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A fuzzy adaptive extended Kalman filter exploiting the Student's t distribution for mobile robot tracking

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posted on 2021-09-01, 08:30 authored by Xin Lai, Guorui Zhu, Jonathon Chambers
To solve the problem of non-Gaussian distribution of measurement noise during the actual process of trajectory tracking when the mobile robot is performing tasks, a novel fuzzy adaptive extended Kalman filter exploiting the Student's t distribution for a robot path tracking is proposed. The distributions of process and measurement noise are modeled using the Student's t distribution. With the adaptive fuzzy controller, the adaptive factors are designed to adjust the covariance matrices of the process and measurement noises simultaneously, which optimize the posterior state and tracking accuracy. The simulation results show that the proposed algorithm has better accuracy and is more robust than existing state-of-the-art algorithms.

History

Citation

Meas. Sci. Technol. 32 105017

Author affiliation

School of Engineering

Version

  • AM (Accepted Manuscript)

Published in

Measurement Science and Technology

Volume

32

Issue

10

Publisher

IOP Publishing

issn

0957-0233

eissn

1361-6501

Acceptance date

2021-06-18

Copyright date

2021

Available date

2022-07-05

Language

English

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