2019
Issue publication date: 23 August 2019
Abstract
Purpose
This paper aims to investigate the effect of unknown noise parameters of Kalman filter on velocity and displacement and to enhance the measured accuracy using adaptive Kalman filter with particle swarm optimization algorithm.
Design/methodology/approach
A novel method based on adaptive Kalman filter is proposed. Combined with the displacement measurement model, the standard Kalman filtering algorithm is established. The particle swarm optimization algorithm fused with Kalman is used to obtain the optimal noise parameter estimation using different fitness function.
Findings
The simulations and experimental results show that the adaptive Kalman filter algorithm fused with particle swarm optimization can improve the accuracy of the velocity and displacement.
Originality/value
The adaptive Kalman filter algorithm fused with particle swarm optimization can serve as a new method for optimal state estimation of moving target.
Keywords
Citation
Xu, X., Luo, M., Tan, Z., Zhang, M. and Yang, H. (2019), "Measured accuracy improvement method of velocity and displacement based on adaptive Kalman filter", Sensor Review, Vol. 39 No. 5, pp. 708-715. https://doi.org/10.1108/SR-10-2018-0255
Publisher
:Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited