Enhanced least-squares positioning algorithm for indoor positioning

Ian Sharp, Kegen Yu

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

This paper presents an enhanced least-squares positioning algorithm for locating and tracking within indoor environments where multipath and nonline-of-sight propagation conditions predominate. The ranging errors are modeled as a zero-mean random component plus a bias component that is assumed to be a linear function of the range. Through minimizing the mean-square error of the position estimation, an expression for the optimal estimate of the bias parameter is obtained. Both range and pseudo-range-based positioning are considered. Simulations and experimentation are conducted which show that a significant accuracy gain can be achieved for range-based positioning using the enhanced least-squares algorithm. It is also observed that the pseudo-range-based least-squares algorithm is little affected by the choice of the bias parameter. The results demonstrate that the experimental 5.8-GHz ISM band positioning system can achieve positional accuracy of around half a meter when using the proposed algorithm.

Original languageEnglish
Article number6212501
Pages (from-to)1640-1650
Number of pages11
JournalIEEE Transactions on Mobile Computing
Volume12
Issue number8
DOIs
Publication statusPublished - 2013
Externally publishedYes

Keywords

  • enhanced least-squares algorithm
  • experimental verification
  • Indoor positioning
  • multipath and nonline-of-sight propagation
  • optimal bias parameter
  • positional accuracy analysis
  • range and pseudo-range

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