University of Hertfordshire

From the same journal

By the same authors

  • Y. Zhuang
  • Q. Wang
  • M Shi
  • Pan Cao
  • L. Qi
  • J. Yang
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Original languageEnglish
Article number8675356
Number of pages10
Pages (from-to)6513-6522
JournalIEEE Internet of Things Journal
Journal publication date1 Aug 2019
Volume6
Issue4
Early online date27 Mar 2019
DOIs
Publication statusPublished - 1 Aug 2019

Abstract

How to provide a low-cost but accurate localization solution for the indoor mobile robots are essential in many Internet of Things applications, such as smart home and asset tracking. To achieve this goal, this paper originally proposes a modified two-filter smoother based on ensemble Kalman filter (KF) (denoted as EnKS) for the localization of indoor mobile robots. The proposed EnKS algorithm consists of both a forward part of an ensemble KF (EnKF) with statistical linear regression and a backward part of a modified information KF with state error vector. The EnKS based on stochastic sampling with ensemble members can achieve better positioning accuracy than other Kalman smoothers. When compared to EnKF, the proposed EnKS combines a backward filter to compensate for the estimation error of EnKF and further improves the accuracy. Furthermore, the implementation of the proposed EnKS is conducted in the real world visible light positioning (VLP) system using pre-existing LED lights for low-cost robot localization. To make a performance comparison, this paper also uses baseline smoothers based on extended KF and central difference KF in the VLP system. Preliminary experimental results imply that the proposed EnKS is able to achieve the best positioning accuracy, as high as 11.18 cm on average, but with a comparable computational complexity, which enables to meet the demands of many robot applications.

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