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Measured accuracy improvement method of velocity and displacement based on adaptive Kalman filter

Xiaobin Xu (College of Mechanical and Electrical Engineering, Hohai University – Changzhou Campus, Changzhou, China and Jiangsu Key Laboratory of Special Robot Technology, Hohai University – Changzhou Campus, Changzhou, China)
Minzhou Luo (College of Mechanical and Electrical Engineering, Hohai University – Changzhou Campus, Changzhou, China and Jiangsu Key Laboratory of Special Robot Technology, Hohai University – Changzhou Campus, Changzhou, China)
Zhiying Tan (College of Mechanical and Electrical Engineering, Hohai University – Changzhou Campus, Changzhou, China and Jiangsu Key Laboratory of Special Robot Technology, Hohai University – Changzhou Campus, Changzhou, China)
Min Zhang (College of Mechanical and Electrical Engineering, Hohai University – Changzhou Campus, Changzhou, China and Jiangsu Key Laboratory of Special Robot Technology, Hohai University – Changzhou Campus, Changzhou, China)
Hao Yang (College of Mechanical and Electrical Engineering, Hohai University – Changzhou Campus, Changzhou, China and Jiangsu Key Laboratory of Special Robot Technology, Hohai University – Changzhou Campus, Changzhou, China)

Sensor Review

ISSN: 0260-2288

Article publication date: 12 August 2019

Issue publication date: 23 August 2019

192

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

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