RBGF: Recursively bounded grid-based filter for indoor position tracking using wireless networks

Yuan Yang, Yubin Zhao, Marcel Kyas

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Numerical methods for recursive Bayesian estimation are widespread in position tracking of robotics. However, challenges arise to indoor network positioning due to the limited processing power and inaccurate ranging measurements of low-end network nodes. For efficient and robust indoor position tracking, we incorporate a recursive bound to a grid-based filter namely RBGF, which approximates the posterior of the target's position by a grid of weighted cells over a bounded state-space. The state-space (the set in which the state samples can take) is recursively confined based on both the previous estimation and current measurements, therefore, the grid cells converge to the true state and the effect of non-line-of-sight (NLOS) measurements is bounded. Experimental results by an indoor sensor test-bed demonstrate RBGF achieves the average and the worst-case of positioning errors about 1 meter and 3 meters, respectively on condition that the average ranging error is about 3 meters.

Original languageEnglish
Article number6782662
Pages (from-to)1234-1237
Number of pages4
JournalIEEE Communications Letters
Issue number7
Publication statusPublished - Jul 2014

Other keywords

  • grid-based filter
  • Indoor position tracking
  • non-line-of-sight (NLOS) ranging errors
  • numerical Bayesian methods
  • particle filter


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