Measured accuracy improvement method of velocity and displacement based on adaptive Kalman filter. Issue 5 (16th September 2019)

• Record Type: Journal Article
• Authors: Xu, Xiaobin; Luo, Minzhou; Tan, Zhiying; Zhang, Min; Yang, Hao
• Is Part Of: Sensor review. Volume 39:Issue 5(2019)
• Page Start: 708
• Page End: 715
• Publication Date: 2019-09-16
• Subjects: Particle swarm optimization -- Kalman filter -- Displacement measurement -- Velocity measurement; Sensor systems -- Periodicals; Detectors -- Industrial applications -- Periodicals; Engineering instruments -- Periodicals; 681.2
• DOI: 10.1108/SR-10-2018-0255
• Languages: English
• ISSNs: 0260-2288

Abstract

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.