Improved Extreme Learning Machine Based UWB Positioning for Mobile Robots with Signal Interference

Jun Ma, Xuechao Duan, Chen Shang, Mengjiao Ma, Dan Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)


For the purpose of tackling ultra-wideband (UWB) indoor positioning with signal interference, a binary classifier for signal interference discrimination and positioning errors compensation model combining genetic algorithm (GA) and extreme learning machine (ELM) are put forward. Based on the distances between four anchors and the target which are calculated with time of flight (TOF) ranging technique, GA-ELM-based binary classifier for judging the existence of signal interference, and GA-ELM-based positioning errors compensation model are built up to compensate for the result of the preliminary evaluated positioning model. Finally, the datasets collected in the actual scenario are used for verification and analysis. The experimental results indicate that the root-mean-square error (RMSE) of positioning without signal interference is 14.5068 cm, which is reduced by 71.32% and 59.72% compared with those results free of compensation and optimization, respectively. Moreover, the RMSE of positioning with signal interference is 28.0861 cm, which is decreased by 64.38% and 70.16%, in comparison to their counterparts without compensation and optimization, respectively. Consequently, these calculated results of numerical examples lead to the conclusion that the proposed method displays its wide application, high precision and rapid convergence in improving the positioning accuracy for mobile robots.

Original languageEnglish
Article number218
Issue number3
Publication statusPublished - Mar 2022
Externally publishedYes


  • errors compensation
  • extreme learning machine (ELM)
  • genetic algorithm (GA)
  • robot indoor positioning
  • ultra-wideband (UWB)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science (miscellaneous)
  • Mechanical Engineering
  • Control and Optimization
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering


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